uk: Комп'ютерні науки

description: Earth observation (EO) imagery has become an essential source of information to better monitor and understand the impact of major social and environmental issues. In recent years we have seen significant improvements in availability and accessibility of these data. Programs like Landsat and Copernicus release new images every day, freely and openly available to everyone. Technological improvements such as data cubes (e.g. OpenDataCube), scalable cloud-based analysis platforms (e.g. Google Earth Engine) and standardized data access APIs (e.g. OpenEO) are easing the retrieval of the data and enabling higher processing speeds. All these developments have lowered the barriers for utilizing the value of EO imagery, yet translating EO imagery directly into information using automated and repeatable methods remains a main challenge. Imagery lacks inherent semantic meaning, thus requires interpretation. For example, consider someone who uses EO imagery to monitor vegetation loss. A multi-spectral satellite image of a location may consist of an array of digital numbers representing the intensity of reflected radiation at different wavelengths. The user, however, is not interested in digital numbers, they are interested in a semantic categorical value stating if vegetation was observed. Inferring this semantic variable from the reflectance values is an inherently ill-posed problem, since it requires bridging a gap between the two-dimensional image domain and the four-dimensional spatio-temporal real-world domain. Advanced technical expertise in the field of EO analytics is needed for this task, making it a remaining barrier on the way to a broad utilization of EO imagery across a wide range of application domains. We propose a semantic querying framework for extracting information from EO imagery as a tool to help bridge the gap between imagery and semantic concepts. The novelty of this framework is that it makes a clear separation between the image domain and the real-world domain. There are three main components in the framework. The first component forms the real-world domain. This is where EO data users interact with the system. They can express their queries in the real-world domain, meaning that they directly reference semantic concepts that exist in the real world (e.g. forest, fire). For simplicity reasons, we currently work on a higher level of abstraction, and focus on concepts that correspond to land-cover classes (e.g. vegetation). For example, a user can query how often vegetation was observed at a certain location during a certain timespan. These queries do not contain any information on how the semantic concepts are represented by the underlying data. The second component forms the image domain. This is where the EO imagery is stored in a data cube, a multi-dimensional array organizing the data in a way that simplifies storage, access and analysis. Besides the imagery itself, the data cube may be enriched with automatically generated layers that already offer a first degree of interpretation for each pixel (i.e. a semantically-enabled data cube [1]), as well as with additional data sources that can be utilized to better represent certain properties of real-world semantic concepts (e.g. digital elevation models). The third component serves as the mapping between the real-word domain and the image domain. This is where EO data experts bring their expertise into the system, by formalizing relationships between the observed data values and the presence of a real-world semantic concept. In our current work these relationships are always binary, meaning that the concept is marked either as present or not present. However, the structure allows also for non-binary relationships, e.g. probabilities that a concept is present given the observed data values. We implemented a proof-of-concept of our proposed framework as an open-source Python library (see GitHub: ZGIS/semantique). The library contains functions and classes that allow users to formulate their queries and call a query processor to execute them with respect to a specific mapping. Queries are formulated by chaining together semantic concept references and analytical processes. The query processor will translate each referenced semantic concept into a multi-dimensional array covering the spatio-temporal extent of the query. It does so by retrieving the relevant data values from the data storage, and subsequently applying the rules that are specified in the mapping. If the relationships are binary, the resulting array will be boolean, with “true” values for those pixels that are identified as being an observation of the referenced concept, and “false” values for all other pixels. Analytical processes can then be applied to this array. Each process is a well-defined array operation performing a single task. For example, applying a function to each pixel or reducing a particular dimension. The workflow of chaining together different building blocks can easily be supported by a visual programming interface, and thus lowering the technical barrier for information extraction even more. This is demonstrated already in an operational setting by Sen2Cube.at, a nation-wide semantic data cube infrastructure for Austria, which uses our proposed semantic querying framework [2]. We believe our proposed framework is an important contribution to more widely accessible EO imagery. It lowers the barrier to extract valuable information from EO imagery for users that lack the advanced technical knowledge of EO data, but can benefit from the applications of it in their specific domain. They can now formulate queries by directly referencing real-world semantic concepts, without having to formalize how they are represented by the EO data. To execute the queries, they can use pre-defined mappings, which are application-independent and shareable. The framework eases interoperability of EO data analysis workflows also for expert users. Mappings can easily be shared and updated, and the queries themselves are robust against changes in the image domain.

uk: вхід без авторизації

name: Semantic querying in earth observation data cubes


Robert P. Spang

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: ## Motivation & Contribution Part of the development of an analysis pipeline for mobility studies using GPS data is benchmarking its performance on both the raw data accuracy and the analysis pipeline itself. When we started to develop our algorithm for stop and trip classification, it became clear that we needed a precisely annotated dataset containing accurate stop and trip labels as a ground truth. Apart from validating our development, we wanted to have a reference point for comparing our analysis methods with existing libraries. For the study, we planned to equip participants with a smartphone to collect movement data in form of GPS and acceleration data for several days in a row. To prolong battery time, we chose a lower sample frequency. Our special focus was to create ground truth for stop and trip detection algorithms, hence the annotation focused on this. Through this manuscript, we contribute a comprehensive dataset providing accurate start and end timestamps for stops over 126 days. The STAGA dataset is an unprocessed table of GPS coordinates, annotated with a timestamp, altitude, GPS accuracy, and class label ("stop" or "trip"). Each sample labeled as a "stop" further contains the GPS coordinates of the location it's attributed to. The acceleration data is provided as a separate file, but covers the same time frame and contains a triple (x, y, z) of acceleration sensor readings for each given timestamp, sampled at 1 Hz. The STAGA~dataset is provided publicly and free to use. We further provide the iOS app used to create the diary data for simple stop/trip annotation while on the go. All this is made available under CC BY 4.0. ## Method #### Diary To create the dataset, we first tried a traditional diary approach: four researchers were taking notes, writing down addresses and times whenever they stopped. While this provided some first samples, it was a tedious and error-prone process, since taking notes is impractical in everyday life. Furthermore, it required looking up the coordinates belonging to each noted address, which works for clearly defined, urban spaces but can become problematic otherwise, e.g. in a park or a rural, outdoor environment as addresses aren't precise enough here. Because of that, we developed a simple iOS app that helped us annotate our movements. The app contains a map to validate the identified position, one button to start or end a stop, and a list overview of previously recorded stops. It captures the GPS position whenever a new stop is started and stores the current time as the start timestamp. When the button is pressed again, the stop is completed and the current time is stored as the end timestamp. Trips are derived from the intervals between two stops. Even more, the app allows exporting the captured annotations as a CSV file which can be directly used for benchmarking purposes. This way, we were able to create a GPS dataset containing precise stop/trip annotations, together with a reference position of the actual stop location. The diary was recorded using an Apple iPhone XR. #### Data Collection The device we used for the recordings was a ZTE Blade A5 (2019). It was configured to record GPS samples at a minimum accuracy of 25m, so if the device was unable to obtain a position reading within this radius, the data point was omitted. We sampled data with a frequency of 0.1 Hz and used both network and GPS as sources for determining the position (the smartphone supports A-GPS and GLONASS). It runs Android 9 and is equipped with a 2.600 mAh battery; during the recording of the dataset, the battery was always charged before the phone shut down. While the dataset contains mostly everyday life, it also holds small periods of vacation, travel, and hiking. Most trips were carried out by bike. However, the dataset contains long periods of walking, car traffic, and train rides as well. While the data was recorded in two different European countries (mostly urban environments), everything was rotated and projected into the North Atlantic for privacy protection. In the same vein, all timestamps have been shifted to start on January first in the year 2000. However, none of these changes should affect the performance of stop and trip detection algorithms, as the relative temporal and spatial accumulation of GPS records are not changed. ## Dataset Statistics The dataset contains 122,808~GPS and 7,813,740~accelerometer records. The recording time spans over 126.65~days. The diary contains 692~stops and 691~trips. The average (mean) duration of a stop is $240.8min$; the average trip duration is $22.7min$. On average, a stop contains $114.0$ GPS samples; a trip contains $63.5$ GPS samples (mean).

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: The STAGA-Dataset: Stop and Trip Annotated GPS and Accelerometer Data of Everyday Life

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: Simulation of the effects of possible regulations for the location of wind and photovoltaic power plants in the Lazio Regional Administration (Italy) The need to make electricity production increasingly sustainable requires careful planning of production plants, mainly for wind and photovoltaic energy conversion. Planning areas correctly, while respecting existing environmental constraints, is not an easy task and requires the collaboration of a panel of experts with different skills. The need to search for new sites to be allocated to renewable energy generation plants is dictated by the most pressing current events, the search for non-impacting energy sources to whose research and development specific points of the National Resistance and Resilience Plan are dedicated, to which are added the consequences of the newborn Ukrainian conflict that has definitively discovered the problematic relationship-dependence of Italy and Europe with energy supplies from non-European countries. Both issues are pushing the country towards a rapid search for new energy strategies for environmental reasons and to make up for natural shortages that require massive imports of gas and other resources from abroad. In particular, the National Recovery and Resilience Plan (PNRR), part of the European Next Generation EU (NGEU) programme, a 750 billion euro package allocated by the European Union to counteract the economic damage caused by the Covid-19 global pandemic, is an economic plan worth 248 billion euro that Italy can use in the five-year period from 2021 to 2026 to implement various reforms and repair the damage created by the pandemic crisis. The plan, presented to the EU under the name 'Italia Domani', envisages investments along three main axes: digitalisation and innovation, ecological transition and social inclusion. These economic interventions are intended to resolve the drama caused by the advent of the Sars-Cov2 virus and help solve structural problems in the Italian economy, accompanying the country towards a path of ecological and environmental transition. It also aims to resolve important issues such as territorial, generational and gender gaps. It is in this context that the national legislation is undergoing a revision, which has entrusted the regional administrations with the task of identifying the territorial criteria that favour or prevent the establishment of certain plants in the various areas of the territory. Each regional administration has the right to graduate the criteria according to the specific geomorphologic characteristics of its own territory and therefore the most efficient procedure would be to verify, with simulations in GIS environments, the effect of defining certain criteria on the territory to assess in advance which and how many areas could have greater or lesser suitability. On the basis of this consideration, we proceeded to experiment with the effects of the most common constraints by developing a real simulation on the territory of the Lazio Region. The experimentation used the well-known open environment QGIS 3.22, which made it possible to exploit the possibilities offered by the open territorial databases of the Lazio Region. It should be noted that the Lazio Region (like most Italian regions) has made many spatial data available in open format in recent years. The European directive called "Inspire" gave a boost to the use, standardisation and free dissemination of spatial data. It provides for the creation of a Community data infrastructure that simplifies the sharing between public administrations and user access to spatial information. In Italy, the directive was transposed into Italian law by Legislative Decree no. 32 of 27 January 2010, which established the National Infrastructure for Spatial Information and Environmental Monitoring as a node of the Community infrastructure. As a result of this implementation, the National Geoportal was created, which was followed by the various Regional Geoportals, such as that of the Lazio region. The implementation of the open data in QGIS 3.12 made it possible to identify topological inaccuracies in the files provided and shared on the Lazio Region site, which led to necessary decisions such as the correction of some polygons that presented errors, such as their overlapping or imperfect closure (the correction of the latter case was suggested by QGis itself, through the "reopen geometries" function). A possible inaccuracy was also found in the "lowland species" and "mountain species" files of the Regional Ecological Network, which seems to show an error in the transcription of the relative geodetic datum on the website, where it is reported as WGS84, UTM33N and resulting from verifications and overlapping more likely ED50 . Uncategorised areas also emerged in the file called "PTPR Regione Lazio (Tav. A - Tav. B)" which were however excluded from those considered to be unsuitable since a more detailed analysis revealed the area of the Parco della Caffarella in Rome, which is hardly conceivable as the site for a wind farm or large-scale photovoltaic plant. Extraterritorial areas within the Region's territory belonging to the State of "Vatican City" were also added as "unsuitable". The first results show that the remaining areas after eliminating all those that are certainly unsuitable are a limited part of the Region itself. It should be noted that these areas are not definitely suitable areas but those that are not unsuitable or potentially suitable, even if further investigation is required to ascertain their suitability. The limited extent of the areas remaining after the exclusion of the unsuitable areas suggested that we make an initial estimate of the sustainability of a total conversion to these energy sources for the whole region in order to assess its potential energy autonomy. The analysis was extended to individual municipalities by comparing average yields per conceivable plant area, then comparing them with inhabitants for an initial estimate of energy needs at least for domestic use.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: This article is a work in progress report on the introduction and exploitation of persistent identifiers (PID) within the OSGeo Foundation and its software project communities. Following an introduction to the topic of Persistent Identifiers (PID), an overview of the currently achieved states and emerging new opportunities, but also new challenges is given. The latter enables the OSGeo project communities to actively participate in the further development of data-driven open science and the evolution of the FAIR (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship from the original data focus to research software and community software projects. With the rise of the Internet and World Wide Web, Universal Resource Locators (URL) have become common practice to reference web resources. A URL specifies its location on a computer network and a mechanism for retrieving it. However, URLs are not a sustainable practice for scientific citation because they will break once the referenced resource is transferred to another web address; i.e., the original URL cannot be resolved anymore and an error message is returned instead (e.g., HTTP error 404). To counter this, persistent identifiers have been introduced as long-lasting references to web resources, including research data, source code, audiovisual content, and also human individuals or communities. Persistence is always achieved by infrastructure services which resolve the references to their target objects. This requires open standards, operation of infrastructure services and best practices for sustainable long term use. The adoption of PID use in the OSGeo Foundation continues for different application areas, with increasing synergy effects forming the foundation of a greater whole. The introduction of PID in OSGeo started in 2014 for a newly discovered version of the historical GRASS GIS informational video from 1987, which is preserved in the AV Portal of TIB Hannover (av.tib.eu/) and can be accessed through a permanent Digital Object Identifier (DOI) (DOI: 10.5446/12963, DOI: 10.5446/31768). Since 2016, OSGeo conference videos have been collected as a permanent service in the AV Portal, with the collection growing by approximately 100 hours of video recordings annually (pre-Covid). In 2017, the rasdaman software project registered a DOI for the first time for release version 9.4.2 in the Zenodo data repository (DOI: 10.5281/zenodo.1040170). Zenodo is a general-purpose open-access repository operated by the European Organization for Nuclear Research (CERN) since 2015. In 2019, the next DOI registration followed for the GMT software project for release version 6.0.0 (DOI: 10.5281/zenodo.3407865). Further improvements of the technical integration of project software repositories hosted on the GitHub platform and Zenodo have enabled a simplified handling of software versioning: When registering a DOI as a PID for a software project, at least two references are created, which are linked to each other: The Concept DOI, which represents the software project as a higher-level intellectual construct, and an initial Version DOI, which references a specific software release. With the integration now available between GitHub and Zenodo, the successive creation of additional Version DOI for upcoming new software releases can be done automatically. Since 2021, the number of DOI registrations by OSGeo software projects has increased significantly. Currently, DOIs are already available for 19 software repositories related to OSGeo projects (wiki.osgeo.org/wiki/DOI). More than half of the official OSGeo software projects can already be referenced by means of DOI. All projects that have registered a DOI have chosen an official scheduled release to initiate DOI versioning. Equipping OSGeo projects and content with PID results in significant added value for scientific users, but also for the respective project communities. Well formatted citations for software project DOI can be conveniently generated in thousands of different citation styles by online citation services (e.g. citation.crosscite.org/). Citation of OSGeo projects is already actively used in scientific publications (e.g. Springer Handbook of Geo Information, 2nd Ed. DOI: 10.1007/978-3-030-53124-9, in print). The metadata of a PID for data and software can also reference PIDs for the authors and others involved. As a result, it is now possible that once the Version DOI of a software release is cited, the involved persons can also be referenced using an individual PID, such as the Open Researcher and Contributor ID (ORCID), and receive measurable scientific credit for their effort. This allows that that collaboration efforts in FOSS software projects will become a measurable and rewarding part of the scientific track record. Furthermore, PID of software, data and other information sources can be related to each other by specifying related persistent identifiers in the metadata. This field is currently undergoing rapid development. A further step will be the linking of the now available concept and version DOI of the OSGeo projects with the PID of the OSGeo conference videos, which will improve the discoverability and re-use of the conference contributions.The OSGeo Foundation can be understood as a growing continuum of software projects, functionalities, groups of people, but also knowledge and information.

uk: вхід без авторизації

name: OSGeo, Persistent Identifiers and the shape of things to come


uk: Комп'ютерні науки

description: Producing and providing useful information for climate services requires vast volumes of data to come together that further requires technical standards. Beside ordinary base processes for climate data processing like polygon subsetting, there is the special case of extreme climate events and their impacts, where scientific methods for appropriate assessments, detection or even attribution are facing high complexity for the data processing workflows. Therefore the production of climate information services requires optimal science based technical systems, named in this paper climate resilience information systems (CRIS). CRIS like the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) are connected to distribute data archives, storing huge amounts of raw data themselves and containing processing services to transform the raw data into usable enhanced information about climate related topics. Ideally this climate information can be requested on demand and is then produced by the CRIS on request by the user. This kind of CRIS can be enhanced when scientific workflows for general climate assessment or even extreme events detection are optimized as information production service, accordingly deployed to be usable by extreme events experts to facilitate their work through a frontend. Deployment into federated data processing systems like CDS requires that scientific methods and their algorithms be wrapped up as technical services following standards of application programming interfaces (API) and, as good practice, even FAIR principles. FAIR principles means to be Findable within federated data distribution architectures, including public catalogs of well documented scientific analytical processes. Remote storage and computation resources should be operationally Accessible to all, including low bandwidth regions and closing digital gaps to ‘Leave No One Behind’. Aggreeing on standards for Data inputs, outputs, and processing API are the necessary conditions to ensure the system is Interoperable. Finally they should be built from Reusable building blocks that can be realized by modular architectures with swappable components, data provenance systems and rich metadata. General building blocks for climate resilience information systems A particular focus will be the "roocs" (Remote Operations on Climate Simulations) project, a set of tools and services to provide "data-aware" processing of ESGF (Earth System Grid Federation) and other standards-compliant climate datasets from modelling initiatives such as CMIP6 and CORDEX. One example is ‘Rook’ an implementation of the OGC Web Processing service (WPS) standard, that enables remote operations, such as spatio-temporal subsetting, on climate model data. It exposes all the operations available in the ‘daops’ library based on Xarray. Finch is a WPS-based service for remote climate index calculations, also used for the analytics of ClimateData.ca, that dynamically wraps Xclim, a Python-based high-performance distributed climate index library. Finch automatically builds catalogues of available climate indicators, fetches data using “lazy”-loading, and manages asynchronous requests with Gunicorn and Dask. Raven-WPS provides parallel web access to a dynamically-configurable ‘RAVEN’ hydrological modelling framework with numerous pre-configured hydrological models (GR4J-CN, HBV-EC, HMETS, MOHYSE) and terrain-based analyses. Coupling GeoServer-housed terrain datasets with climate datasets, RAVEN can perform analyses such as hydrological forecasting without requirements of local access to data, installation of binaries, or local computation. The EO Exploitation Platform Common Architecture (EOEPCA) describes an app-to-the-data paradigm where users select, deploy and run application workflows on remote platforms where the data resides. Following OGC Best Practices for EO Application Packages, Weaver executes workflows that chain together various applications and WPS inputs/outputs. It can also deploy near-to-data applications using Common Workflow Language (CWL) application definitions. Weaver was developed especially with climate services use cases in mind. Case of AI for extreme events investigations Here we present challenges and preliminary prototypes for services which are based on OGC API standards for processing and implementation of Artificial Intelligence (AI) solutions. We will presenting blueprints on how AI-based scientific workflows can be ingested into climate resilience information systems to enhance climate services related to extreme weather and impact events. The importance of API standards will be pointed out to ensure reliable data processing in federated spatial data infrastructures. Examples will be taken from the EU Horizon2020 Climate Intelligence project, where extreme events components could optionally be deployed in C3S. Within this project, appropriate technical services will be developed as building blocks ready to deploy into digital data infrastructures like C3S but also European Science Cloud, or the DIAS. This deployment flexibility results out of the standard compliance and FAIR principles. In particular, a service employing state-of-the-art deep learning based inpainting technology to reconstruct missing climate information of global temperature patterns will be developed. This OGC-standard based web processing service (WPS) will be used as a prototype and extended in the future to other climate variables. Developments focus on heatwaves and warm nights, extreme droughts, tropical cyclones and compound and concurrent events, including their impacts, whilst the concepts are targeting generalized opportunities to transfer any kind of scientific workflow to a technical service underpinning scientific climate service. The blueprints take into account how to chain the data processing from data search and fetch, event index definition and detection as well as identifying the drivers responsible for the intensity of the extreme event to construct storylines.

uk: вхід без авторизації

name: Deployment of AI-enhanced services in climate resilience information systems


uk: Комп'ютерні науки

description: Soil erosion is a major global land degradation threat. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity and climate change, is one of the most decisive factors when it comes to making decisions about conservation policies and for earth-system modelers seeking to reduce uncertainty on global predictions [1]. In this context, the use of remote-sensing based methods for soil erosion assessment has been increasing in recent years thanks to the availability of free access satellite data, and it has repeatedly proven to be successful [2, 3]. Accurate information about it is, however, usually known only at the local scale and based on limited field campaigns. Its application to the Arctic presents a number of challenges, due to peculiar soils with short growing periods, winter storms, wind, and frequent cloud and snow cover. However, the benefits of applying these techniques would be especially valuable in arctic areas, where ground local information can be hard to obtain due to hardly accessible roads and lands. Here we propose a hybrid solution, which uses ground truth samples to calibrate the processed remote images over a specific area, to then automate the analysis for larger, less accessible areas. This solution is being developed for soil erosion studies of Iceland specifically, using Sentinel 2 satellite data combined with local assessment data from Iceland’s Soil Conservation Services department, Landgræðslan. Their historical data is more extensive than usual, since they are the oldest soil erosion department in the world. Available data includes parameters of bare ground cover, which can be calculated from satellite images alone, after using information from observationally correct areas without vegetation for calibration; Icelandic soil profiles, to be analyzed to find how the profile relates to soil erosion intensity; as well as the parameters of agriculture use and arable land data including plant species in cultivated lands. For the training phase we employ a dataset composed of 550 cropped georeferenced and atmospherically corrected Sentinel 2A images [4], combined with a Digital Elevation Model (DEM) of Iceland that allows us to detect slopes which can produce landslides or help erosion to occur. The dataset is labelled by six degrees of erosion severity, using measurement points furnished by Landgræðslan. We split it into 2/3 for model training and 1/3 for model testing. These images are in tiles of 10980x10980 pixels (about 600 MB) and cover an area of approximately 100x100 km2. We can crop the images down to preferred size. They contain multispectral data, divided up into 12 bands of varying wavelengths, and a resolution from 10 to 20m. We could add as well some of the 60m bands if necessary. Different band data are combined to create indices which represent or highlight certain features, such as vegetation, soil crusting, bare soil, and red edge indices. Elevation data from the Arctic (north of 60°N, including Iceland) started to be openly available since 2015 through the ArcticDEM project. The DEMs are derived from satellite sub-meter stereo imagery, particularly from WorldView 1-3 and GeoEye-1. This information can be used to detect to what extent plant growth is reduced at higher heights because of longer snow cover, shorter growing period and stronger winds on one side. By using the variation of DEM and building a slope map, we can see that soil erodes more on steep slopes which leads to a higher likelihood of erosion the steeper they are. The tools for geometric and topographic correction include SNAP (Sentinel application platform), Sen2Core, FLAASH (Fast line-of-sight atmospheric analysis of hypercubes), DOS (Dark Object Subtraction) and ATCOR software. This correction reduces effects due to shadows and surface irregularities and corrects the single-date Sentinel-2 Level-1C Top Of Atmosphere (TOA) products from atmospheric effects in order to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product. After a preprocessing technique based on dimensionality reduction in order to avoid adding too much noise to the algorithm, this labelled data is then used to train a Support Vector Machine (SVM) model for classifying each coordinate. We choose the SVM algorithm as a starting point because it is a fast and reliable algorithm that performs well for classification problems with high-dimensional feature spaces such as ours, and does not require large training sets to achieve high accuracy as other algorithms do (e.g. deep neural networks). The output of the model is a set of coordinates, each with a numeric classification representing soil erosion severity, and used for creating a map of soil erosion severity in a selected area. This methodology has been proven to provide good results, achieving an overall land cover classification accuracy of 94% [5], a performance that can be attributed to the spectral complexity of Sentinel-2 data, particularly the red-edge bands which give room for separability of erosion classes. Low separability is a common limitation to the applicability of classification methods. We address this by using ISODATA and minimum distance methods. Two factors that could affect the accuracy of the delineation of eroded soils using spectral images are the intensity of the soil erosion processes and changes in the spectral characteristics of disturbed soils. The research described here aims at producing a reliable, widely applicable and cost-effective method to classify Icelandic soils into different categories of erosion risk, a proof of concept which, once engineered, could be straightforwardly expanded and applied to other Arctic areas, such as Greenland and Canada.

uk: вхід без авторизації

name: Remote mapping of soil erosion risk in Iceland


uk: Комп'ютерні науки

description: An approach for real-time validation of the location of biodiversity observations contributed in a citizen science project Motivation: Because of technological advancements, public participation in scientific projects, known as citizen science, has grown significantly in recent years (Schade and Tsinaraki 2016; Land-Zandstra et al. 2016). Contributors to citizen science projects are very diverse, coming from a variety of expertise, age groups, cultures, and so on, and thus the data contributed by them should be validated before being used in any scientific analysis. Experts typically validate data in citizen science, but this is a time-consuming process. One disadvantage of this is that volunteers will not receive feedback on their contributions and may become demotivated to continue contributing in the future. Therefore, a method for (semi)-automating validation of citizen science data is critical. One way that researchers are now focusing on is the use of machine learning (ML) algorithms to validate citizen science data. Methodology: We developed a citizen science project with the goal of collecting and automatically validating biodiversity observations while also providing participants with real-time feedback. We implemented the application with the Django framework and a PostgreSQL/PostGIS database for data preservation. In general, the focus of biodiversity citizen science applications is on automatically identifying or validating species images, with less emphasis on automatically validating the location of observations. Our application's focus, aside from image and date validation (Lotfian et al. July 15-20, 2019), is on automatically validating the location of biodiversity observations based on the environmental variables surrounding the observation point. In this project, we generated species distribution models using various machine learning algorithms (Random Forest, Balanced Random Forest, Deep Neural Network, and Naive Bayesian) and used the models to validate the location of a newly added observation. After comparing the performance of the various algorithms, we chose the one with the best performance to use in our real-time location validation application. We developed an API that validates new observations using the trained models of the chosen algorithm. The Flask framework was used to create the API. The API uses the location and species name as parameters to predict the likelihood of observing a species (for the time being, a bird species) in a given neighborhood. Moreover, the model prediction, as well as information on species habitat characteristics are then communicated to participants in the form of real-time feedback. The API has three endpoints: a POST request that takes the species name and location of observation and returns the model prediction for the probability of observing the species in a 1km neighborhood around the location of observation; a GET request that takes the location of observations and returns the top five species likely to be observed in a 1km neighborhood around the location of observation; and a GET request that returns the species common names in English. User experiment: A user experiment was carried out to investigate the impact of automatic feedback on simplifying the validation task and improving data quality, as well as the impact of real-time feedback on sustaining participation. Furthermore, a questionnaire was distributed to volunteers, who were asked about their feedback on the application interface as well as the impact of real-time feedback on their motivation to continue contributing to the application. Results: The results were divided into two parts: first, the performance of the machine learning algorithms and their comparison, and second, the results of testing the application through the user experiment. We used the AUC metric to compare the performance of the machine learning algorithms, and the results showed that while DNN had a higher median AUC (0.86) than the other three algorithms, DNN performance was very poor for some species (below 0.6). Balanced Random Forest (AUC median 0.82) performed relatively better for all species in comparison to the other three algorithms. Furthermore, for some species where the other three algorithms performed poorly (AUC less than 70%), Balanced-RF outperforms the others. The user experiment results provided us with preliminary findings that support the combination of citizen science and machine learning. According to the findings of the user experiment, participants with a higher number of contributions found real-time feedback to be more useful in learning about biodiversity and stated that it increased their motivation to contribute to the project. Besides that, as a result of automatic data validation, only 10% of observations were flagged for expert verification, resulting in a faster validation process and improved data quality by combining human and machine power. Why it should be considered: Data validation and long-term participation have always been two of the most difficult challenges in citizen science and VGI (volunteer geographic information) projects. Various studies have been conducted on biodiversity data validation, focusing primarily on observation images with automatic species identification; however, not enough attention has been paid to observation location validation, particularly automatic location validation taking into account species habitat characteristics. Furthermore, to the best of our knowledge, the combination of machine learning and citizen science for sustaining participation by providing real-time user-centered and machine generated feedback to participants has received, till now, little attention and therefore our work is new, original and completely coherent with the vision of community citizen science, where scientists and citizen scientists are supposed to learn from each other.

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description: The collection of georeferenced information on the field has become an established and popular practice allowing professionals, volunteers and citizens to contribute to mapping objects or reporting events. Field data collection is essential to a variety of domains [1] including many scientific and humanistic disciplines, humanitarian and rescues operations, locations reviews and professional engineering surveys, to mention a few. The spread of mobile devices that can record location coordinates, media and features while on the go (and share them through the web) is primarily accountable for such diffusion. As a result, a number of mobile apps and software frameworks (both proprietary as well as free and open-source) have been developed and released to perform data collection on the field. Most of these frameworks allow developers or data collection promoters to customize collection forms according to the characteristics of each collection task and manage both users and records through web dashboards or database management systems. From the user perspective, mobile client apps are available to access selectively the collection forms and contribute to the data collection on the field using mainly smartphones or tables. Focusing on general-purpose data collection software frameworks, some of the most popular free and open-source solutions are the Open Data Kit (ODK), the KoBoToolbox and Epicollect. Other relevant examples of free and open-source frameworks implementing a more technical approach to field data collection are e.g. QField, Geopaparazzi and SMASH. Proprietary or pay-per-use solutions developed by major GIS firms are also available on the market but they were not considered in the benchmark analysis carried out in this work. The outlined free and open-source software frameworks provide client and server modules and both web and mobile apps to support the full development of field data collection projects [2]. From the developer (or data collection promoter) perspective, the adoption of such frameworks is facilitated by the availability of open APIs, interfaces and dashboards to generate, deploy and manage collection forms, users and records. Nevertheless, limitations connected to the final user experience are common to most of them. On one hand, mobile client apps are not always available or optimized for all mobile OS, therefore preventing their use on the field by a significant number of potential contributors [3]. This is the case e.g. of the ODK on iOS devices. On the other hand, each of these frameworks requires the installation of a specific mobile app on the user's device. This operation may not represent a significant obstacle to the contribution to specific data collection projects by very active or committed users. However, it might inhibit the contribution of occasional users who may not be willing to install additional software on their device for sporadic mapping of objects or event reporting [4]. In view of the above, this work presents the Geo Collector Bot, an alternative free and open-source software toolkit to empower field data collection projects avoiding the development and/or the installation of a specific mobile app on contributors' devices. The Geo Collector Bot is a configurable Telegram-based chatbot enabling the dispatching of data collection forms that can be activated and filled through Telegram chats. It consists of a backend application written in Typescript and running on Node.js. As the supporting mobile client, the Telegram app is exploited thus enabling a large number of users to contribute, even sporadically, to data collection projects (potentially every Telegram user; 550 million monthly active users as of July 2021). The Geo Collector Bot is released under MIT License and source code, documentation and a demo are available on GitLab (geolab.como/geocollectorbot). The Geo Collector Bot works as a standard Telegram Bot. To collect the data, the Bot asks a series of questions to the user including location coordinates, media, textual annotations, multiple-choice checkboxes, etc. and persists the answers to a spatial database. The questions flow can be customized by editing a JSON configuration file. Local deployments of the system are facilitated by the provision of a Docker container (hub.docker/r/geolabpolimi/geo-collector-bot). The Geo Collector Bot has been developed in the framework of the INSUBRI.PARKS project (insubriparksturismo.eu), funded by the Interreg Co-operation Programme 2014-2020. This project aims at increasing the tourism attractiveness of the Insubria region (between Northern Italy and Southern Switzerland) through the provision of infrastructure as well as integrated marketing and management strategies for the Insubria natural parks. The Bot represents a component of the virtual infrastructure supporting the project. It was originally designed to allow both parks visitors and managers to easily collect and share geolocated records on parks status and feedback on points of interest. However, the ultimate goal of the presented work is to provide an open and general-purpose data collection software framework suitable for multi-purpose applications. The current version of the Geo Collector Bot still does not provide dedicated backend supporting modules for both collection tasks and records management. To that end, the development of a web control dashboard is planned and it will be included in the stack of the Geo Collector Bot Docker container as an auxiliary component. The Bot has been tested using a PostgreSQL-PostGIS database. Additional configuration options to plug other spatial database systems is planned as well in the future development of this work.

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name: Geo Collector Bot: A Telegram-based open toolkit to support field data collection


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description: Analysis of the spatiotemporal accumulation process of Mapillary data and its relationship with OSM road data: A case study in Japan Japan's open infrastructure map development using OpenStreetMap was triggered by the Great East Japan Earthquake in 2011, which led to a widespread understanding of the activity, and by the end of September 2019, more than 35,000 unique users had made some kind of contribution, and the data is still being updated daily. The data is still being updated daily. In addition, the Mapillary project (Juhász and Hochmair, 2016; Mahabir et al., 2020) which started in April 2014, is a location-based landscape photo-sharing service that, like OSM, is crowdsourced and allows users to post photos of places around the world, not just on roads. This activity has started to spread in Asia, especially in Japan, where the number of contributors and the number of photos taken is rapidly increasing (Ma et al., 2020). These voluntary crowdsourcing activities are a great incentive to work on the creation of micro-scale road data, especially those that cannot be maintained or updated by public agencies. On the other hand, most of the research on Mapillary to date has been concerned with technical methodologies, such as the study of ground object extraction based on deep learning of images using Mapillary data, and approaches such as local comparison of data generated by contributors, as is commonly done in OSM research, have not made much progress. This study was conducted in September 2014. In this study, we obtained about 41.7 million log data through the Search Images API of Mapillary API ver3 taken in Japan from September 2014 to September 2019. Then, together with the line data of OSM roads at the same point in time, the maintenance status of Mapillary and OSM road data in municipal units in Japan was spatially analyzed mainly with QGIS, considering the time series and user trends. The data for the entire country of Japan to be analyzed is so huge that it is difficult to perform spatial analysis with the basic database (PostGIS), so we tried to add various attributes that can be analyzed spatially in QGIS by converting the data to FlatGeobuf format, which has been attracting attention recently. We also tried to add various attributes that can be analyzed spatially in QGIS. The added attributes include the administrative name of the local government in Japan, and the type, version, last editor, and date of data update of the road in the nearest vicinity of the taking photo point (maximum search radius set to 50m) from the OSM dump file obtained separately. Some of the results of the analysis are as follows. The number of unique contributors who participated in the maintenance of Mapillary data for five years across Japan was about 1500, and it was found that the top 20 users generated about 90% of the data. The top three contributors each shared more than 5 million images. The number of contributors involved in the OSM road data as a comparison of user participation is about 4,800, suggesting that Mapillary data is generated by about 1/3 of the users compared to OSM. We extracted the major contributors for each of the 1,700 municipalities in Japan and found that about 50 users were involved. Although the Mapillary data in Japan is supported by a smaller number of contributors than the OSM data, we succeeded in bringing to light the image of contributors in each region by analyzing the data on a micro-regional basis. In terms of the number of Mapillary images taken and their spatial characteristics, the number of images taken on major roads (equivalent to OSM's highway = trunk or primary) in non-urban areas in the Tohoku region (especially Fukushima Prefecture: approximately 6 million images, Iwate Prefecture: about 4 million images) and Kansai region (Kyoto Prefecture: about 3 million images) is outstanding, while the number of images taken on sidewalks (highway = sidewalk) in the metropolitan areas of Tokyo and Osaka is low. In the metropolitan areas of Tokyo and Osaka, the data developed to supplement the OSM data for sidewalks (highway=path, footway, unclassified) and other road types that exist in reality but are not well-developed in the OSM data. In the paper, we plan to describe the local activities in Fukushima Prefecture and Kyoto City, where Mapillary activities are particularly active, in addition to comparisons at the national and municipal levels. In this paper, we also focus on the temporal transition of data maintenance. In this paper, we also focus on the temporal transition of data maintenance. Specifically, we analyzed the relationship between OSM data and the points where Mapillary images were taken using time series clustering. This study is a multifaceted spatial analysis of long-term photography logs through Mapillary and the first study to reveal macro trends across Japan as well as more local trends in combination with attributes of road data from municipalities and OSM. In addition, by using distributed processing methods such as tiling technology and FlatGeobuf to obtain a large dataset of more than 41 million POIs (Points of Interests) from APIs and analyze the data spatially in QGIS, we were able to process the data without requiring a large-scale server. This is also a significant achievement. Finally, since the Mapillary log data used for the analysis is large-scale, we are planning to provide both archived data and spatially aggregated GIS data.

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description: An open-source-based workflow for DEM generation from Sentinel-1 for landslide volume estimation Digital elevation models (DEMs) are a representation of the topography of the Earth, stored as elevation values in regular raster grid cells. These data serve as basis for various geomorphological applications, for example, for landslide volume estimation. Access to timely, accurate and comprehensive information is crucial for landslide analysis, characterisation and for understanding (post-failure) behaviours. This information can subsequently be used to effectively assess and manage potential cascading hazards and risks, such as landslide dam outburst floods or debris flows. Freely available DEM data has been an important asset for landslide volume estimation. Earth observation (EO) techniques, such as DEM differencing, can be leveraged for volume estimation. However, their applicability is reduced by high costs for commercial DEM products, limited temporal and spatial coverage and resolution, or insufficient accuracy. Sentinel-1 synthetic aperture radar (SAR) data from the European Union's Earth observation programme Copernicus opens the opportunity to leverage free SAR data to generate on-demand multi-temporal topographic datasets. Sentinel-1 A & B data provide a new opportunity to tackle some of the problems related to data costs and spatio-temporal availability. Moreover, the European Space Agency (ESA) guarantees the continuity of the Sentinel-1 mission with the planned launch of another two satellites, i.e., Sentinel-1 C & D. Interferometric SAR (InSAR) approaches based on Sentinel-1 have often been used to detect surface deformation; however, few studies have addressed DEM generation (Braun, 2021). For example, Dabiri et al. (2020) tested Sentinel-1 for landslide volume estimation, but highlighted the need to further research and systematically assess the accuracy of the generated DEMs. InSAR analysis is often conducted using commercial software; however, a well-structured workflow based on free and open-source software (FOSS) increases the applicability and transferability of the DEM generation method. Although a general workflow for DEM generation from Sentinel-1 imagery based on InSAR has been described and documented (ASF DAAC, 2019; Braun, 2020, 2021), there is still a need for improvement, harmonisation and automation of the required steps based on open-source tools. Within the project SliDEM (Assessing the suitability of DEMs derived from Sentinel-1 for landslide volume estimation), we explore the potential of Sentinel-1 for the generation of multi-temporal DEMs for landslide assessment leveraging FOSS. Relying on the open-source Sentinel Application Platform (SNAP) developed by the ESA, the Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping (SNAPHU) developed by Stanford University, and several other open-source software publicly available for geospatial and geomorphological applications, we work on a semi-automated and transferable workflow bundled in an open-source Python package that is currently under active development. The workflow uses available Python SNAP application programming interfaces (APIs), such as snappy and snapista. We distribute the SliDEM package within a Docker container, which allows its usage along with all its software dependencies in a structured and straightforward way, reducing usability problems related to software versioning and different operating systems. The final package will be released under an open-source license on a public GitHub repository. The package consists of different modules to 1) query Sentinel-1 image pairs based on perpendicular and temporal baseline thresholds that also match a given geographical and temporal extent; 2) download and archive suitable Sentinel-1 image pairs; 3) produce DEMs using InSAR techniques and perform necessary post-processing such as terrain correction and co-registration; 4) perform DEM differencing of pre- and post-event DEMs to quantify landslide volumes; and 5) assess the accuracy and validate the generated DEMs and volume estimates against reference data. The core module focusses on DEM generation from Sentinel-1 using InSAR techniques available in SNAP. The script co-registers and debursts Sentinel-1 image pairs before generating and filtering an interferogram. Phase unwrapping is performed using SNAPHU. The unwrapped phase is then converted into elevation values, which are finally geometrically corrected and co-registered to a reference DEM. Co-registration is based on assessing the normalised elevation biases over stable terrain (after Nuth and Kääb, 2011). We assess errors and uncertainties for each step and the quality of the Sentinel-1 derived DEMs using reference data and statistical approaches. The semi-automated workflow allows for the generation of DEMs in an iterative and structured manner, where a systematic evaluation of the resulting DEM quality can be performed by testing the influence of different temporal and perpendicular baselines, the usage of ascending and descending passes, distinct land use/land cover and topography, among other factors. Several major landslides in Austria and Norway have been selected to evaluate and validate the workflow in terms of reliability, performance, reproducibility, and transferability. The SliDEM workflow represents an important contribution to the field of natural hazard research by developing an open-source, low-cost, transferable, and semi-automated method for DEM generation and landslide volume estimation. From a practical perspective, disaster risk management can benefit from efficient methods that deliver added-value information. From a technical point of view, SliDEM tackles scientific questions on the validity of EO-based methods and the quality of results related to the assessment of geomorphological characteristics of landslides.

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description: How to grow? -Modeling land use change to develop sustainable pathways for settlement growth in the hinterland of Cologne, Germany Urban sprawl is associated with negative environmental impacts such as the loss of habitat and the loss of most fertile soils for agriculture. The hinterland of Cologne, Germany is facing these challenges. The area is expected to face a population increase by 200,000 inhabitants in the next twenty years. Given past development trends, this population increase will have to be mainly absorbed by the cities and villages in the hinterland. While this provides ample economic opportunities, negative impacts on ecosystems as well as on agriculture have to be assumed due to urban sprawl and increasing fragmentation. The region is known as as one of the most productive agricultural regions in Central Europe. As highest fertile soils are located in the direct neighborhood of existing settlements, urban sprawl will lead to strong trade-offs with agricultural production. The aim of the scientific project NACHWUCHS is to identify alternatives to the continuation of existing development patterns. Therefore, we developed a baseline land use model and compare it to scenarios that assume different brownfield development activities. Stakeholder involvement is at the core of the project, as policies for alternative pathways cannot be successfully implemented without the support by farmers, real estate companies, environmental stakeholder , the municipalities and the district administration. The most important aspect of land use change in the region is the allocation of new housing areas. This is modeled by a tool-chain based on a free software stack, that uses PostgresSQL with a Postgis extention, Python and QGIS. The allocation model for new housing areas is currently based on a random forest classifier that has been trained on the official governmental ATKIS vector land use data set. The predictors of the model included distance to public transport and social infrastructure as well as existing land use development plans. The allocation of new housing areas was limited to areas outside of protected areas. Furthermore, only a few land use classes – mainly agriculture – were allowed for the allocation of new housing areas. The distance-based predictors were calculated by the openrouteservice, which uses OpenStreetMap data to build the routing graph and to assign routing weights. A 100 by 100m vector grid was used for model training and prediction. Model performance was evaluated based on a split in test and training data that considered spatial relationships. Based on the suitability of the grid cells the demand for projected new housing areas was allocated. We used nine scenarios that differed in the building density for new housing areas as well as by the extent of brownfield development . In the study presented, building density is expressed in residential units per hectare. Residential units per hectare is simplified as the number of flats in a building. In the simulated scenarios, three density classes (10, 30 and 50 residential units per hectare) and three different proportions of brownfield development (10, 20 and 40 per cent) were combined. In the simulated period from 2018 to 2040, we had an area increase of more than fifty percent between the scenario with the lowest density and the lowest proportion of brownfield development and the scenario with the highest density and the highest proportion of brownfield development . The results of the allocation procedure was evaluated based on a set of indicators which cover environmental, agricultural and social aspects. Examples are the supply of agriculture related ecosystem services, soil fertility, economic value of agricultural production and hemeroby.We used the Open Data of the State of North Rhine-Westphalia, which contained geodata for the relevant domains economy, environment and nature conservation, agriculture, social affairs and transport. The data are Inspire-compliant and available under a free licence (DL-DE-＞Zero-2.0) . The data set further allowed the evaluation of the model results with regard to the consequences of the flood disaster of the 14th July 2021, which severely affected parts of the hinterland of Cologne. Our results will be used in the context of a mission statement for the future regional development, developed together with locals stakeholders. The mission statement defined development goals for four sub-regions derived by socio-economic and environmental properties based on 17 UN SDGs. With the help of the above-mentioned indicators, we will evaluate how close or how far the results of the different scenarios are to these goals and assist local stakeholders, e.g. in the search for locations of new residential areas. A transfer of the model to regions with similar settings is possible as long as suitable data is available for retraining the model and for the estimation of the indicator sets, highlighting again the importance of open data. The ATKIS data used is openly available for some of the federal states of Germany but not beyond. For North-Rhine Westphalia a transfer semms reasonable- Test runs based on the CORINE land use / land cover product lead to comparable results, indicating that this might be a suitable replacement for the ATKIS based land use information.The Python code of the model, the necessary scripts to generate the required postgisdatabase, a QGIS project example for the visualisation of the results as well as a set of training and test data are provided under free licence via a Gitlab repository.

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description: Traffic accidents are a significant problem facing the world, as they result in many deaths and injuries every year. Generally, the probability of traffic accidents occurring at any point is not random. Factors such as the condition of the road, where the accidents occurred, and the general structure of the land play an essential role in the accidents that will occur at one point. For this reason, traffic accidents tend to occur intensively in areas where these factors are different from usual. It is critical to identify such areas and take the necessary measures to ensure road safety and reduce traffic accidents. Identifying the different geographic locations where traffic accidents occur can help prevent more traffic accidents, personal injuries, and fatal accidents and understand the different accident occurrence conditions. When the literature is considered, it is seen that many studies in this field are handled with different methods. Analyzing the locations where traffic accidents occur by considering the hot spots with spatial clustering methods plays a very active role in examining the tendency of traffic accidents to occur. In this study, it is thought to deal with detecting traffic accident hot spots by using the GIS-based Nearest Neighbor Hierarchical Clustering Method (NNH) and Density-based clustering Method (DBSCAN). Nearest Neighbor Hierarchical Clustering Method (NNH) is a hot spot spatial clustering method that detects accident hot spots. This method considers two types of criteria for spatial mapping clustering of spatial point data: the threshold distance (d), which is the Euclidean distance between each pair of data points, and the minimum number of points that must be present in a cluster (nmin) (Kundakci E, 2014; Kundakci and Tuydes-Yaman, 2014; Levine, 1996; Levine et al., 2004; Ture Kibar and Tuydes-Yaman, 2020). At the point of realizing this method, the crime stat program, which was developed especially for hot spot clustering analysis of crimes, is widely used. CrimeStat is a crime mapping software program developed by Ned Levine (Levine, 1996). Density-based clustering, on the other hand, is also known as DBSCAN, is a method for finding specific predefined events and hotspots. The algorithm, moreover, is open source and recommended for noisy data in large spatial databases (Ester et al., 1996). This method identifies a cluster as the most densely connected set of points possible. There are two criteria addressed in this method: Epsilon and minimum scores. The maximal radius of the neighbourhood is epsilon, and the minimal number of points in the epsilon-neighbourhood to describe a cluster is minimum points. This clustering algorithm separates the point data into three different forms (Schubert et al., 2017). In the study, the Mersin province of Turkey was chosen as the pilot region for the analyses using the mentioned methods. Mersin is a port city located in the Mediterranean Region of Turkey, located between 36-37° north latitude and 33-35° east longitude. As of 2021, it has a population of 1.891.145 (URL-1, 2022). It is the most important domestic tourism center of Turkey and is on the way to becoming Turkey's new tourism region with the appointments made in tourism in recent years and new hotels built on the beach. This study predicted determining the risky areas where speed-related traffic accidents will occur in Mersin, which is an important point for the country, and to make predictions by making evaluations depending on the road geometry at the determined points. In addition, it will be examined whether the measures to be taken based on the analysis at the determined points are made comparatively with two different methods and whether these evaluations create differences by considering both based on a large region and the basis of a more local region. The study was planned in four phases. First of all, spatial and non-spatial data of the selected pilot region will be provided. For this stage, traffic accidents data between 2013-2020 will be obtained from the general directorate of safety and the general command of the gendarmerie. The obtained data will be organized and then transferred to the geographic database for GIS-based analyses in the second stage. Since speed-related traffic accident hot spot analysis will be performed in the study, the database will be suitable to include speed-related accidents. The NNH and the DBSCAN method will be performed in the third stage, and the results will be discussed. At this stage, the Crime Stat III program will be used for the NNH method, and the open-source GIS program QGIS will be used for the DBSCAN method. All results will be analyzed, visualized, and evaluated through the QGIS program. In the last stage of the study, the results obtained will be examined according to the probability of accidents. Finally, the obtained risky areas according to the analysis results will be evaluated according to the geometry of the road. In short, it will be examined within the framework of accident-road geometry whether the structure of the road and the high-risk areas of the accidents overlap. The fact that the points where speed-related accidents will tend to cluster will be determined, with the study to be carried out, will address a significant gap in this field. Since the effectiveness of the methods will be compared with a different analysis, a study will be constituted a base for studies in a similar field. In addition, since the reasons such as whether these methods produce effective results in large regions and more local regions will be examined, it is thought that important suggestions will be made and contributions to the literature. Finally, since the results obtained in the study will be evaluated depending on the road geometry, the traffic accident-road geometry relationship will be discussed. Thus, a base for similar studies will be provided.

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name: Speed-related traffic accident analysis using GIS-based DBSCAN and NNH clustering


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description: OGC API State of Play - A practical testbed for the National Spatial Data Infrastructure in Switzerland # Context and purpose OGC standards shape a backbone within the OSGeo community in defining a pathway to software implementation toward the standardization of geospatial information and related services ensuring interoperability between FOSS4G software. Since 2016, the OGC has initiated the specification of a new generation of standards based on the OpenAPI so as to facilitate integration in modern web applications and systems. Underpinning the OGC API roadmap, the development of all these standards represents a significant amount of activities carried out by various OGC working groups, testbeds and pilots from the OGC Innovation Program. Some standards have been approved, many are still under development and it is therefore not always easy to follow the progress. Indeed, while some geodata infrastructures involving national entities are already deploying this new generation (e.g. Canada MSC GeoMet), some initiatives run a phase of experimentation (e.g. Geonovum Testbed Platform for the Dutch geoportal). From a practical perspective, how can organizations and institutions anticipate to leverage this new generation of standards to deploy a geospatial data infrastructure? This issue is what this article is about, introducing a project that seeks to address it by running an OGC API testbed platform with a special focus to the Swiss context. This project is embedded in the Resources for the NSDI Program (related to the Swiss Geoinformation Strategy) with the purpose to contribute to the upcoming revision of e-government standards regarding geoinformation (e.g. eCH-0056 Geoservices application profile). The project is about a study jointly carried out by swisstopo and complementary academic partners (HEIG-VD, SUPSI, UNIGE). # Approach As a result of the above mentioned complexity and overlapping of existing standards, the project team has applied a benchmark study approach, where different standards are tested in experimental cases and evaluated in comparison of other existing solutions. The outcomes include both quantitative and qualitative results that will be condensed in practical recommendations for implementation and adoption of the OGC API family. This research aims at evaluating a selection of different OGC specifications as well as different server and client implementations in order to define e-government recommendations to promote collaboration between authorities, companies and individuals. The selected mainstream topic for the experimental cases is about climate change. While not yet connected in a complex pilot study, each case represents one of the required components: from sensing (remote/in-situ) to data visualization and exploration, through data offering and elaboration. The study is organized in three parts: - The hydro-meteorological monitoring network of the Canton Ticino, which is currently managed using the SOS standard, has been selected as representative of a practical implementation of basic data required for the climate change impact assessment pipeline. The network, which has a 40 years long time-series, is currently composed of 60 stations and 140 sensors observing precipitation, air temperature and humidity, water temperature, river height. Collected information is operationally used by the local administration to design and actuate water resources protection and allocation to guarantee a sustainable management of the resource and the natural environment while protecting from the impacts of extreme events like floods and droughts. The Sensor Things API operational applicability is evaluated by testing this standard to fulfil all the major in place daily practical operations like for example data quality management, data sharing with third parties, data collection from vendor specific sensors and data analyses and visualization. - Switzerland was the second country in the World after Australia to have an operational satellite Earth Observations (EO) Data Cube. The Swiss Data Cube (SDC) is a tera-scale analytical cloud-computing platform allowing users the access, analysis and visualize up to 38 years (1984-2022) of consistent calibrated and spatially co-registered optical and radar Analysis Ready Data. The SDC leverages the information power of Big Earth Data for monitoring the environment by minimizing time and knowledge required for analysing large volumes of raster data. The derived analytical products provide an effective means to build socially robust, replicable, and reusable knowledge, to generate ready-to-use products supporting evidence-based decisions. Currently, all the data products and their related description (i.e. metadata) are accessible through “traditional” OGC services such as WMS, WCS, CSW. For example, the Normalized Difference Water Index (NDWI) time-series can be used to estimate and monitor the evolution of vegetation water content over the entire country. The aim of the experimental case will be to use a set of new OGC APIs implemented on top of the Swiss Data Cube to track the evolution of NDWI. To reach this objective we will implement the OGC API Coverages, Processes, EDR, Records, STAC APIs to access NDWI raster data time-series and compute zonal statistics using different administrative units/levels (e.g. national, canton). Given past activities of the team project related to portrayal interoperability with OGC standards like WMS, WMTS, SLD/SE, this part aims to challenge a set of specifications of the OGC API, especially Features, Tiles, Maps, Styles and to provide insights about OGC SymCore. At one side, the experimental case will consider outputs from running Geoclimate, an open source geospatial toolbox to compute a set of urban climate related parameters describing a given area of study using OpenStreetMap data as a base. The intent is to make these indicators discoverable and to serve them as data and maps through the OGC API. At the other side, the aim is to address national needs for geodata visualization using the Minimum Geodata Models (MGDM) in conjunction with their styling models, testing how the symbol description may be encoded in a standard way with modern formats and techniques to build styles and symbology (i.e. from SLD/SE to GeoCSS with or without cascading, etc). For all these experimental cases, FOSS4G are deployed, especially at the server level with FROST, pygeoapi, Geoserver, QGIS Server. The results are useful for developers, government agencies and organizations who want to implement and use the new family of OGC standards.

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description: Making Sense of the Noise: Integrating Multiple Analyses for Stop and Trip Classification ### Motivation & Contribution Mobility researchers using GPS first obtain raw coordinates and timestamps from GPS instead of the variables they're interested in. Conversion is needed to acquire, for example, the time spent out of home, the number of revisited places, or the total time spent on the go. All of these rely on the ability to precisely identify stops and trips and are therefore fundamental when it comes to mobility research. The commonly adopted strategy involves a combination of a distance and a time threshold to identify significant places (Ash- brook and Starner, 2002, Ye et al., 2009). Here, GPS records are grouped together, if they lie within such a pre-defined radius and time. When we planned the technical basis for a mobility intervention study, we tested several existing systems based on this approach. We observed, on the one hand, significant segmentation of the identified stops, due to the relatively large amount of signal noise. On the other hand, we could only identify stops having a duration greater than a pre-defined time threshold, usually five minutes. Hence, the temporal resolution of this analysis was sub-optimal. Reduced this threshold, lead to an increased number of falsely identified stops (false positives) and segmentation. To solve this, we developed a modern stop and trip identification algorithm. For a human annotator, this task is fairly easy: when dwelling on a spot, the GPS records scatter around the true position because of its imperfect signal. Records obtained from a trajectory through an environment are clearly distinguishable - although the imperfect signal diverges from the true position similarly. This observation inspired us to create a new algorithm around the idea of investigating the signal patterns, and therefore the geometric properties of the signal noise. We describe the algorithm's mechanics in detail and discuss its design decisions. Further, we provide benchmark results against established and frequently cited libraries. ### Approach Fundamentally, the algorithm is based on a multitude of different, geometric analyses. Each analysis method is applied to a rolling window of subsequent GPS samples. For example, one metric evaluates the ratio between total path length and the bounding box of the set. Another is concerned with the mean angles between the point vectors. Subsequently, all metrics are combined to form a majority-based classification decision for each individual GPS sample. This way, the different methods can compensate for a wrong decision of a minority of the metrics. If available, the acceleration of the device is also taken into account to exclude unambiguous periods of non-movement. Therefore, we created a simple metric that transforms a three-dimensional vector of x, y, and z acceleration into a motion score that expresses the amount of physical movement of the recording device. The labels of individual GPS samples are then used to aggregate stop intervals. In the last step, the resulting stop intervals are filtered. Therefore, each interval is compared against the neighboring ones to decide if a) it should be kept as it is, b) if it should be merged with a close stop-interval to reduce segmentation, or c) if it should be discarded. ### Validation To test the accuracy of our analysis approach, we benchmarked the system against the built-in methods for stop and trip detection of Moving Pandas (Graser, 2019) and Scikit Mobility (Pap- palardo et al., 2019). These represent a large share of the most commonly used tools for mobility research. To test the classification performance, we created a large dataset containing trajectories from over 126 days of everyday life and captured 692 stops. This reference acts as ground truth for the comparison of different frameworks. We investigate sample-by-sample classification metrics (accuracy, precision, recall/sensitivity, specificity, and F1) and stop/trip interval specific metrics (stop-counts, several metrics to quantify the number of detected stops against the reference, such as % matched reference stops, absolute duration error, missed stop duration, absolute start deviation, absolute end deviation, and position deviation). To ensure a fair comparison of the algorithmic approaches, we did not take the acceleration data into account, as the reference systems do not support filtering stop and trip intervals using this kind of data. ### Results & Discussion Our Stop & Go Classifier outperforms other systems in most metrics: it identifies more stops correctly, the stops it misses are shorter in duration, and the start and end times of the identified stops are almost twice as precise as the closest competitor. The core ideas of the system are a) it uses unfiltered, raw GPS data, b) it analyzes these regarding their geometric properties, and c) it uses multiple scoring mechanisms to create one solid classification.

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description: Automatic assessment of lake health status using an open source approach: Lugano lake case study Lakes are a fundamental resource with a number of environmental benefits and with a not negligible influence on the local economy and on the quality of life. They work as a storage of water when floods or droughts occur, in the first case, they are useful to laminate the excessed flux of water, in the second as water supply during shortages. In addition, they influence the filling of groundwater and they play a role in the preservation of the general habitat biodiversity. From an economical point of view, they are an attraction for tourism, residential living as well as a source of recreation and of work for fishers. Unfortunately, climate changes together with human activities are more and more threatening such resources modifying the known dynamics and affecting the general health status of lakes (Fenocchi et al. 2018; Free et al. 2021; Lepori et al. 2018). In this context, the INTERREG project SIMILE (System for the Integrated Monitoring of Insubric Lakes and their Ecosystems), born from the collaboration between Italy and Switzerland, aim at developing an information system using an open source approach and based on innovative technologies to help decision maker in the management and evaluation of the status of the transboundary and sub-alpines lakes such as Lake Maggiore, Lugano and Como. The SIMILE project wants to intensify the monitoring of these lakes by creating an open real-time monitoring system and by integrating data coming from different sources in order to create the possibility to fully exploit the potential with the heterogeneity of the available information and better studying the resource. The work presented in this paper is focused on the achievements reached by the research carried out on lake Lugano in the context of the SIMILE project after two years of work. In particular, the presented research is oriented on the automatic generation of some indicators that are usually calculated to evaluate the lake status through the use of open standard, software and hardware. Lake Lugano is a transboundary lake divided in two main watersheds, North and South, respectively with an area of 27.5 Km2 and 21.4 Km2 and a maximum depth of 288 and 89 m. It is a eutrophic lake which has a critical health status in particular during the 70s, but thanks to new regulations and to the mitigation actions studied by the Swiss administration it is recovering. One of the fixed targets is to reach 150 gC/y which corresponds to a mesotrophic status. This value gives information about the metabolism activity of the lake and can be calculated using different approaches. At this moment, on lake Lugano, to get such information monthly campaign according to the Nielsen method (Nielsen, 1952). This approach is the one recognized by the administration and it is conducted by specialist limnologists. However, it has some issues that can be synthetized in three points: 1) it needs the use of radioactive components; 2) it is quite expensive in terms of man hour and the engagement of an external laboratory to analyze such kind of special samples; 3) since it has a monthly temporal resolution it needs mathematical model to interpolate data between the different campaigns. According to this overview, the proposed paper wants to investigate a fully open web solution in order to calculate indicators that can help in understanding the health status of the lake and try to solve the individuated limits that are currently affecting the water monitoring. Such an open platform uses open standards as the Sensor Observation Service (SOS) of the Open Geospatial Consortium (OGC) to integrate different sources of data and to offer the possibility to gather the information in a standardized way. Thanks to this achievement, it was possible to develop. The scope is to standardize the calculations and provide a solution where indicators can be calculated automatically saving also time since the traditional process. Potentially such an approach could calculate in real-time the indicators thanks to the use of the LISTEN/NOTIFY feature which exists in PostgreSQL, the database technology on which the platform is based. Finally, in this paper is presented the preliminary results of the development of a new algorithm to calculate the lake metabolism which can, if validated, offer a new approach that can solve the individual issue of the current one. Basically, the developed open monitoring system implemented and deployed on the lake offers real-time data The platform is composed of dockerized and specialized services in order to offer a suite that is easily replicable, scalable and upgradeable. In conclusion, an overview of the results reached during these years of project is presented. Such a solution increases the replicability of the system since it is fully open and guarantees the openness of data, source code, standards and also the hardware part. Such technologies help in developing an automatic system that can calculate indicators to help decision makers in managing the water resource and scientists to better study the new unknown dynamic and facing the new challenges to which lakes are exposed.

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description: Context It is said that data visualization is as important as the data itself. As the amount of data generated from Earth observation (EO) satellites – i.e. Copernicus program (Jutz and Milagro-Pérez, 2020) – is getting bigger and bigger, we need more efﬁcient tools to deal with this onslaught of data. To help data scientists better extract relevant information from datacubes, we noticed that an under-exploited computer graphics tools could bring new perspectives to specialists. Datacubes are known to be the reference format to handle EO data; several techniques such as Web WorldWind developed by NASA exist to process and interact with them. Recent works have shown focus on the preparation of largescale geospatial data (Mazroob Semnani et al., 2020), a highly technical subject, could beneﬁt from optimizations. QGIS is another tool frequently used in the field, that can be enhanced by plugins and can retrieve data from Web platforms. A modern approach to process efficiency is the use of GPUs. Still, when reviewing the use of GPUs to process geospatial data, the emphasis is often put on the parallel processing of geospatial datasets rather than focusing on their visualization (Saupi Teri et al., 2022). Objectives One of the main contributions of this paper is to consider geospatial data using GPU resources for intermediate computation and visualization. Considering the increasing interest to interact with this data directly using Web pages or Notebooks, this article presents tools allowing a program to run on the GPU and display the desired datacubes using the WebGL API. This can result in high performances thanks to its low-level control and possibility to use GPGPU algorithms. WebGL running natively on most web browsers, another beneﬁt will be the end-user ease of use. The end goal is to display even large (i.e. 1024^3) datacubes rendered on the ﬂy in real time on a PC, still well-equipped. Methodology To keep our applied research efforts focused, we have set up an independent international expert advisory group. Indeed, we wanted above all to provide something useful and concrete for the actors in the field. The represented institutes are ESA (EC), EURAC (Italy), GISAT (Czech Republic), Terrasigna (Romany), TU Wien (Austria), VITO (Belgium), and even a former NASA (USA) analyst. They have been regularly interviewed to get constant feedback on the suitability of our developed application, the ﬁnal goal of our project being to build a toolbox of models to efﬁciently visualize different EO datacubes formats. Models This paper presents three main models applicable to datacubes from an EO context, some relatively standard and others innovative, still all revisited via the GPGPU architecture. Implicit curves model – This model has two main approaches: discrete and math-based sub-models. Especially adequate to process (x, y) or (x, y, t) datacubes in a 2D or 3D visualization we developed and compared both sub-models with their dependencies. Sets of given iso and δ values are extracted from the data and stored as sets of curves. They can be displayed in a 2D environment or in 3D with additional information such as: (1) the simulation of the data as a 3D surface; (2) different colormaps for the surface representing yet other external data; (3) surface render in steps to emphasize the given iso and δ values; (4) user customizable colormaps; and (5), a water level simulation rendering. Derivative 3D rendering model – This model is specialized in analyzing (x, y, t) datacubes as a volume where the time t is part of the visualization. Indeed, the aim is to visualize the evolution in time of a geographical area by highlighting the temporal differences within a volume. After selecting the (x, y) region of interest the user selects a reference layer representing the state of an area at a deﬁned time t and a time interval Δt. The cumulated differences between the two are visible in a colored sub-volume deﬁned by the time interval. In order to add more contextual information in the visualized geographical area, we have added the possibility to display an additional map (such as topographic data) at the reference layer level within the volume. Jupyter Notebook massive rendering model – To make the toolset even easier to use, we have developed a visualization model deployable in Jupyter. This model allows rendering of (x,y,z) and (x,y,z,t) data volumes. Two rendering algorithms are already available: (1) the implicit surface simulation for any iso intensity -- but only via the discrete approach -- and (2), an XRay-cast simulation. Discussion Results show our models can process large amounts of data and render them in real-time. Where large 3D datasets would normally become problematic to handle for any GPU, we developed specialized tools to overcome software and hardware limitations. For instance, a 3D datacube can be sorted into a 2D texture to be directly loaded into GPU memory, thus improving performance. When the textures become too big to work with WebGL, their information can be split in the RGBA channels of standard 2D textures for a four-fold decrease in memory use. Furthermore, when displaying our rendering models, and in the case of machines without sufficiently powerful graphics cards, we propose to display only the fraction of the data that interests the user. All of these highly efﬁcient rendering models are assembled together in a toolbox dedicated to datacube visualization.

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name: Earth Observation DataCubes Multi-visualization Toolbox


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description: Analysis of Local and Remote Mappers’ Open Geographic Data Contribution to Oil Spill Disaster Response in Niger Delta Region, Nigeria Open mapping leverages on volunteer mappers mobilized and engaged from the public. volunteers most often are trained and coordinated virtually to carry out dedicated mapping task, irrespective of their geographic location, professional and academic background. In this study volunteer mappers engaged are categorized into two namely: the Local Volunteer Mappers (LVM) comprising of all the potential and actual mappers resident in Nigeria and the Remote Volunteer Mappers (RVM) comprising of all potential and actual mappers not resident in Nigeria. The study sampled 2 Local Government Areas (LGAs) of River State from the 4 vulnerable oil spill disaster LGAs of Ogoni land communities. Ogoni land is a major oil spill disaster vulnerable area of Nigeria, being the major host communities of crude oil exploitation in the Niger Region of Nigeria. Following the hazardous impact and damage of Ogoni land by oil Spill disaster over the years of oil exploitation in Niger Delta, UNEP assessed that the environmental restoration of Ogoni land would require coordinated efforts on the part of government agencies at all levels, industry operators and communities. UNEP also presented its recommendations as a major opportunity to bring new investment, employment opportunities and a culture of cooperation to Ogoni land in addition to driving improvements in the environmental and health situation on the ground. To effectively implement the UNEP recommendations for restoration of Ogoni land, there is a need for a geographic data that provides critical building footprint in the area, especially, to identify and access the vulnerable oil spill communities. Maps produced would be used by government agencies and other stakeholders working to implement UNEP report on Ogoni land restoration as well sustainable development. Consequently, the study engaged volunteer mappers to respond to sampled Oil spill communities viz-viz 3 LGAs in Rivers State, Niger Delta Region of Nigeria. To assess the level of participation of Local (mappers in Nigeria) and Remote Mappers (Not Resident in Nigeria), two mapping projects were created in HOT tasking manager for local and remote mappers respectively. For the purpose of campaigning for Volunteer Mappers the 2 project tasks were tagged ‘’ Mapathon Battle for Vulnerable Oil Spill Disaster Communities in Niger Delta’’ respectively. Project task 6358 was created exclusively for remote mappers outside Nigeria to map Tai LGA, while, project task 6359 was created exclusively local mappers resident Nigeria to map Gokana LGA in a Mapathon battle challenge. Project task 6358 had a total grided cells of 825 mapping tasks for online engagement of mappers while project task 6359 had an automated grided cells of 706 mapping tasks due to differences in the size of the area. The Mapathon unveiled the following research results. Engagement of remote mappers for project task 6358-Tai LGA shows that out of the 583 tasks completely mapped, only 13 were yet to be validated after 2 years of creating the project. This is as a result of archiving the project and diversion of attention to urgent tasks. The project recorded a total of about 16,416 edits comprising of 13,552 buildings and 858km of roads mapped in Tai LGA within the timeline of the study. Demographic characteristics of the contributors to project 6358 on the basis of HOT Tasking Manager users by experience and level shows that 50% were advance mappers and 100 % has more than 1 year mapping experience .The project engaged a total of 56 contribtors by mapping and validation. All mappers and validators by experience has used the tasking manager for more than 1 year while their mapping levels ranges between 40% for beginner mapper, 10 % for intermediate and 50 % for advanced mappers. The project timeline as illustrated by the graph shows that mapping and validation of the Tai LGA task commenced on the same date: 6th August,2019 at the rate of 12% mapping and 2 % validation. Mapping progressively ascended to 64% on the 4th day and got to its peak on the 9th day being 15th August with 99% of the entire task mapped. However, validation of the mapping task had a straight curve with the highest peak of validation being the 12th of September with 95% of the task being validated. By 8th January ,2020, being 6th months of the project,100% of the tasks were completely mapped while 13 of the 596 tasks were yet to be validated. The timeline statistics also shows that an average of 20mintes 46 seconds was the time spent per task to map a total of 583 tasks of 16,416 edits. Also, an average of 6minutes 16seconds was spent for validation per task leaving about 1hour 21minutes 29seconds to finish up the validation of 13 tasks left unvalidated due to a shift to other project tasks and less passion for the project under study. However, the analysis of local mappers engaged in HOT Project Task 6359 Gokana LGA also unveiled the following: The study shows that 706 (100%) of the tasks were completely mapped except for validation of 473(67%) tasks which requires further coordination of mappers. There is no record of bad imagery and tasks left unmapped. The project also recorded a total of about 2064 changesets for mapping a total of about 18,367 edits, comprising of 14,983 buildings and 521 km of roads. The project also recoded a total of 173 contributors comprising of 169 mappers and 8 validators. These mappers (100%) had more than 1year experience in online mapping with OpenStreetMap and are categorized into beginner mappers (72%), intermediate (6%) and advance mappers (21%). The entire project timeline by mapping and validation took a period of about 2years 4months(28months) from 6th August 2019 to 27th December ,2021 as at the time of writing this report. Conclusively, there is a lacuna worthy of research investigation in the mapping response level and capability of remote mappers from other countries and local mappers from Nigeria in crowdsourced rapid response mapping using OpenStreetMap.

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