uk: Комп'ютерні науки

description: Collaborative validation of user-contributed data using a geospatial blockchain approach: the SIMILE case study Decentralized applications are a fundamental element for internet development, not only because they are safer but also because they make data accessible to more people than centralized applications. One of the most important architectures of decentralized applications is blockchain, a computing infrastructure capable of sharing data obeying consensus and in an immutable way. The most popular blockchain applications belong to the financial sector, and developments are still missing in other areas that can take advantage of this technology. An area that can benefit from blockchain characteristics is citizen science, which, as its name specifies, is the research activity performed by a community of citizens. Due to the requirements to this extent, this work studies the feasibility to use a blockchain architecture in citizen science, specifically for ecosystem monitoring. Additional to this, this work helped to understand the advantages and disadvantages of using this technology in this area. Current state-of-the-art applications that propose partially a solution to citizen science are FOAM and CryptoSpatial Coordinates. FOAM [1] is a geospatial web application that builds a consensus-driven globe map using the blockchain Ethereum protocol. To achieve network verification, it employs a cryptographic software utility token, where cartographers verify if points added to the network are false or correct. This removes the need for a central authority to regulate and verify the points. The voting mechanism uses FOAM tokens to avoid spamming from the participants. The system works by mapping a blockchain address to a physical location, which can be registered with a spatial resolution of 1m by 1m. CryptoSpatial Coordinates (CSC) [2] is an Ethereum smart-contract library that can be used for developing geospatially enabled decentralized apps. It uses Blockchain technology to store, retrieve, and process vector geographic data. In our approach, we were only “inspired” by the previous solutions, but we decided to develop something new and original. The system is developed in Solidity programming language. This allows usage on every blockchain that supports the Ethereum Virtual Machine and guarantees extended flexibility. Moreover, this choice is justified by the expanded ecosystem that Ethereum offers. The architecture of Smart Contracts is completely open-source and developed with a focus on the reusability of the components for other applications in the same field. The two main parts of the architecture are the Cell Smart Contracts and the Registry Smart Contracts. This is based on the mapping of a Discrete Global Grid System (DGGS) [3] with Smart Contracts. As a DGGS we choose S2 [4], which is an open-source library developed by Google that offers good processing functionalities and a grid with a fine-grained resolution. Each Smart Contract representing a Cell is used to keep track of the hash of the observations collected in the application. The hashes are used to locate and retrieve the stored files in the decentralized storage InterPlanetary File System (IPFS). This structure also allows to store metadata about the observations, for example, their quality decided through a peer voting mechanism or with some other system. The Registry Contracts are linked to a resolution of the DGGS and have the duty to keep track of the mapping between the DGGS cells of that resolution and their respective Smart Contract. The prototype platform is developed in Velas, a blockchain architecture with a strong focus on fast transaction speed and low costs of fees compared to other blockchains (e.g. Ethereum, Cardano, Solana). The use-case for this work was the Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems (SIMILE) project. SIMILE is a cross-border Italian-Swiss project with the aim to improve the collaboration between public administrations and stakeholders for the management of the Insubric lakes (Lugano, Como and Maggiore) and their ecosystems, as well as monitoring water resources quality [5]. One of the main sources of data in SIMILE is collected with a Citizen Science approach, meaning that the data is collected from normal citizens through their smartphones. The observations of this type include data about water quality, climatic parameters, and multimedia files such as images can be included. The collected data can be currently validated by the public authorities managing the platform but this requires time which is not always available to technicians. In our system, the observations are instead validated through a mixed rating system that allows both users and admins to evaluate each entry. Furthermore, the use of the proposed blockchain architecture allows access to the collected data without relying on the currently existing Web Application. The practical importance of this work is to fill the gaps currently present in citizen science applications, by proposing an innovative system that works with the blockchain infrastructure. The result of this work and the technological development performed, demonstrate that citizen science applications can be, as a matter of fact, developed as a decentralized infrastructure. The main advantages with respect to other systems are data immutability, security and no single point of failure. Future work can include the implementation of a system to further incentivize the collection of data. This will work with a reward system in the form of a Utility Token. This token could be accepted by the public administrations benefitting from the data, in exchange for some form of compensation such as discounts on public services.

uk: вхід без авторизації


Bartlomiej Burkot

type: Person

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: This presentation will be a real story about the process of building webmapping portals with usefull public transport information and the quality of air. Two portals will be shown one from Warsaw and one from Cracow (gdziejestautobus.pl/mapa/, mapakrakow.pl/). This will be an use case of using opensource software and open data for building the web mapping portal. The challenges will be presented by constructing layers with live positions of public transport vehicles and the state of air quality in Poland. The technical details will be presented along with the logistic and an business aspects. Following points will be covered: used development software, user experience challenges, design of project, project organization, effort, cost, legal issues. There will be shown the sources of data from open public API services in Poland. The one is the open API with vehicles location data of public transport office in Warsaw. The second is the data coming from public office responsible for environment protection and monitoring. Both presented portals shows the live position of public transport vehicles in the capital of Poland. The portal for Cracow will also show the live state of air pollution in the city. The pollution data come from sensors located in Poland collection the quality of air. Both portals are the examles of: how to connect opensource Web-GIS tools and open public data to build an interesting web mapping site showing usefull data in the convenient spatial way.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

name: Buses moving on the map - use case of building web mapping portal

type: Organization

name: Open Source Geospatial Foundation (OSGeo)

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: Environmental changes can have different causes on local level (e.g. soil sealing) as well as on global level (e.g. climate change). To detect these changes and to find patterns in the reasons for them it is necessary to collect broad environmental data, temporally and spatially. Thereto citizens can play an essential role to collect the data (Goodchild, 2007). In particular, we developed a system which enables citizens to monitor the occurrence and distribution of birds and provides the collected data to the public in order that both researchers and citizens can derive conclusions from them. With our automated approach we want to support other citizen science solutions like eBird (Sullivan et al. 2014) where contributors manually report their sightings. Therefore, we built a prototypical bird feeder equipped with several sensors and the infrastructure to process the data collected by the feeder. The feeder is easy to reproduce at a reasonable price by following an open available manual. This allows anyone to build the feeder on their own, enabling a large distribution at many locations. The feeder automatically detects when a bird is visiting it, takes an image of the bird, determines the species and connects the observation with environmental data like the temperature or light intensity. All the collected data are published on a developed open access platform. Incorporating other surrounding factors like the proximity of the feeder station to the next forest or a large street allows it to pursue various questions regarding the occurrence of birds. One of them might ask, how does the immediate environment affect bird abundance? Or do sealed surfaces have a negative effect compared to a flowering garden? The developed weatherproof bird feeder is attached with multiple sensors. Thereby the standard equipment includes a motion sensor to detect if a bird is currently visiting the feeder, a camera to take images of the birds, a balance to weigh the birds and a sensor to measure the environment's temperature and air pressure. In addition to the standard sensors, further sensors were tested with the prototype, which usefully supplement the monitoring but are not absolutely necessary for the operation of the station. Thus, a microphone is suited to record the voice of the birds or generally the surrounding noises. A brightness sensor can be valuable to draw conclusions whether birds visit the feeder in relation to light conditions, or a sensor to measure the air pollution (e.g. PM10) to investigate if the air quality influences the bird occurrence. Besides, the usual camera can be replaced by an infrared camera to capture animals which visit the feeder at night. Thus, the station is expandable and customizable depending on the individual use cases or research questions. The environmental sensor data is continuously logged and sent to the open access platform, whereby the corresponding interval can be set by the user. Once the motion sensor detects a movement, the camera recording starts as well as scale and microphone start to store values. As long as the motion sensor detects movement, camera, microphone and balance are running. After the movement is finished, a light-weighted recognition model is used to check whether a bird is depicted in the images. If this is the case, all data collected during the movement, including the respective environmental data, will be sent as a package to the open access platform. In order to process the data collected by the station, we have developed various methods and software for data storage, analysis and sharing. The data processing is done on a centralized server. Communication with this server is enabled through a RESTful API and a website. On the server created entities of the feeders can receive environmental data as well as movement packages. When movements are sent, the server analyzes the amount of birds and identifies the species with artificial intelligence. In addition to the storage, the server makes the data available to users in two ways. First, the data is downloadable as raw JSON via the API, which enables others to use it for their own research. Second, the data is presented nicely on our website, to make it easily inspectable for everyone. However, not only via our stations a upload to the server is possible, it is also open for the upload of data gathered by other systems. Further, it is also possible to upload images of birds and receive the represented species. The feeder is designed so that it can be replicated by anyone. The corresponding instructions will be published shortly. The code to run the station and the server is available via GitHub (github.com/CountYourBirds). Moreover, different options for the validation of the data, especially the species classification, are implemented. One step is the automatic validation by the sensor values or metadata. For instance if a standard camera recognizes a bird but currently it is night (detected by light sensor or time of the day) or the balance detects nothing, the observation is discarded. Further validation can come from actual people. An interface is provided which is used to show people values and especially images recorded with the automatically recognized species. The depicted data can be validated to find corrupt sensors and wipe out mistakes made within the image classification. Additionally, the serverside evaluation of the data is supplemented by a validation of the recognized species. It is checked whether it is possible that the species can occur at that geographic region or at that time of the year. As next steps we want to conduct workshops with citizens and experts, both for putting together the stations as well as evaluating the data and the station itself. In general a strength of our implemented approach is that it is easily adaptable to other use cases, especially to detect other animals. For example with small adaptations to the feeder it could be used to detect or count different mammals like squirrels or for insects like butterflies and bees.

uk: вхід без авторизації

name: Multi-Sensor Feeder: Automated and Easy-To-Use Animal Monitoring Tool for Citizens


uk: Комп'ютерні науки

description: Multi-branch Deep learning Based Transport Mode Detection using Weakly Supervised Labels Mobility data, based on global positioning system (GPS) tracking, have been widely used in many areas. These include analyzing travel patterns, investigating transport safety and efficiency, and evaluating travel impacts. Transport Mode Detection (TMD) is an essential factor in understanding mobility within the transport system. A TMD model assigns a GPS point or a GPS trajectory to a particular transport mode based on the user's activity and medium of travel [1]. However, the complexity of the prediction procedure increases with the number of modes that need to be predicted. For example, it is comparatively easy to predict whether a user is 'static' or 'slow moving' or 'fast moving' but it's hard to predict detailed transport modes such as walk, bike, car, bus, train, boat, etc. Therefore, this study proposes a multi-branch deep learning-based TMD model which can predict multi-class transport modes. Two major challenges need to be addressed in order to generate a state-of-the-art deep learning model. The first is to prepare ground-truth data. There are insufficient open-sourced ground-truth data available for transport modes in Japan. Hence, we proposed a transport mode label generation approach using snorkel [2]. Snorkel is a weakly supervised labeling function, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, experts write labeling functions that express arbitrary heuristics based on the logic that can be drawn from understanding the data and the physical actions they represent. In this study, we used snorkel for generating the ground truth data for transport mode. Initially, we considered publicly available road networks, railway networks, bus routes, etc., for creating road, bus, train labels by overlaying GPS points on these transportation networks. However, there are multiple occasions where the road, bus, and train classes overlap each other, especially in a city region. Hence, we introduced a boolean (True/False) based soft-labeling function, where the same GPS point might have multiple True values for road or railway. Second, we derived mobility-related features from the raw GPS data. Raw GPS raw data is typically composed of latitude, longitude, and timestamps. The raw GPS data were used to generate point-level features such as speed, speed difference, acceleration, acceleration difference, initial bearing, and bearing difference. Apart from that, we also generated trajectory level features such as average speed and average acceleration. Transportation network-based soft-labeling and other mobility features are used to define labeling functions in the snorkel. These label functions are used to create true ground truths using a generative machine learning model with a portion of the GPS data. The generated labels (walk, cycle, bus, car, train, boat/ship) were then used to train the proposed deep learning model. To construct the model we opted to use two branches where raw GPS latitude and longitude values were used in one and the derived mobility features are used in the other. We used 3 fully-connected hidden layers for raw GPS data (lat/lon) and 4 fully connected hidden layers for mobility features. Features derived from the two branches are concatenated. Further, 3 fully connected hidden layers and softmax cross-entropy were used as a loss function. The proposed deep learning model has 108,614 trainable parameters and Adam is used as an optimizer. This particular two-branch model structure achieves better accuracy as it combines raw data as well as the derived mobility features in the network. An example of the benefit from this approach benefit can be the network's ability to relate GPS coordinates with road driving classes, thus inherently inferring that location as on a road. Note, many of these inferences that improve classification accuracy are possible via dramatically more advanced pre-processing to build out additional features. However, that approach is more time-consuming and could never catch all the potential inferences that an unbiased set of deep learning layers can inherently extract. We evaluated the trained model's effectiveness in two ways. We compared the results against the popular XGBoost classifier, with our model producing over 5% higher accuracy for the benchmark Geolife dataset [3]. Moreover, we collected smartphone-based GPS trajectories for multiple modes of transportation collected by testers in Bengaluru, India, and Tokyo, Japan. With this new absolute ground truth data, we compared the resulting predicted classes between operating system-provided activity classifications, the above XGBoost model, and our own. Our experiments show promising results with improved accuracy and increases in number of labeled data points. Of key note is that the iOS [4] and android [5]in-built activity recognition tools provide the 'automotive' class as a single class, while our proposed model efficiently distinguishes automotive classes as car, bus, and train with improved accuracy. This work completely depends upon Free and Open Source Solutions (FOSS) for data preparation, mobility feature generation, deep learning model training, and big data computing. That includes various geospatial libraries such as geopandas, shapely, rtree, weakly label generation platform snorkel, deep learning platform tensorflow, keras, big-data computing platforms such as pyspark, hadoop, hive, etc.

uk: вхід без авторизації


keywords: foss4g2022 generaltrack Stateofsoftware

uk: Комп'ютерні науки

description: Access to long-term biodiversity datasets is vital for monitoring, managing, and protecting freshwater ecosystems. Detecting critical ecosystem changes, such as losing unique biodiversity and ecosystem services, is dependent on access to data. A wealth of biodiversity data exists for river ecosystems in South Africa, but an operational information system to access these data is currently not available. To address this knowledge gap, the Freshwater Biodiversity Information System (FBIS) has been developed. FBIS is a platform for hosting, visualizing, and sharing freshwater biodiversity information for South African rivers. The project seeks to mobilize and import to the system baseline biodiversity data, identify strategic long-term monitoring sites, and train key organizations on how to use the information system. Using map-based visualizations, user-friendly dashboards and rapid data extraction capabilities, the system will improve knowledge of freshwater biodiversity and long-term river health trends, thereby supporting better-informed river management decisions and conservation planning projects.

uk: вхід без авторизації

name: The Freshwater Biodiversity Information System (FBIS) – mobilising data for monitoring freshwater ecosystems


uk: Комп'ютерні науки

description: It is essential to understand what future epidemic trends will be, as well as the effectiveness and potential impact of public health intervention measures. The goal of this research is to provide insight that would support public health officials towards informed, data-driven decision making. We present spatialEpisim, an R Shiny app (github.com/ashokkrish/spatialEpisim) that integrates mathematical modelling and open-source tools for tracking the spatial spread of COVID-19 in low- and middle-income (LMIC) countries. We present spatial compartmental models of epidemiology (ex: SEIR, SEIRD, SVEIRD) to capture the transmission dynamics of the spread of COVID-19. Our interactive app can be used to output and visualize how COVID-19 spreads across a large geographical area. The rate of spread of the disease is influenced by changing the model parameters and human mobility patterns. First, we run the spatial simulations under the worst-case scenario, in which there are no major public health interventions. Next, we account for mitigation efforts including strict mask wearing and social distancing mandates, targeted lockdowns, and widespread vaccine rollout to vaccinate priority groups. As a test case Nigeria is selected and the projected number of newly infected and death cases are estimated and presented. Projections for disease prevalence with and without mitigation efforts are presented via time-series graphs for the epidemic compartments. Predicting the transmission dynamics of COVID-19 is challenging and comes with a lot of uncertainty. In this research we seek primarily to clarify mathematical ideas, rather than to offer definitive medical answers. Our analyses may shed light more broadly on how COVID-19 spreads in a large geographical area with places where no empirical data is recorded or observed.

uk: вхід без авторизації

name: spatialEpisim: an open-source R Shiny app for tracking COVID-19 in low- and middle-income (LMIC) countries


uk: Комп'ютерні науки

description: The project “Geopaparazzi Survey Server (GSS) and SMASH for Mobile Data Collection in a UN Peacekeeping mission (MONUSCO)” aimed to operationalize the use of GSS and SMASH to support field data collection in MONUSCO. This talk will cover the endeavours of this project, introducing the background, user requirements and use cases, the implementation of the online GSS at a UN central data centre as well as the project outcomes and recommendations. MONUSCO GIS: MONUSCO GIS Unit uses mobile devices, GPS, mini UAV, satellite imagery from commercial providers, to ensure availability of detailed topographic and up-to-date mission operational data for the various GIS end products (cartographic maps, imagery, infographics, interactive web map applications, geodatabases). Having a central server for mobile data collection will ensure constant availability of standardized, quality controlled and up-to-date operational data, which is key for the provision of quality GIS products & services and consequently data-driven decisions and actions for the Mission. From the beginning, there hasn't been a centralised infrastructure readily available to all the geographically dispersed UN Mission users in the DR Congo for field mobile data collection. This project marked the genesis of it. UN Open GIS Initiative: Established in March 2016, is to identify and develop an Open Source GIS bundle that meets the requirements of UN operations, taking full advantage of the expertise of contributing partners (Member States, international organizations, academia, NGO’s and private sector). Geospatial Information Systems (GIS) have played a substantial role in providing timely and effective geospatial information products (maps and dynamic tools) to ensure the United Nations operations are equipped with suitable information to support the UN mandates through informed planning and decision-making processes. The UN has been using proprietary GIS software for the past two decades. The rapid growth and development of open-source GIS solutions present the technological potential, operational flexibility, and financial benefits as well as ease to access for UN operational partners and host nations.

uk: вхід без авторизації

name: UN Open GIS Initiative: Geopaparazzi Survey Server and SMASH for Mobile Data Collection in a UN Peacekeeping Mission (MONUSCO)


What is new in Giswater 3.5

uk: Комп'ютерні науки

description: Giswater (giswater.org) is a open source software aimed at being a corporate tool in water utilities with which to manage network assets in an excellent way and at the same time have the assets ready for hydraulic simulation, a feature that today Today it is known how to have a digital clone of network assets. Technologically, it uses a set of Open Source technologies such as EPANET, SWMM, PostgreSQL, PostGIS or QGIS, all of them mature and proven, which give it a very powerful base for growth and consolidation. Its 'database centric' architecture gives it enormous potential with which maintenance operations (network outages) can be managed in an integrated way, longitudinal profiles can be made, events inventoried, among others. It has a data model with dual-face architecture, which allows full integration of inventory and hydraulic model data, both for drinking water networks and for urban drainage and sanitation networks, giving full flexibility to the modeler to work with hydraulic capacities without any impact on inventory data for each asset item. The EPA file export module has certain "on the fly" transformations to make the two different geometries (remember the dual-face) of the inventory elements compatible for both EPANET (GitHub: /Giswater/giswater_dbmodel/ wiki/epanet-on-the-fly-transformations) how to for SWMM. It allows you to work with different scenarios to create different modeling conditions in order to check the worst case scenario or check how the network will respond in future scenarios. For Water Supply networks it is possible to work with demand scenarios (GitHub: Giswater/giswater_dbmodel/wiki/epa-demand-scenarios) and for Urban Drainage projects it is possible to work with DWF scenarios (GitHub: Giswater/giswater_dbmodel/wiki/epa-dwf-scenarios) and hydrological scenarios (GitHub: Giswater/giswater_dbmodel/wiki/epa-hydrology-scenarios) Additionally, it also allows working with alternatives to plan new elements of the network without interfering with the elements of the asset inventory. By creating an alternative (GitHub: Giswater/giswater_dbmodel/wiki/masterplan-capabilities) you can modify the physical reality of the network without affecting the real assets at all. Another especially interesting feature is that it allows collaborative work. Large hydraulic engineering projects have been worked on to date in a sequential or fractional way, but not collaboratively. Thanks to Giswater it is now possible to work on projects in a real collaborative way, given the inherent multi-user characteristics of the Databases on which the project pivots. The project was born seven years ago and in its version 3.5 it incorporates interesting novelties, among which the following stand out: Complete refactor of the python code, new hydraulic model capabilities with the management of multi-scenarios or the improvement of the usability of numerous tools such as dynamic zoning or the info among others.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Over the last years, new architecture arose with the aim of easing the deployment and the usage of geospatial data and software in the cloud. Large accounts are starting to leverage the power of Kubernetes in public clouds such as AWS or Azure associated with traditional OSGEO software such as GeoNetwork and GeoServer. In this talk we'll present our experience and results of working with large institutions in the public sector (civil defence, judiciary) or in the private sector (insurances, telecoms). We'll demonstrate how working the agile way with open-source software and high-level contributors allow to tackle successfully even the most ambitious challenge. As a result of these efforts (multiple 100-man days contributed to the communities), we could participate significantly in developing GeoServer cloud as well as GeoNetwork microservice. Both projects aim to solve the cloud native challenge and are well underway of succeeding. After such an initial effort, we encourage every party using open-source software to participate in the maintenance and contribute to open-source development. Only with a real open-source engagement can we as a community achieve producing sustainable best of breed open-source software. Finally, we recommend customers to make sure their service providers have a positive impact in the communities.

uk: вхід без авторизації

name: Contributing to Geospatial Cloud Native Solutions


uk: Комп'ютерні науки

description: The Routing Engine Valhalla has been extended with a solution of the Chinese Postman Problem (CPP). This means that the most efficient route to travel all roads and paths in the area can now be calculated within a defined polygon. The CPP is a well-known problem from graph theory, where the goal is to visit every edge in a graph while minimizing the distance traveled. In theory, a graph can be either directed, undirected, or mixed. In this implementation, the CPP has been implemented for directed graphs, as this corresponds to the representation of graphs in Valhalla and the data structure of OpenStreetMap (OSM). The latter forms the data basis for the calculation of the CPP route. The CPP is solved using the following set of algorithms: the Floyd-Warshall algorithm, the Hungarian method, and the Hierholzer method. After successfully implementing the theoretical code base of the CPP, the main challenge was to make the route calculation executable using real-world road networks (OSM). A key problem with the implementation of the theoretical CPP is that in real-world graphs, not every edge is always reachable by all other edges. Therefore, various extensions had to be made to allow the computation of a CPP route using OSM data. For example, within a larger area, rarely all road segments are accessible exclusively via the roads located in the area. It is often necessary to leave the area to access these otherwise inaccessible parts of the road network. Eventually, we were able to create a working prototype of the CPP in Valhalla. In addition to the function of freely selecting the area to be traveled, restricted zones, so called no-go areas, can also be defined. After selecting the vehicle type (car, bicycle, pedestrian, etc.), the CPP route can be calculated, which also includes turn-by-turn navigation.

uk: вхід без авторизації

name: Implementation of the Chinese Postman Problem in the Valhalla Routing Engine


uk: Комп'ютерні науки

description: MapFish Print is a mature Java-based open source software (BSD-2 license) for printing maps. Opposed to frontend solutions such as inkmap, MapFishPrint runs server side and is integrated in several open source GIS frameworks like GeoMapFish ( for creating geoportal applications) or geOrchestra (spatial data infrastructure). The classic approach to deploy MapFish Print is using a WAR-file in a Servlet Container (for example Tomcat), while it can also be integrated into cloud environments with prebuilt Docker images. Alternatively, MapFish Print’s core printing library can also be integrated into other projects programmatically. MapFish Print supports the common data formats and standards (WMS, WFS, WMTS, GeoJSON, etc.) and provides access to rich cartographic features like rotations, grids, north-arrow or legends and multi-page printing. The layout is defined by a JasperReports template and a YAML configuration file. The template allows users to define the layout, include elements for maps, legends, grids and alphanumeric tables. Clients request a concrete print-out with a JSON-Request, providing along information like bounding-box, map layers and other data. The final report will be rendered by MapFish Print either as PDF or as a raster image and returned to the client. We will present a summary of existing features as well as new (e.g. tiled WMS with buffered tiles for rendering large areas without label conflicts) and planned features of the MapFish Print open source project.

uk: вхід без авторизації

name: MapFish Print, the classic printing component, a project update


uk: Комп'ютерні науки

description: An Open-Source Mobile Geospatial Platform for Agricultural Landscape Mapping: A Case Study Of Wall-to-Wall Farm Systems Mapping in Tonga AN OPEN-SOURCE MOBILE GEOSPATIAL PLATFORM FOR AGRICULTURAL LANDSCAPE MAPPING: A CASE STUDY OF WALL-TO-WALL FARM SYSTEMS MAPPING IN TONGA Pacific Island Countries (PICs) such as Tonga rely on landscape services to support communities and livelihoods in particular smallholder and commercial agriculture. However, PICs are increasingly vulnerable to climatic and environmental shocks and stressors such as increasing cyclone occurrence and landscape conversion. Spatially explicit, timely, and accurate datasets on agricultural and other land use at the community scale are an important source of information for land use policy development, landscape management, disaster response and recovery, and climate-smart sustainable development. However, such datasets are not available or readily accessible to stakeholders engaged in landscape management in PICs. Household surveys, participatory GIS (PGIS), and remote sensing are approaches that have previously been used to capture community-scale landscape uses in PICs; however, these approaches are challenged by data collection and management burdens, mismatched scales, timely integration of databases and data streams, aligning system requirements with local needs, and various socio-technical issues associated with developing and deploying applications in new domains. Such data collection approaches only provide single time-steps representations of landscape uses and fail to capture the highly dynamic and spatially diverse nature of PIC landscapes. We have addressed these challenges by developing, integrating, and deploying a tool for agricultural landscape monitoring at a local scale. This tool is composed of a stack of open-source geospatial applications and was developed through a collaboration between Tonga’s Ministry of Agriculture, Food, and Forests (MAFF) and researchers from Australian and South Pacific universities. We used a formal, iterative ICT for Development (ICT4D) framework to engage and co-develop the tool with MAFF and other landscape stakeholders including community leaders. The ICT4D framework is based on agile methods and is made up of five components: context analysis; needs assessment; use-case and requirements analysis; sustainability assessment; and development, testing and deploying. The five components provide a framework to ensure that project stakeholders (landscape managers, developers, and end-users) consider the range of technical and non-technical factors that will determine successful implementation of an ICT system in a new domain. Here, the goal was to transition from infrequent paper-based and non-spatial surveying of farms to develop a spatial data infrastructure that supports coordinated large-team farm mapping, data syncing and storage, and geospatial data analysis and reporting that aligns with MAFFs needs, and guides landscape management actions. Here, we describe our team’s experience in applying the iterative ICT4D framework. We present the development activities associated with successive phases of the project and reflect on the advantages (and constraints) this framework offers for developing open-source geospatial applications for deployment in new domains with a low-resource context. Initially, we introduce the qualitative fact finding, context analysis, and needs assessment to ascertain and distil MAFF’s needs for geospatial data and applications. Then, we present several stages of application design, development, testing, and refinement in various MAFF data collection and reporting campaigns, which enabled analysis and the detailed specification of the requirements for the agricultural landscape monitoring tool. This includes work on developing initial prototype applications, implementing small-scale vanilla and land utilisation surveys, and finally an island-wide wall-to-wall crop survey with a large team of field data collectors. Finally, we present the system architecture and a case study of the final iteration of the tool deployed for Tonga’s country-wide wall-to-wall farm system survey completed by MAFF in 2021. The final iteration of the tool was composed of a stack of open-source geospatial tools including QField for mobile mapping and data collection, QFieldCloud for user authentication and data syncing, and newly developed, open-source geospatial data visualisation, analysis, and reporting applications. This case study discusses: (1) how a team of over 40 data collectors were able to work collaboratively to build up a database comprising records from over 11,000 farms using QField and QFieldCloud; and (2) how custom applications developed in this project enable visualisation of this data on web maps and automated reporting to inform policy development and landscape decision making by MAFF. We also illustrate the critical role the tool and the crop survey information collected in 2021 played in assisting MAFF’s recovery efforts in the aftermath of the Hunga Tonga–Hunga Ha'apai submarine volcano explosion and subsequent tsunami which impacted heavily on Tonga’s main island of Tongatapu in January 2022. We also discuss the potential challenges in delivering the tool to other low-resource jurisdictions in the South Pacific including issues related to data dissemination, privacy and security; user management; technical and financial sustainability; scalability; training and knowledge transfer; and creating and fostering a community of open-source developers and users in PICs. The success of our case study demonstrates the importance of stakeholder engagement in an iterative ICT 4D development framework, and the great potential that open-source geospatial tools such as QGIS, QField, and QFieldSync can play in agricultural landscape management and disaster response in PICs.

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uk: Комп'ютерні науки

description: OpenMapTiles is an open-source set of tools for processing OpenStreetMap data into zoomable and web-compatible vector tiles to use as high-detailed basemaps. These vector tiles are ready to use in MapLibre, Mapbox GL, Leaflet, OpenLayers, QGIS as well as in mobile applications. Dockerized OpenMapTiles tools and OpenMapTiles schema are being continuously upgraded by the community (simplification, performance, robustness). The presentation will be demonstrating the latest changes in OpenMapTiles. The last release of OpenMapTiles greatly enhanced cartography and map styling possibilities, especially for roads such as adding new tags (expressway, access, toll). But also adding concurrent route labels or motorway junctions. Improvements were also done in the countryside by adding important tracks and paths (displaying from zoom 12), cliffs, aretes, and ridges. Another enhancement is the possibility to show mountain heights in customary units (feet in the US). OpenMapTiles is also used for generating vector tiles from government open-data secured by Swisstopo.

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name: OpenMapTiles 3.14 - vector tiles from OpenStreetMap & Natural Earth Data


keywords:
foss4g2022
generaltrack
UsecasesAndapplications
Benjamin, DK

uk: Комп'ютерні науки

description: Humanitarian Openstreetmap Team (HOT) administers several free-software applications with varied deployment architectures on multiple cloud platforms. As an organization that values openness and transparency, we actively seek out open source tools that help us enact our principles of open participation and collaboration. In that vein, we chose Terraform as the tool for managing infrastructure at HOT. Using HOT's experience managing OSM Galaxy infrastructure using Terraform, this talk describes our use of Terraform to manage infrastructure at scale in order to improve DevOps processes with infrastructure reproducibility, security, cost and change management. We will present these advantages in the context of our own team's experiences and the challenges we faced trying to build a scaling technology stack and compare Terraform with popular Infrastructure as Code (IaC) alternatives. The talk will use OSM Galaxy API (galaxy.hotosm.org) as a case study to describe the process of porting infrastructure to Terraform in order to manage infrastructure continuously at enterprise scale - which is particularly relevant for non-profits and organizations that develop compute-intensive technology.

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name: Using Terraform to manage HOTOSM's infrastructure as code


uk: Комп'ютерні науки

description: Cluster Analysis: a comprehensive and versatile QGIS plugin for pattern recognition in geospatial data As geospatial data continuously grows in complexity and size, the application of Machine Learning and Data Mining techniques to geospatial analysis is increasingly more essential to solve real-world problems. Although, in the last two decades, the research in this field produced innovative methodologies, they are usually applied to specific situations and not automatized for general use. Therefore, both generalization and integration of these methods with Geographic Information Systems (GIS) are necessary to support researchers and organizations in data exploration, pattern recognition, and prediction in the various applications of geospatial data. The lack of machine learning tools in GIS is especially clear for what concerns unsupervised learning and clustering. The most used clustering plugins in QGIS [1] contain few functionalities beyond the basic application of a clustering algorithm. In this work we present Cluster Analysis, a Python plugin that we developed for the open-source software QGIS and offers functionalities for the entire clustering process: from (i) pre-processing, to (ii) feature selection and clustering, and finally (iii) cluster evaluation. Our tool provides different improvements from the current solutions available in QGIS, but also in other widespread GIS software. The expanded features provided by the plugin allow the users to deal with some of the most challenging problems of geospatial data, such as high dimensional space, poor quality of data, and large size of data. In particular, the plugin is composed of three main sections: - feature cleaning: This part aims to provide some options to reduce the dimensionality of the dataset by removing the attributes that are most likely bad for the clustering process. This is important to achieve better results and faster execution time, avoiding the problems of clustering in high dimensionality. The first filter removes the features that are correlated above a user-defined threshold, since highly correlated features usually provide redundant information and can lead to overweight of some characteristics. The other two filters identify the attributes with constant values for all the data points or with few outliers differentiating from them. These types of features don’t provide any valuable information and can worsen the performance of clustering. To identify quasi-constant features, we use two different parameters introduced in the function NearZeroVar() from the Caret package developed for R [2]: the ratio between the two most frequent values and the number of unique values relative to the number of samples. - clustering: This section is used to perform clustering on the chosen vector layer. First of all, the user needs to select the features to use in the process. It is possible to select the features both manually and automatically. The automatic feature selection is done using an entropy-based algorithm [3] presented in two versions with different computational complexities. The currently available algorithms for clustering are K-Means and Agglomerative Hierarchical, and the users can select the one that best suits their needs. Before performing clustering, the plugin offers the possibility to scale the datasets with standardization or normalization, and to plot two different graphs to facilitate the choice of the number of clusters. - evaluation: In this section we show all the experiments carried out in the current session, with a recap of the settings and performances of the experiments and the possibility to save and load them with text files. To evaluate the quality of the experiments we calculate two indexes and the comparisons among experiments on the same dataset. The indexes are the internal metrics Silhouette coefficient and Davies-Bouldin index. To directly compare the clusters formed by two or more experiments we compute the score [4], which evaluates how many couples of data points are grouped together in all of the experiments or in none of them. Every experiment completed in the current session can be stored in a text file, and the experiments saved in previous sessions can be loaded in the plugin and are shown in the evaluation section along with the other ones. One of the major challenges during development has been allowing most of the functionalities on large datasets as well, both from the point of view of the number of samples and the number of dimensions. To achieve this, we also implemented algorithm options with good time complexities, as in the case of entropy with sampling and K-Means. Moreover, for all the data storage and manipulation done in the system, we use the data structures and functions provided by the libraries pandas and NumPy to guarantee high performance. Another important objective of the research is the accessibility and ease of use of the plugin since the general user of GIS is often lacking a machine learning and computer science background. To guarantee this, the User Interface is simple and self-explanatory, and each section contains a brief guide to explain all the functionalities. Furthermore, some algorithm parameters that cannot be modified via the interface are stored in an external configuration file, and can be modified via this. This is done to avoid confusing the less experienced users. Along with the implementation, the research is integrated with a considerable experimental phase, both during and after the development phase. This phase is essential to highlight both the potential of the plugin and its limitations in real-world scenarios. The great volume of experiments is conducted on data about the city of Milan, describing social-demographics, urban and climatic characteristics and with different granularities (ranging from less than 100 data points to almost 70000, and with a large number of numerical attributes, up to 109). Overall, the experimental phase shows good and adequate flexibility of the plugin, and outlines the possibilities for future developments that can be provided also by the QGIS community, given the open-source nature of the project.

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uk: Комп'ютерні науки

description: The use of GIS to support energy planning is now widespread and well consolidated, as evidenced by the numerous studies available in the international literature. Many companies and governmental institutions have transferred their data and results into open source web platforms or tools for public access. Within the broad topic of the interaction between renewable energy and environment, over the last years RSE S.p.A. has faced the necessity to develop and maintain WebGIS and online platforms related to various aspects of the energy system, in order to characterize the territory and its possible influences on renewable energy sources integration in the energy system, thus supporting the decision-making process towards energy transition. One of the most significant products is the Integrated Atlas for the National Energy System and Renewable Sources, a WebGIS platform which represents on a national scale significant variables of the energy sector (resources, demand, installed plants, territorial constraints) under a system view, with the principal aim of supporting energy planning. From a technical point of view the Integrated Atlas is developed on TerriaMap, a catalogue-based web geospatial visualisation platform developed by the Australian research centre CSIRO. TerriaMap uses the JavaScript library TerriaJS together with other open source libraries as React JS, Leaflet and Cesium, for 3D visualization. Besides standard WebGIS functionalities, the Integrated Atlas provides the access to TOTEM (Territory Overview Tool for Energy Models), an advanced open source tool for the energetical characterization of the territory, essential for supporting multi-energy modelling. Starting from spatial and energy data, the TOTEM tool estimates electrical and heat demand, wind and solar resource and other significant energy variables on hourly and provincial scale. Concerning technical details, the tool and its web interface are developed in Python and use libraries such as Pandas, Flask ad Bokeh. The tool is opensource and it will be release under MIT License, however only a portion of the input data are currently publicly available due to data providers’ restrictions. The need to harmonize data and analysis about the relations between energy and environment and provide an access point to the developed tools has inspired the creation of the Energy and Environment Geoportal, based on the Mapstore Open Source web platform developed by GeoSolutions. This platform allows viewing and querying geospatial published data, integrating different remote resources into interactive and immediate representations such as maps, dashboard or geostories. As an example, detailed results of above cited multi-energy models, which receive spatialized energy data as inputs, have been synthesized in a geostory, an immersive narration which explains how a multi-energy analysis based on detailed territorial characterization of a region can support the evaluation of the best alternatives for its energy development. The geostory is accessible from the Energy and Environment Geoportal. It must be specified that these products are still under development and subject to continuous updates of both data and technology. More details about contents and tools are left to the final presentation.

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name: Open Source geospatial applications for energy and environment integration


keywords: foss4g2022 generaltrack OpenData

uk: Комп'ютерні науки

description: Open data movement has been very active and trendy lately. Many solutions brought fresh air in the metadata ecosystem. Nevertheless, no one really pushed forward the confluence of the open data world and the geo metadata world (often powered by ISO or INSPIRE standards). Actually, many organizations still use both systems, which leads to confusion for the end users: datas are duplicated, metadatas are harvested in both directions, many websites aim to serve the same goal. Overall, this split does not help to easily find your data. It can also give headache to platform administrators, developers and architects who try hard to keep all catalogs synchronized. Based on this analysis, we are convinced that an ultimate solution could take the advantages of both ecosystems. Complex ISO standards, INSPIRE rules and opendata light schemas can co-exist in the same catalog. All new great ideas like quick data visualization or dataviz widgets can be supplied for any kind of data. The datahub literally came out from the need to centralize any kind of public dataset within the same platform. Thought as a backend API agnostic solution, the datahub first implementation has started based on the GeoNetwork 4 api, with an ElasticSearch backend. The search is fast, accurate, multilingual and customizable. The solution has been designed from use cases: how do you want to help the end users to find, use and value their datas. It brings a new experience to old fashion INSPIRE catalogs and aims to embrace modern challenges like the community, vote, favorites, publishers, usages of the datasets, dataviz and so on. Leveraging technical challenges to merge open data and metadata, the datahub emphases on a pure, intuitive and fluent user experience.

uk: вхід без авторизації

name: Datahub: the confluence of open data and geo data


uk: Комп'ютерні науки

description: Compass Informatics [1] is pleased to announce the open sourcing of its routing library Wayfarer [2][3]. Wayfarer is a pure Python library that allows spatial features to be loaded into a NetworkX [4] network format. Once in this format the data can be manipulated and analysed using the huge range of graph algorithms in NetworkX. The Wayfarer library provides a number of helper functions for example to calculate routes, split edges, find ends of paths, and retrieve features by keys. The talk will outline the use cases for the library, and when it may be suitable to use rather than alternatives such as pgRouting. Case studies will be presented including Wayfarer’s use in Ireland's Pavement Management System to help designate works and surveys on the road network. Wayfarer is also currently used for an Environmental Protection Agency project to create fully connected river networks in Ireland. [1] compass.ie/ [2] pypi.org/project/wayfarer/

uk: вхід без авторизації

name: Introducing Wayfarer - a Python Routing Library


uk: Комп'ютерні науки

description: Examination of Metro Stations Equal Accessibility for All using Open Data Kit (ODK) Applications: A Case Study of Noida City in India Nowadays, the need is felt to create a sustainable and inclusive urban environment accessible to all, which requires a people-centered urban planning approach. Along with alleviating environmental problems and minimizing traffic congestion, the public transit system serves as a means of providing equal access (Rossetti, et al., 2020). This paper attempts to re-evaluate the isochrones prepared to access public transport stops particularly transit nodes across Noida city using a GIS-based approach and Open Data Kit (ODK) approach. The isochrones predict the time to reach any area from a transport node like a transit station based on the shortest path model, however, not all roads and streets offer equal access to all (Lei & Church, 2010). In this study, macro-built-environment attributes responsible to increase pedestrian distance length and time to reach metro stations were identified using a GIS-based approach by integrating land-use and transportation data. However, the micro-built environment attributes like pedestrian behaviour, preference of travel modes, and purpose and frequency of transit trips made by the transit users were gathered by conducting metro station user surveys using the ODK app linked with its ODK aggregate server. The ongoing transport and urban planning methods hardly give any importance to the understanding origin and destination to reach important places with more ease and mobility (Bhatt & Minal, 2022). Urban researchers have not investigated any studies to evaluate equal accessibility to effectively and smoothly use public transit services by easily accessing transit stations (Yang, et al., 2019). The objective of this study is to map the pedestrian permeability and impermeability by categorizing roads and streets around identified transit nodes as public, private, and non-accessible by all. The prime function of accessibility is to link people with activities through linkages (Lei & Church, 2010). In this study, the travel modes particularly considered were on foot, non-motorized (cycle and rickshaw), shared e-rickshaw, bus service, and dropped off by two-wheeler and four-wheeler. A stratified random sampling technique was adopted to calculate the sample size of 12 existing elevated metro stations in Noida on the Blue transit line of DMRC. A self-administered questionnaire was used to conduct metro station surveys using ODK mobile app at identified 12 metro stations in Noida starting from Noida Sector 15 until the last Noida Electronic city (NEC) station. A sample size of 1% of the average transit ridership data, collected from DMRC for each station was taken to achieve a 95% confidence level. However, some stations like the Golf course and stations following Noida city center operational since the year 2019 have low ridership below 5000 persons per day. For these stations, 2% sample size of the average ridership data was considered to achieve a similar confidence level. Following this step, the existing land use encircling individual metro stations within a radius of 800 meters was demarcated from Noida Master Plan (NMP) 2031. Based on static master plan land-use distribution, the stations were categorized as residential, non-residential, mixed-use and transport hubs. The questionnaire contained 31 items which included various aspects including the usual purpose of metro trips made, employment status, availability of the driving license, household size, current city living in, number of cars available in the household, car availability during the transit trip made, building topology of the transit user, and number of floors. Most importantly, the preferred travel mode to reach the nearest metro station, frequency of trips made in a week, metro travel pattern changed in past six months, particularly due to COVID-19 restrictions, and the reason to opt for transit services. Other allied questions were distance and time to reach the metro station using different travel modes. The survey results were downloaded for the ODK aggregate server and converted to an excel sheet from CSV file format for its data analysis. In QGIS, a buffer distance of 800 meters in both directions was marked along the blue line transit corridor with its 12 metro stations in Noida. Initially, typically common walkable pedestrian routes terminating at individual metro stations were identified with their trip origin location in the nearby sector. Based on the farthest location found in all the directions encircling the metro station, walkable sheds were developed for all the 12 metro stations in Noida. However, as the stations are quite close in between 1 to 2 kilometers, the walkable sheds overlap for some consecutive metro stations. Thereafter, using Garmin Etrex 10, all the common routes, most commonly followed by e-rickshaws as an alternative to walking were traced. The photographs of the barriers found that typically increase the walking distance and time to reach the metro stations were clicked. Thereafter, using My Maps an app by Google, the Garmin tracked routes were fed along with photos imported through the Google Photos app. In the case of Noida, there exist both planned and unplanned barriers to equal accessibility for all. While, the gated communities, large super-blocks, and many Government housing societies are planned barriers that impede accessibility for all within TOD station areas. The presence of urban villages with organic street layouts and narrow incomplete streets are hardly accessible and force potential transit riders to shift away from walking or rely on e-rickshaws as short to medium-distance travel modes. Finally, the study proposes a TOD index based on the Space syntax model and distance measurement to categorize roads within the TOD area as public roads, private roads - accessible by only a few residents, non-suitable streets - not fit for use.

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Curated Major Map Features Library

uk: Комп'ютерні науки

description: A team of experienced mappers and language experts at Meta has reviewed a dataset of major map features from OpenStreetMap(OSM), and used the curated results on one of their validation processes to check for quality issues of the Daylight and OSM maps. The dataset of the curated results is considered as a reference library of major map features with their key information. During the validation process, this library is used to compare against Daylight and OSM data to look for suspicious changes on features included in the library. With such reference library, the Meta Basemap Team is able to keep a stable quality on major map features in an efficient manner. To ensure the data in this library is up-to-date and comprehensive, systematic approaches to continuously improve the library are also developed. In our talk, we will share more details about this curated library and processes, and how we maintain the freshness of the library.

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uk: Комп'ютерні науки

description: Building footprint extraction is a popular and booming research field. Annually, several research papers are published showing deep learning semantic segmentation-based methods to perform this kind of automated feature extraction. Unfortunately, many of those papers do not have open-source implementations for public usage, making it difficult for other researchers to access those implementations. Having that in mind, we present DeepLearningTools and pytorch_segmentation_models_trainer. Both are openly available implementations of deep learning-based semantic segmentation. This way, we seek to strengthen the scientific community sharing our implementations. DeepLearningTools is a QGIS plugin that enables building and visualizing masks from vector data. Moreover, it allows the usage of inference web services published by pytorch_segmentation_models_trainer, creating a more feasible way for QGIS users to train Deep Learning Models. pytorch_segmentation_models_trainer (pytorch-smt) is a Python framework built with PyTorch, PyTorch-Lightning, Hydra, segmentation_models.pytorch, rasterio, and shapely. This implementation enables using YAML files to perform segmentation mask building, model training, and inference. In particular, it ships pre-trained models for building footprint extraction and post-processing implementations to obtain clean geometries. In addition, one can deploy an inference service built using FastAPI and use it in either web-based applications or a QGIS plugin like DeepLearningTools. ResNet-101 U-Net Frame Field, ResNet-101 DeepLabV3+ Frame Field, HRNet W48 OCR Frame Field, Modified PolyMapper (ModPolyMapper), and PolygonRNN are some of the models available in pytorch-smt. These models were trained using the Brazilian Army Geographic Service Building Dataset (BAGS Dataset), a newly available dataset built using aerial imagery from the Brazilian States of Rio Grande do Sul and Santa Catarina. Pytorch-smt also enables training object detection and instance segmentation tasks using concise training configuration. This way, considering the aforementioned, this talk presents the usage overview of both technologies and some demonstrations. Using metrics like precision, recall, and F1, we assess the results achieved by the implementations developed as a product of our research, showing that they have the potential to produce vector data more efficiently than manual acquisition methods. DeepLearningTools is available at the QGIS plugin repository, while pytorch_segmentation_models_trainer is available at Python Package Manager (pip). The Brazilian Army Geographic Service develops both solutions, making their codes available at GitHub - dsgoficial.

uk: вхід без авторизації

name: Building Footprint Extraction in Vector Format Using pytorch_segmentation_models_trainer, QGIS Plugin DeepLearningTools and The Brazilian Army Geographic Service Building Dataset


uk: Комп'ютерні науки

description: In 2020 National Land Survey of Finland had scanned and digitized over 100.000 historical orthophotos from 1931 to 2020. This unique dataset had been open for a couple of years via a WMS-T API service but was not well known to the public at the time, and not truly available to less technically oriented users. Hence, there was a need to get them findable and easily accessible with a web browser. Instead of building a completely new service, the images were to be published with the Open Source based national geoportal of Finland - Paikkatietoikkuna. The geoportal is built with Oskari Map Application Platform which was enhanced for this use case to support timeseries data. The historical orthophotos are scattered both in time and in geography. To improve end-user experience and make discovering data easier an OGC API Features service was used for metadata. The final result was received with quite a bit of enthusiasm: after publishing the data in Paikkatietoikkuna geoportal in June 2021, the visitor numbers soared to new records. Nearly all of the feedback has been positive, and now it is possible for anybody to benefit from this extremely valuable and fascinating historical data. The code is fully open source and can be easily used in any Oskari instance – all you need is the data, which, obviously, isn’t the easiest part of this all.

uk: вхід без авторизації

name: Developing an UI for historical orthophotos timeseries data


uk: Комп'ютерні науки

description: Neither the open (geo)data initiative and, needless to say, the open source for geospatial one, are the new kids on the block anymore. Crucial steps have been taken all over the world in establishing the framework of openness, collaborative development and transparency ranging from hands-on events - such as code sprints or collaborative data gathering - mapathons - to funding opportunities and legislative measures. In this context, we present a 3 years-old EU supported initiative founded on open source and open data and that has reached maturity. In our talk, we present the potential support that such initiatives have in the wider framework of environmental monitoring and reporting across Europe - Geo-harmonizer. Geo-harmonizer stands for EU-wide automated mapping system for harmonization of Open Data based on FOSS4G and Machine Learning. The project unfolded between 2019 and 2022 and was co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union.

uk: вхід без авторизації

name: FOSS4G and Open Data to the rescue: a European story!


uk: Комп'ютерні науки

description: Water and Sanitation Corporation (WASAC) (wasac.rw/) started mapping rural water supply system in Rwanda since 2018. WASAC conducted the data collection by using QGIS, QField and PostGIS all over the country of Rwanda, and now all GIS data is available as open data and visualized in this website (rural.water-gis.com/) by using Mapbox Vector Tiles. WASAC is trying to achieve universal access to water in SDGs Goal 6 by keeping updating and utilizing GIS data. We are developing GIS system as open source, and all of source code was developed in Github through WASAC organization repositories here under the collaboration with The United Nations Vector Tile Toolkit team. Our approach uses quite low-cost technologies which are more sustainable in low and middle-income countries. All our data is also available in OpenAFRICA (africaopendata.org/organization/water-and-sanitation-corporation-ltd-wasac). Our achievements of the project were presented in previous global conference of FOSS4G 2019 Bucharest and FOSS4G 2021 Buenos Aires. In FOSS4G 2022, we would like to update our current situation of the GIS system to the community.

uk: вхід без авторизації

name: Rural water supply mapping by using FOSS4G application in Rwanda


uk: Комп'ютерні науки

description: The story and the future of the MapLibre community - the project that continues to develop various browser and native technologies for map tile visualization ever since Mapbox changed their licensing on the amazing Mapbox gl js technology that sadly became proprietary restricted to Mapbox own service. This talk will cover existing lib capabilities, how the project grew to include native, navigation, routing, 3D, and other features. How the project was able to quickly migrate to typescript with lots of additional testing and stabilization efforts. How we became a large non-centralized collective of mapping technologies covering web, android and ios devices. How hundreds of small and large donations from developers and companies have helped with extra incentives. Some possible future projects and ideas will be presented by individual feature owners, including the possibility of uniting all library efforts using a cross platform compilation from the common Rust code (web assemblies + native libs) and additional styling features.

uk: вхід без авторизації

name: OpenSource to the rescue: the future of MapLibre

name: Open Source Geospatial Foundation (OSGeo)


uk: Комп'ютерні науки

description: Connectivity of roads in a map is essential for many use cases including navigation. We present a graph-based solution to the road conflation problem which takes into account the connectivity of the road network. First, we generate a road network graph in both sources based on bifurcation points. Second, we carry out node and edge matching between the graphs where we follow shortest distance as a matching criterion. This is followed by the merging stage where graph edges with matching end nodes get conflated. Newly added roads are connected with the graph based on node and edge matching. We carry out experiments on conflating open source footway datasets from multiple cities with the OSM. The resulting conflated map contains up to 16x map feature improvements per city with geometrically accurate and smooth results around road junctions. Future work involves using different graph matching criteria to improve on the conflated output.

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name: A Graph-Based Road Conflation Method Preserving Connectivity


The story of OSGeo in Oceania

uk: Комп'ютерні науки

description: OSGeo has existed in Oceania in various forms for quite a while now. Some of the major contributors to projects such as QGIS are based in Oceania and open geo events have been organised in the region for many years. It is only in more recent times however that we have started to support these efforts through the creation of a local chapter. When a group of us came together to organise Oceania’s first regional FOSS4G SotM conference in Melbourne, 2018, it became clear structure was needed to sustain the momentum we had created. Structure was established by forming an entity and completing all the tasks that go along with that. This included creating a constitution, financial policies, forming a board of directors, establishing a membership policy, consulting with the community, working out what the entity’s primary purpose is and so much more. We’ve made plenty of mistakes along the way, but we’ve also learned a lot. There are many successes too, such as the establishment of a Microgrant program to support initiatives across the region, the continuation of annual regional conferences, funding travel so that people without the financial means to do so could attend conferences, and the welcoming of new members from far and wide. This talk is an insight into the journey of OSGeo Oceania. It is not meant to be a how to guide or a pat on the back, but rather a chance to facilitate discussion among the FOSS4G community so that we can find ways to support the use and understanding of open geospatial software in our respective regions.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: TerriaJS is an open-source framework for web-based geospatial catalogue explorers. It uses Cesium and Leaflet to visualise 2D and 3D geospatial data, and it supports over 50 different Web APIs, file formats and open data portals. It is almost entirely JavaScript in the browser, meaning it can even be deployed as a static website, making it simple and cheap to host. TerriaJS is used across the globe to create next-generation Digital Twin Platforms for open geospatial data discovery, visualisation and sharing - it is used to drive - National Map (nationalmap.gov.au/) (Australian Gov) - Digital Earth Australia Map (maps.dea.ga.gov.au/) - Digital Earth Africa Map (maps.digitalearth.africa/) - Pacific Map (map.pacificdata.org/) - NSW Spatial Digital Twin (nsw.digitaltwin.terria.io/) (Australian State Gov) - and many others Supported formats/protocols include: - Imagery services like WMS/WMTS and ArcGis Imagery/Map Service - Feature/vector services like WFS, ArcGis Feature Service, Mapbox vector tiles, GeoJSON, Shapefile, KMZ, GPX, GeoRSS - 3D sources like Cesium 3d-tiles, GLTF and CZML - Tabular/sensor data: CSV, SDMX, GTFS, SOS, Socrata and Opendatasoft - Open Data portals: CKAN, CSW, Socrata, Opendatasoft, Magda, THREDDS, WMS/WFS Servers, ArcGis Portal and SDMX - Geoprocessing with WPS - With plans to support new and upcoming services like OGC APIs and STAC in the future. In this talk, I will show how TerriaJS can connect to Open Data Portals to - Discover open datasets - Visualise datasets in 2D and 3D - Perform aggregation/analysis on datasets - Create/share maps with the world!

uk: вхід без авторизації

name: Exploring the World’s Open Data Portals - Discovery, Visualisation and Analysis of Open Data with TerriaJS


uk: Комп'ютерні науки

description: Approach and concepts 3D-georeferenced historical pictures have a high potential for the analysis of different landscape features such as melting glaciers, the effects of urbanization or natural hazards. Moreover, historical pictures have a higher temporal and spatial resolution than satellite imagery and therefore allow for analyses that go farther back in time. A 3D georeferenced picture can for instance be combined with a digital terrain model (DTM) and other reference data to calculate the exact footprint of the picture and to generate a list of visible toponyms that can be used to find pictures of a specific place or region. The utilization of historical pictures is unfortunately still difficult: 1. historical pictures need to be digitized 2. collections are often spread across several places in different archives and collections 3. metadata is often not available. In the ongoing open-source project Smapshot (Produit et al. (2018), smapshot.heig-vd.ch/) over 150’000 digitized historical pictures have been georeferenced in 3D by more than 700 participants. In the web-platform Smapshot a participant can georeference a picture using monoplotting (Bozzini et al, 2012): ground-control-points (GCP) are digitized both in the historical picture and in a virtual globe that displays recently updated data. These GCP allow for the calculation of the exact position from where the picture has been taken (3D point) and the three angles that define the direction of view: roll, pitch and yaw. Once the position and the direction of view has been calculated a footprint of the picture is generated using a DTM. Results In order to make the pictures and the metadata from Smapshot available to the public, an open API for 3D-georeferenced historical pictures has been created. The goal was to offer free access to all the data in the Smapshot database and to allow for different types of queries such as retrieving the footprints of the photos, fetching metadata for a picture (e.g. owner, title, date, x/y/z position and roll, pitch, yaw angles) or retrieving photos that are within a certain range from a specific point. This API was built in NodeJS (nodejs.org/) with a PostgreSQL/PostGIS (postgresql.org/, postgis.net/) database and python code for the georeferencing algorithm. The API is a REST API fully documented using the OpenAPI specification. The API project has been open-sourced and specific test-suites have been put in place to ensure quality and to allow community contribution with confidence. One challenge for the establishment of this API was standardization: Today there are several standards for the definition of metadata in pictures such as the IIIF (iiif.io/) or the Dublin-Core (dublincore.org/) standards. These standards however have limited support for geospatial data. On the other hand, spatial standards poorly support pictures that are oriented in 3D. The glTF standard (khronos.org/gltf/) is one example and there is also a recent initiative from the OGC called GeoPose (ogc.org/projects/groups/geoposeswg) which formalizes a standard to define a 6DoF pose everywhere on Earth including a position and orientation in 3D. Reasons why it should be considered 3D georeferenced images are increasingly used by several projects that document change over time; e.g. within the field of digital humanities even paintings can be considered for 3D georeferencing and differences between the real world and the painted world can give room for analysis and interpretation. Another use-case is the creation of geovisualization-applications that show the contents of historical pictures in 3D and that enable a user to compare its contents to the real world (e.g. augmented or virtual reality applications) Furthermore in the context of climate change, pictures and paintings document change and deliver evidence. Image processing techniques can be used to automatically detect features (machine learning) and if several pictures are available for one region (but taken from slightly different viewpoints) 3D features can be generated. The open API for 3D georeferenced historical pictures makes these types of analyses easier and opens up the data for a larger public. It also becomes possible to implement other solutions that utilize the data directly - e.g. for displaying historical pictures in a 3rd party web page or for implementing machine-learning processes that automatically download pictures and metadata in order to recognize features and places. The results of the project are also an important input for standardization activities that aim at establishing standards in the context of georeferenced pictures and their metadata. An important perspective of the project is the establishment of an infrastructure for 3D-georeferenced pictures that can be deployed on a national or international level and that also offers the possibility to push new data (e.g. pictures) in the database. This work is of interest for researchers who want to utilize and analyze 3D georeferenced historical imagery and for people who want to establish open API’s to give access to data that is relevant for research.

uk: вхід без авторизації

name: An open API for 3D-georeferenced historical pictures


uk: Комп'ютерні науки

description: Converting OpenStreetMap planet data into vector tiles has been a complex and costly process, but now, thanks to the Planetiler project, it has become possible to do on a single powerful machine in just a few hours – over two orders of magnitude speed up! OpenMapTiles is a mature customizable tile generation framework and layer specification that can be tailored to specific tile generation needs. It has existed for many years, and allowed users to generate their own layers, optimizing for size or completeness. Over the years it moved to PostGIS-based ST_AsMVT approach, and made numerous small improvements. The biggest downside of OMT was the extensive hardware requirements. Recently Mike Barry rewrote core functionality of the OMT stack as a single monolithic app, making it possible to generate entire planet data in just a few hours on a single machine. Now the OMT community is actively adapting this new approach, researching if Rust would be even better approach, and experimenting how to make the process customizable and support real-time updates.

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name: OSM planet data to vector tiles in a few hours: OpenMapTiles & Planetiler


uk: Комп'ютерні науки

description: The objective of ARCOS is to design and implement an early-warning system providing continuous monitoring of the Arctic Region. Designed to generate actionable products in the security domain by processing and fusing multi-sensor data, the system integrates available information from space, non-space sources and products available from multiple Copernicus services. ARCOS generates information at three different levels of scale and user interaction: 1. Level 1: Automatic Early-warning System. Integration of space and non-space data sources for the triggering of alarms on the region when certain conditions happens. Automatic early-warnings are generated in case anomalous behaviours are detected. For this wide-area monitoring, automatic extraction of analytics and AI techniques are applied. 2. Level 2: User-Driven Alert System, where space and non-space data is processed on specific locations provided by the user. The alarms can be configured based contextual information based on the user input. 3. Level 3: Geospatial Intelligence Products. Following early-warnings generated in Level 1 or 2, geospatial intelligence products requiring human intervention are provided upon user request. The system is developed using GeoNode (https://geonode.org/) as a Core component and integrates OpenEO services (https://openeo.org/) for the generation of innovative contents from open datasets like Copernicus Sentinels data, Copernicus Services data, AIS data and Social media.

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name: ARCOS Platform for Monitoring of Arctic Region


uk: Комп'ютерні науки

description: The OGC APIs are a fresh take at doing geo-spatial APIs, based on WEB API concepts and modern formats, including: Small core with basic functionality, extra functionality provided by extensions OpenAPI/RESTful based JSON first, while still allowing to provide data in other formats No mandate to publish schemas for data Improved support for data tiles (e.g., vector tiles) Specialized APIs in addition to general ones (e.g., DAPA vs OGC API - Processes) Full blown services, building blocks, and ease of extensibility This presentation will provide an introduction to various OGC APIs and extensions, such as Features, Styles, Maps and Tiles, DAPA, STAC and CQL2 filtering. While some have reached a final release, most are in draft: we will discuss their trajectory towards official status, as well as how good the GeoServer implementation is tracking them, and show examples based on the GeoServer HTML representation of the various resources.

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name: Demystifing OGC APIs with GeoServer: introduction and status of implementation


uk: Комп'ютерні науки

description: Developing a plugin for QGIS is both as simple as one of the countless tutorials and as complicated as a software engineering job facing with the dynamism of the project (maintenance requirements), the size of the APIs and constraints that need to be taken into account (Windows, etc.). At Oslandia, we create and maintain many plugins for our clients, which leads us to streamline their development... and especially their maintenance! Historically, many extensions were created using the amazing Plugin Builder and the underlying tool (pb_tool) but it no longer fit our needs. We present here our QGIS Plugin Templater, based on Cookiecutter (cookiecutter.readthedocs.io/) and the related work on developer tools (tests, documentation, code structure, formatting, linter...). We will also mention the other tools we are using or following closely (the 3Liz toolbelt, the other template from Gispo Coding...). Yet Another Plugin Generator? Probably but we think it's worth it!

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name: Better productivity for QGIS plugin developers


uk: Комп'ютерні науки

description: Due to its SQL-based engine, the OpenSource Software pgRouting is the most flexible routing engine available. A common misconception is that pgRouting is the least performant routing engine. So how to keep both performance and flexibility with pgRouting ? Many factors should be taken into account. To begin with, the use cases. What level of precision do you need ? Will you be computing short or long routes ? Will there be many routes computed at the same time ? Simplification is the most common way to deal with performance issues. However, when accuracy is at the core of decision making, a minimum level of precision must be kept. Reducing the number of rows the routing engine will have to process is the number one tip to enhance performances. But there are many other technical and functional optimizations that can make pgRouting run much faster. We will look at some choices we had to make in various projects. How the data is the first key to optimization. But also how to help the routing engine make the best of it. Which algorithms are best used for which use cases, and how fine tuning the database can help too.

uk: вхід без авторизації

name: pgRouting optimization: from technical to functional


uk: Комп'ютерні науки

description: Micronutrient deficiencies (MNDs), so-called ‘hidden-hunger’, can have serious ramifications for the health of individuals affected and the economy of the country in which they live. MNDs are a global problem but disproportionately affect populations in low-income countries. Work to alleviate these deficiencies aligns with the UN’s Sustainable Development Goals (SDG), especially SDG2 – access to adequate safe and nutritious food. Data which can support the understanding of the scale and location of these deficiencies can be fragmented in their availability and accessibility, creating a barrier to their use in planning interventions by stakeholders in the very nations where the impacts of MNDs are most severe. The Micronutrient Action Policy Support (MAPS) tool is a web-hosted open access platform providing a unique enabling environment for the wider agriculture-nutrition community and beyond which allows users to view and explore MND risks at various spatial and temporal scales. The tool can provide users with dietary micronutrient supply estimates of all nations in sub Saharan Africa using national-scale and subnational-scale data. Preprocessing steps to clean these data in R language are made available through the open github repository, so that any user can replicate the data used in the tool. Priorities for the data and functionality have been co-designed with key users from project proposal stage. Stakeholder feedback is used in continued iteration as richer content, supporting material, and functionality is planned, developed and released. The platform is built on open-source technologies utilising Postgres and PostGIS to store, combine and interrogate a range of heterogeneous datasets to calculate micronutrient supply estimates, node.js for data APIs and web map services using Geoserver. Further data processing is conducted using R, with the front-end interface utilising Angular, leaflet.js and chart.js. Metadata is managed and served via Geonetwork. The code for the platform, as well as data processing scripts, methods and processes are all open source at https://github.com/micronutrientsupport. This talk will provide an overview of the platform along with the datasets and open-source technologies that underpin its functionality, and the UX approaches taken to ensure that this tool meets the currently unmet needs of priority users. (micronutrient.support)

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name: Micronutrient Action Policy Support (MAPS) - A decision support tool for investigating the scale and geographic distribution of micronutrient availability in sub-Saharan Africa


State of GDAL

uk: Комп'ютерні науки

description: We will give a status report on the GDAL software, focusing on recent developments and achievements in the 3.4 and 3.5 GDAL versions released during the last year, but also on the general health of the project. In particular, we will present new drivers such as the one handing Zarr datasets (format for the storage of chunked, compressed, N-dimensional arrays) or the Spatio-Temporal Asset Catalog Items driver to create virtual mosaics from STAC items, and potential future additions such as a new JPEG-2000 based driver using the Grok library, a driver for the SAP Hana database or driver for columnar storage format such as Apache Parquet and Arrow. The topic of coordinate epochs in geospatial datasets and how we’ve addressed it in various formats (GeoTIFF, GeoPackage, FlatGeobuf) will also be mentioned. As well as other improvements such as the JPEG-XL codec for the GeoTIFF format, or support for 64-bit integer data types in rasters. We will present the new CMake build system, the roadmap for its implementation, and its advantages for users and developers.

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uk: Комп'ютерні науки

description: Mapillary is the platform that makes street-level images and map data available to scale and automate mapping. There are many tools available within Mapillary’s ecosystem, as well as many real world use cases where Mapillary can have an impact. In this talk, we will give an overview of the state of the Mapillary platform in 2022. This will include a look at compatible camera devices, upload methods, data and imagery management, download methods, integrations, and stories about users who apply Mapillary to solve a challenge. You should walk away from this talk knowing how you want to use Mapillary to improve maps important to you, and what tools you need to get started. If you are interested in improving OpenStreetMap, contributing to open data, capturing imagery in your community, or leveraging Mapillary street-level imagery and GIS data into your professional work, this talk is for you. No coding or technical experience is necessary, and the tools and features available can be adapted to any skill level. Join us!

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name: How to Mapillary - Getting started with Street-level Mapping


uk: Комп'ютерні науки

description: QWC2 (QGIS Web Client 2) is the official web application of QGIS, that allows you to publish your projects with the same rendering, thanks to QGIS Server. The environment is composed of a modern responsive front-end written in JavaScript on top of ReactJS and OpenLayers, and several server-side Python/Flask micro-services to enhance the basic functionalities of QWC2 and QGIS Server. QWC2 is modular and extensible, and provides both an off-the-shelf web application and a development framework: you can start simple and easy with the demo application, and then customize your application at will, based on your needs and development capabilities. This talk aims at introducing this application and to show how easy it is to publish your own QGIS projects on the web. An overview of the QWC2 architecture will also be given. It will also be an opportunity to discover the last new features that have been developed in the past year and ideas for future improvements.

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name: Easily publish your QGIS projects on the web with QWC2


uk: Комп'ютерні науки

description: G3W-SUITE is a modular, client-server application (based on QGIS-Server) for managing and publishing interactive QGIS cartographic projects of various kinds in a totally independent, simple and fast way. The suite is made up of two main components: G3W-ADMIN (developed through Python, using Django ) as the web administration interface and G3W-CLIENT as the cartographic client., developed using a modular approach and is based on a “reactive programming” paradigm using Vue.Js, Javascript framework and OpenLayer3. This components communicate through a series of API REST. The application is compatible with QGIS 3.22 LTR and it is based on strong integration with the QGIS API. It is released on GitHub with Mozilla Public Licence 2.0 Many graphic/functional aspects of the WebGis publication derive directly from QGIS projects as, first of all, the general and OGC services capabilities. The suite automatically inherits aspects related to the project (themes, 1: N relations, simple and atlas print layout, filter on legend based on map content, layer display order and activation status ...) and related to individual layers (activation scale, interrogability, published attribute fields, join attributes, attribute form, editing widgets ...) . Of particular interest is the strong integration with the QGIS DataPlotly plugin. QGIS projects can be published as WebGis services via direct upload (no plugins needed) on the Administration component. The granular system of permissions and the subdivision into roles of users (individuals or groups) allows the management of services to be delegated to second and third level administrative users. It is also possible to define consultation permissions on individual WebGis services and editing permissions on individual layers with different editing powers per user. Finally, it is possible to define geographic and alphanumeric constraints (both in consultation and in editing) differentiated by individuals or groups of users. Alphanumeric constraints can be based on SQL language or QGIS expressions. It is also possible to define for each layer aspects relating to the preparation of predefined searches, caches and downloads in various formats. A particularly advanced function is related to online editing and to the possibility of easily creating web cartographic management systems by defining the various aspects at the level of the QGIS project. This function (operating directly on the data through the QGIS API) allows multi-user editing thanks to a feature-lock system. Editing works both at the geometric level (with intra- and inter-layer snap), and at the attributes level (editing form and the widgets included) also connected by joins or 1:N relationships. The call will allow to illustrate the innovations of the current and future versions. These include the implementation of editing functions, user-based filters linked to the visibility of layers and attributes, the possibility of using QGIS projects based on embedded base projects and the integration of the vectorial and raster Temporal Controller of QGIS. Online geographic analysis possible thanks to the integration of Processing algorithms through dedicated APIs.

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name: G3W-SUITE: an OS framework dedicated to the publication and management of QGIS projects as WebGis services


uk: Комп'ютерні науки

description: Sensordata (IoT) is widespread in both private and public sector. However, making use of sensordata across different sectors and applications is challenging - in particular with respect to a geospatial application across different use cases. This encompasses both enviroment/climate sensors, like water-level sensors to smart-building monitoring and water pipe sensors. An interdisciplinary team from diverse sectors is working towards building national standards, an open architecture and implementing proof-of-concepts on a national sensorhub for sharing streams and archives of sensordata in Norway. The team builds upon the very successful open data ecosystems (SDI) that exists in Norway for standardized geospatial data. The project is funded from a range of partners including municipalities, the mapping authority and the maritime ports of Norway. The working group includes open source tech expertise on sensor technology alongside user and demand expertise from the different sectors. This talk will focus on the technological advances made from the team both on software and architecture. There will be particular focus on the open architecture and software prototyping that has been developed in the working group. Both of which will be available under an open license.

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name: Norwegian National SensorHub - sharing IoT data with open standards and technology


uk: Комп'ютерні науки

description: Geological data usually suffer from very low visibility because they are specialized data that are only accessible to a few people and can only be visualized and processed using special software. The swissgeol.ch portal, newly launched by swisstopo (Swiss Federal Office of Topography), aims to change this by making the data accessible on the Internet in a low-threshold and simple way using 3D visualization based and promoted with open-source technology and code. swissgeol.ch is a web application for the visualization and analysis of geological sub-surface data. It has been publicly available at viewer.swissgeol.ch since 2020, and the open source code is can be downloaded at GitHub.com/swissgeol/ngm. In addition to the geo-portal of the Swiss Confederation (map.geo.admin.ch) which focusses on 2D spatial data, swissgeol.ch extents its functionality to 3D data above, on and below the surface. For this, it relies on 3D visualization on the web, which is based on CesiumJS and offers numerous expert tools. CesiumJS is the most widespread open source 3D globe library and is used worldwide in many different applications. It not only visualizes large-scale global data, but also very detailed data at the local scale, such as buildings in the 3D view of map.geo.admin.ch. With the development of swissgeol.ch, an underground navigation option was developed in CesiumJS for the first time, which allows the visualization of 3D objects below the terrain. In addition to navigating underground, it is also possible to see through the earth's surface using transparency settings, as well as to slice the 3D-scene vertically. With the use of 3D tiles and precise terrain (2m precision), the data is delivered in an optimized format for the web. At the same time, the download of original data of entire layers or individual objects in the layer is offered. After the positive echo of last year’s talk, we are going to present this year selected features and data in greater detail and introduce recent developments: A new user experience and a specific user space for projects, as well as new data and formats, e.g. using the new voxel Cesium Next Tiles specs.

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name: swissgeol.ch - Geology in 3D: new features