uk: Комп'ютерні науки

description: Geospatial data and analysis is more central than ever to data science, research, and policy analyses. This is especially evident in the explosion of tools, both open source and proprietary that have been developed over the past 5 years to help users manage and gather insights from their data. However many of these powerful tools, like geopandas (analysis and modeling) and deck.gl (visualization)— are technically inaccessible to analysts and researchers without the available time or skills for advanced coding. A number of commercial ventures (Carto, ESRI etc) attempt to overcome this limitation by bringing these tools together as part of polished, graphical user interface driven platforms. While these platforms offer ease of use, they raise concerns about longevity, data ownership, and academic support. Matico is a new free and open-source platform we are developing at the Spatial Data Science center that seeks to fill the gap between open but technically focused tools and commercial platforms. Consisting of a suite of interoperable components, Matico enables organizations and individuals to manage and visualize their geospatial data while easily maintaining their own infrastructure. A backend server allows users to easily load, clean, analyze, and distribute data through APIs, queries, and in-browser data editing tools while a powerful app builder allows users to develop their own rich applications that target diverse audiences. This talk will demonstrate the current features of Matico, our future roadmap , and demonstrate relevant use cases. Matico is now and will forever be open through a permissive MIT open-source license. Learn more at matico.app/

uk: вхід без авторизації

name: Matico: a new federated FOSS platform for spatial analysis, data management, visualization, and app building


Matthew Hanson

type: Person

keywords: foss4g2022 generaltrack OpenData

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: The term Analysis Ready Data started as a way to describe a Landsat product that would efficiently allow time-series based analysis by providing a consistent, grid and pixel-aligned product corrected to surface-based measurements. Since then it has come to mean a wide range of things, but without a clear set of standards on how to characterize ARD there is little to no interoperability among datasets that call themselves ARD. The Analysis Ready Metadata initiative uses the SpatioTemporal Asset Catalog (STAC) spec as the vehicle for describing well-characterized data. This goes beyond the basic geospatial and temporal characteristics captured in the core STAC spec and into detail about the processing level of the data, corrections that have been applied, as well as spatial and measurement uncertainties. Having well-characterized data through it’s STAC metadata enables discovery of usable data, automated processing using interoperable workflows, and tracking of data provenance of derived products. The CEOS ARD (previously CARD4L) specifications require certain metadata and processing to be done for it to be compliant and can use this STAC metadata to automatically assess the potential for a dataset to be compliant with the needed requirements. This talk will cover elements of STAC, ARD, and the CARD4L family product specifications.

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: Analysis Ready (Meta)Data: Moving ForewARD with Analysis Ready Metadata

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: Baremaps is a blazing fast vector tile server which makes your life easier regarding the publication of OSM data: import, generation and cloud storage. But Baremaps also shines and differentiates from solutions like pg_tileserv in the way you can customize your tileset and merge custom datasets. Based on this advantage, we turn Baremaps out to be a vector tiles studio api, allowing the user to easily customize the content of the vector tiles. We adopted the OGC api specification for tileset, layers and styles. Baremaps offers various entry points to manage the datasets and serve them as vector tiles. As an exemple, you can dynamically import different kinds of data sources (geojson, SHP, database) to the server which will expose them as datasets, then you can use any kind of dataset within the same tileset. You can also bring value to your data by doing aggregations (spatial, attribute, hexbin) or computation. It leverages the power of postgis functions and vector tiles specification into one solution. You can attach a style for your dataset and baremaps will serve both Mapbox style file and Vector tiles stream to render the map the way you expect. To illustrate this concept, we will showcase a studio UI which literally provides a tool to quickly create valuable maps and publish them to the web. Baremaps Studio is the solution to handle dynamic rendering and styling of your vector datas.

uk: вхід без авторизації

name: Baremaps studio: dynamic vector tiles map rendering


keywords: foss4g2022 generaltrack Stateofsoftware

uk: Комп'ютерні науки

description: For many years QGIS has been focused on 2D spatial data and support for 3D data was very limited. This has changed in the last couple of years and QGIS is getting a full suite of tools to work with 3D data. QGIS development team has been actively working on better support for data with elevation - such as point clouds, raster digital elevation models, 3D vectors or meshes. This has been possible mainly thanks to the successful crowdfunding campaign run in autumn 2021: lutraconsulting.co.uk/crowdfunding/elevation-pointcloud-enhancements-qgis/ In this talk, we will show outcomes of these development efforts including: - a brand new profile tool for detailed inspection of elevation data - new 2D/3D visualization options for data - great improvements to the usability of 3D map views - support for Cloud Optimized Point Cloud (COPC) format We will also discuss the plans for future releases and how QGIS can even better fit requirements of users with the ever increasing supply of 3D data.

uk: вхід без авторизації

name: Elevation data support in QGIS: 3D, profiles, point clouds and more!


New OS: @vcmap/core

uk: Комп'ютерні науки

description: In the current web GIS ecosystem, 3D is nothing new. It is currently fairly simple to create a 3D web application for rendering geospatial data using open source software, the same can be said for 2D GIS. But in some use cases, you do not wish to have to decide between one or the other. Enter @vcmap/core, a new OS project developed by virtualcitySYSTEMS GmbH of Berlin. With a number of high level abstractions, this slim open source library allows you to create web applications which are able to represent the same data in 2D, 3D and even oblique imagery. By abstracting layers, maps, interactions and styling, your data becomes renderer agnostic. Additionally, a parameterized approach to 3D allows you to easily create cuboid 3D representations from simple 2D representations. A feature which has proven useful in urban planning scenarios. Furthermore, the @vcmap/core comes with a powerful serialization mechanism. All runtime objects can be serialized and stored using JSON. This way, you can easily develop a web gis framework which allows a quick deployment of multiple applications which only differ in data. And this is not all, the @vcmap/core is still not finished, with geometry editors on the roadmap and a further open source project, the @vcmap/ui to follow this year. The @vcmap/ui is an accompanying UI which integrates smoothly with the @vcmap/core and provides a powerful plugin API. This plugin API allows for fast development of custom tools with which to enhance, analyze and use your geospatial data without the need to fully implement an entire web GIS.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: The MapWithAI RapiD editor for OpenStreetMap offers a variety of open data to improve OpenStreetMap. This web-based map editor presents the user with various sources of open data to validate and add to OpenStreetMap, including MapWithAI roads, Microsoft buildings, and various open datasets shared via Esri. In addition to these past data offerings, the user can now validate and add sidewalks and crosswalks derived from both Mapillary street-level imagery, as well as derived from various organizations who provide footway open data. Finally, Mapillary point data derived from imagery can also now be verified and directly converted into map data, thanks to a more efficient and rapid workflow. We will explore all that open data available in the RapiD editor, with a specific focus on how footways are generated from Mapillary, validated from open datasets, conflated against existing OpenStreetMap data, and presented to the user for improved maps of pedestrian walkability.

uk: вхід без авторизації

name: Open Data in OpenStreetMap’s RapiD Editor


QGIS and Community: The QGIS Open Day

uk: Комп'ютерні науки

description: The QGIS Open Day are organised on the principle of self-organisation and community participation. The monthly sessions are open to anyone in the opensource community and cover various topics from presenting new developments and releases, tutorial style work-flows and interactive open sessions. In the year the channel has been active, QOD has generated over 50 videos obtained 4000 subscribers and on average QOD channel receives Approximately 5000 views each month. Most QOD viewers are from the United States, Germany, India, France and the UK and 94% of QOD viewers are male. The most popular video on the channel is “A geological map work-flow in QGIS with Chris Lambert with” 5277 views. Looking forward, the QOD channel aims to be the official platform to show the functionality of the new QGIS releases, plugins, work-flows, and opensource GIS platforms. The channel aims to increase support, viewership and participation from a wider, more diverse audience and encourage different regions to contribute. Join the QOD community and let’s learn from each other.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Managing hundreds of layers from different sources in a Mapserver production is extensive work. Keeping them up to date, scalable and in constant deployment takes time and effort. Not to mention monitoring all of it. By combining a configuration management tool (open-source Progress Chef in our case) and Mapserver, we have a continues deployment cycle. Mapserver’s map file is divided into pieces that Chef puts together. All the layer files are separate entities which are easily manageable and changeable. Different map files can be produced combining different layers to keep map files smaller but still all in one place for management. It also enables to switch off or turn on layers easily. This also gives the benefit of keeping development environment different from production. Through MapProxy seeding process we also provided our thousands of users with their base map services and serve them WMS, WFS and our own produced Vectortiles. All of it is also under constants monitoring and the logs are processed to produce simple statistics to see which applications are requesting, which layers are being accessed the most. We have built a notification system that notifies us immediately through hooks if our services are down or there are errors in any of the Mapserver layers requests. It brings us back to the point of how to make your Mapserver layer handling, production, and management smoother and more straightforward. Let us share our insight!

uk: вхід без авторизації

name: Mapserver layer handling, production, and management in larger scale environment


uk: Комп'ютерні науки

description: Publicly available data tends to be spatially aggregated to administrative units, limiting the feasibility of nuanced analyses that reflect the natural state of communities and provide actionable insights for a wide range of stakeholders. While higher resolution data is generally available within government agencies, access for external researchers is limited due to well-established privacy concerns. Inspired by our own use case of developing a regional quality of life metric for neighborhoods in Denmark, our team at Aalborg University’s Department of the Built Environment, in collaboration with data.org’s Growth and Recovery Challenge, and Data Clinic, set out to develop and open source not only foundational granular spatial units and data that adhere to privacy laws, but also the accompanying methodology that has the potential for broad applicability in other countries. In this presentation, we will demonstrate the methodology’s generalizability, particularly across common European land use and geographical features, and show how the resulting high-resolution shape files and community data can become crucial tools for government decision-makers, community organizations, and researchers in their efforts to increase transparency and engage in practical, actionable research. Focused initially on our Denmark use case, we algorithmically create spatial units with minimum household and population counts from country-wide hectare cell level data. Our approach uses data on road networks and administrative boundaries to create socially meaningful component polygons. This is achieved by developing tools based on already existing open source packages available in R and Python. The hectare cells are then mapped onto the polygons and clustered using the max-p regionalization algorithm with constraints on the minimum population and household counts to arrive at the final set of spatial units. To improve the accessibility of this data to not just researchers but also administrative decision-makers, community organizations, and the general public, we are developing an online tool to explore and visualize indicators within the resulting fine-grained regions such as disposable income, educational level, housing prices, migration rates, distances to public institutions, and labor market attachments in Denmark. Regional inequality in Denmark has increased over time, and with the help of this tool, we hope to provide the ability to study these key metrics both within and across municipal regions. In the development of the tool, we prioritize user feedback and common use cases to ensure both applicability and longevity. This project has been developed with an open-source mindset by: 1) creating flexible open data resources that can adapt to a wide range of public use cases 2) open sourcing the methodology for use in other countries/regions and 3) enabling the use of existing open data and tools such as Open Street Maps, R and Python in the pipeline. We firmly believe that the project has the potential to improve knowledge sharing and collaboration between GIS experts, decision-makers, researchers and the general public not only in Denmark, but also in Europe and beyond.

uk: вхід без авторизації

name: Not too big, not too small: open source geospatial units that are just right


Dataviz in QGIS and on the web

uk: Комп'ютерні науки

description: #### QGIS and Dataviz Creating plots is out of the main scopes of QGIS but thanks to the simple Python API, it is easy enough to create additional scripts and plugins. The DataPlotly plugin has been developed for QGIS(the first release was created in 2017 while nowadays the plugin has been downloaded more than 100,000 times). It's today a well maintained Python plugin with a growing community of developers, users and testers. DataPlotly allows creating D3 like plots from spatial data. It is build on top of Plotly.com, a JavaScript library which offers an easy API for many languages such as Python, R, Matlab etc. The plots are completely interactive so that plot elements are directly linked with map items; therefore the user is able to query map items from the main plot canvas. Thanks to a crowdfunding campaign, the functionalities of DataPlotly were extended: a complete refactoring of the code, more plots but especially the creation of plots in the layout composer, also for atlas layouts. The plugin is also compatible for QGIS server. Lizmap Web Client is an opensource server application to publish QGIS project on the web without any coding skills needed. It’s using QGIS Server in the backend so users have the same rendering between their QGIS Desktop and the web version of their project. Thanks to the DataPlotLy plugin installed on QGIS Server and to the Lizmap application, it allows users to print PDF with plots from in their web-browser.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: When you care about data integrity of spatial data you need to know about the limitations/weaknesses of using simple feature datatype in your database. For instance (land.copernicus.eu/pan-european/corine-land-cover/clc2018) contains 2,377,772 simple features among which we find 852 overlaps and 1420 invalid polygons. For this test I used “ESRI FGDB” file and gdal for import to postgis. We find such minor overlaps and gaps quite often, which might not be visible for the human eye. The problem here is that it covers up for real errors and makes difficult to enforce database integrity constraints for this. Close parallel lines also seems to cause Topology Exception in many spatial libraries. A core problem with simple features is that they don't contain information about the relation they have with neighbor features, so integrity of such relations is hard to constraint. Another problem is mixing of old and new data in the payload from the client. This makes it hard and expensive to create clients, because you will need a full stack of spatial libraries and maybe a complete locked exact snapshot of your database on the client side. Another thing is that a common line may differ from client to client depending on spatial lib, snapTo usage, tolerance values and transport formats. In 2022 many system are depending on live updates also for spatial data. So it’s big advantage to be able to provide a simple and “secure” API’s with fast server side integrity constraints checks that can be used from a standard web browser. When we have this checks on server side we will secure the equal rules across different clients. Is there alternatives that can secure data integrity in a better way? Yes, for instance Postgis Topology. The big difference is that Postgis Topology has more open structure that is realized by using standard database relational features. This lower the complexity of the client and secures data integrity. In the talk “Use Postgis Topology to secure data integrity, simple API and clean up messy simple feature datasets.” we will dive more into the details off Postgis Topology Building an API for clients may be possible using simple features, but it would require expensive computations to ensure topological integrity but to solve problem with mixing of new and old borders parts can not be solved without breaking the polygon up into logical parts. Another thing is attribute handling, like if you place surface partly overlapping with another surface should that have an influence on the attributes on the new surface. We need to focus more on data integrity and the complexity and cost of creating clients when using simple feature, because the demands for spatial data updated in real time from many different clients in a secure and consistent way will increase. This will be main focus in this talk.

uk: вхід без авторизації

name: Data integrity risks when using simple feature


uk: Комп'ютерні науки

description: We’re drowning in data, but the geospatial world lags badly behind in versioning tools compared to our software counterparts. Kart (kartproject.org) is solving this with a practical open tool for versioning datasets, enabling you to work more efficiently and collaborate better. We will introduce you to Kart and demonstrate some of the key features, including our QGIS plugin. And we'll highlight what’s coming next on our roadmap. Since 2021 we have added support for Raster and Point Cloud datasets, and we'll be showing how we build on Kart's versioning and spatial filtering techniques to efficiently navigate, access, and use large and small datasets. Kart allows you to quickly and easily manage history, branches, data schemas, and synchronisation for large & small datasets between different working copy formats, operating systems, and software ecosystems. Modern version control unlocks efficient collaboration, both within teams and across organisations meaning everyone stays on the same page, you can review and trace changes easily: ultimately using your time more efficiently.

uk: вхід без авторизації

name: Kart: an introduction to practical data versioning for rasters, vectors, tables, and point clouds


MapMint: The service-oriented platform

uk: Комп'ютерні науки

description: MapMint is a comprehensive task manager for publishing web mapping applications. It is a robust open source geospatial platform allowing the user to organize, edit, process and publish spatial data to the Internet. MapMint includes a complete administration tool for MapServer and simple user interfaces to create mapfiles visually. MapMint is based on the extensive use of OGC standards and automates WMS, WFS, WMT-S, and WPS. Most of the MapMint core functions are run through WPS requests which are calling general or geospatial web services: vector and raster operations, mapfiles creation, spatial analysis and queries and much more. MapMint server-side is built on top of ZOO-Project, MapServer and GDAL and its numerous WPS services are written in C, Python and JavaScript. MapMint client-side is based on OpenLayers and Jquery and provides user-friendly tools to create, publish and view maps. MapMint architecture and main features will be introduced in this presentation, and its modules (dashboard, distiller, manager, and publisher) will be described with an emphasis on the OGC standards and OSGeo software they are using. Some short but relevant case studies and examples will finally illustrate some of the key MapMint functionalities.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: GC2/Vidi: What’s new in spatial data infrastructure project The GC2/Vidi platform helps you build a spatial data infrastructure quickly and easily. Powered using open source components for a scalable solution focused on freedom rather than fees. GC2/Vidi comprises two software projects: - GC2 – makes it easy to deploy PostGIS, MapServer, QGIS Server, MapCache, Elasticsearch, GDAL/OGR. And offers an easy-to-use browser application to configure the software stack. - Vidi – a modern take on browser GIS. It is the front-end client for GC2. The GC2/Vidi project is released under GPL and accepted as an OSGeo Community Project in 2018. The talk gives a brief overview of the platform and summarizes the capabilities it has to offer. A new CLI tool (Command Line Tool), which enables administration, import/export of data, starting MapCache seed jobs, running SQLs and more will be introduced. In addition, the new "GC2/Vidi User Group" will be introduced. It is a non-profit organization whose mission is to promote the adoption of GC2/Vidi and the underlying technologies as well as knowledge sharing. The organization was founded in 2020 and has about 15 members, including municipalities, public transport and private companies.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Exploitation platforms offer a cloud-based virtual work environment where expert users can access data, develop algorithms, conduct analysis close to the data and share their value-adding outcomes. We now have a complementary ecosystem of platforms, data sources and cloud services. To fully exploit the potential of these complementary resources we anticipate the need to encourage interoperation amongst the platforms, such that users of one platform may consume the services of another directly platform-to-platform. The goal of the EO Exploitation Platform Common Architecture (EOEPCA) project is to define and agree a re-usable exploitation platform architecture by identifying a set of common building blocks that provide their services through open interfaces (e.g. OGC), to encourage interoperation and federation within this Network of Resources. We are also developing an open source Reference Implementation, to validate and refine the architecture, and to provide an implementation to the community. The Reference Implementation comprises a set of open source components that are available on GitHub, and provided with helm charts for Kubernetes deployment. The components can be used together as an integrated platform, or individually for specific capabilities - which include: - Application Deployment and Execution Service (ADES) - processing engine for execution of user defined applications via OGC API Processes interface - Processor Development Environment (PDE) - integrated web tooling to develop, test and package apps for ADES execution - Resource Catalogue - metadata catalogue for data/applications which provides OGC CSW, API Records, STAC and OpenSearch interfaces - Data Access - standards-based access to both platform and user-owned data (OGC WCS, WMS, WMTS) - Workspace - centralises the user’s management of owned resources through personal Resource Catalogue and Data Access services, integrated with platform S3 object storage - Identity and Access Management - OpenID Connect (OIDC) for authentication (OIDC) and User Managed Access (UMA) for authorization, with integrations for external identity providers We provide an introduction to each of the building blocks and the open source projects that underpin their development. All of our work is available on GitHub (github.com/EOEPCA), via our website (https://eoepca.org/) and through our helm chart repository (eoepca.github.io/helm-charts).

uk: вхід без авторизації

name: EOEPCA - An Open Source Exploitation Platform


uk: Комп'ютерні науки

description: The United Nations Mission in South Sudan (UNMISS) is a United Nations peacekeeping mission for South Sudan, which became independent on 9 July 2011. UNMISS was established on 8 July 2011 by United Nations Security Council Resolution 1996 (2011) and as of March 2021, it is composed of 19,075 total deployed personnel including 14,222 troops; 217 experts on mission; 1,446 police personnel; 2,228 civilians; 387 staff officers and 388 UN Volunteers, where, it is headquartered in the South Sudanese capital of Juba. Under Chapter VII of the Charter of the United Nations, UNMISS is therefore authorized to use all necessary means to implement its mandate which includes: (a) Protection of civilians (b) Creating conditions conducive to the delivery of humanitarian assistance (c) Supporting the Implementation of the Revitalized Agreement and the Peace Process (d) Monitoring, investigating, and reporting on violations of humanitarian and human rights law The mission has decided to extend its public outreach activities in a different method by utilizing geospatial information and using open geospatial tools and data for showcasing some of its important activities in support of above-mentioned mandates, and for this purpose contracted a service provider through bidding exercise and procurement protocols. In this general session talk, speaker(s) will give their presentations on below topics: - UN Open GIS Initiative Background - UNMISS GeoStories architecture, FOSS4G tools and data - Preventing mis/dis-information by extending public outreach - Review selected Geostories in support of UNMISS mandate

uk: вхід без авторизації

name: United Nations Mission in South Sudan GeoStories


uk: Комп'ютерні науки

description: We're living in the world of APIs. CRUD operations are base of lot of operations. Many smart frameworks such as Django, Flask, Laravel provides out of the box solutions to filter the data, which covers almost all needs to separate data based on column values. When it comes to Geospatial data, we expect to filter data based on their location property instead of metadata. This is where things get complicated, if you are using framework that doesn't have package, library built to handle such use cases, you are likely to be dependent on either database or any external package to handle it. Fortunately Geodjango [docs.djangoproject.com/en/4.0/ref/contrib/gis/] (Django's extension) allows us to create databases which understands geometry and can process it [docs.djangoproject.com/en/4.0/ref/contrib/gis/geoquerysets/#gis-queryset-api-reference]. It also provides support to write APIs using Rest Framework extension [pypi.org/project/djangorestframework-gis/] which takes this to next level allowing user to output the data in various formats, creating paginations inside geojson, create TMSTileFilters, etc. In this talk we'll scratch the surface of this python package and see how to build basic CRUD APIs to push, pull GIS data along with filtering it to the PostgreSQL database

uk: вхід без авторизації

name: Creating GIS Rest APIS using Geodjango under 30 minutes


uk: Комп'ютерні науки

description: Kaoto is an graphical tool to *orchestrate components* in a *visual*, low-code and no-code editor. Once you have your workflows defined, you can deploy them directly to any kubernetes compatible cloud. Kaoto both be deployed as a SaaS platform or used as a standalone application. The user interface have both a source code text editor and a drag and drop graphical space. This way users can work both no-code and low-code at the same time, simplifying the learning curve of Apache Camel to create integrations. Kaoto is *highly customizable*. It supports custom views for your specific needs, like showing manuals and helpers for your specific use cases. You can also add *your own domain specific languages* and extensions to use different underlying frameworks with the same user interface. This helps your non tech savvy users adapt to new environments. Kaoto augments your productivity, accelerating new users and helping experienced developers to build complex integrations.

uk: вхід без авторизації

name: Kaoto: Integrate your Architecture without coding


uk: Комп'ютерні науки

description: COMTiles (github.com/mactrem/com-tiles) is a streamable and read optimized file archive for hosting map tiles at global scale on a cloud object storage. Currently most geospatial data formats (like MBTiles or Shapefiles) were developed only with the POSIX filesystem access in mind. COMTiles in contrast is designed to be hosted on a cloud object storage like AWS S3 or Azure Blob Storage without the need for a database or server on the backend side. The map tiles of a COMTiles archive can be accessed directly from a browser via HTTP range requests. COMTiles are already successfully used in some projects to significantly reduce the hosting costs and simplify the handling of large tilesets in the cloud. Structure of the talk: - Basic concepts of COMTiles like the structure of the streamable index table (pyramids vs space-filling curves vs fragments) - Comparison of COMTiles to existing cloud native geospatial formats regarding the visualization of large datasets in the browser - Advantages of using a streamable archive format like COMTiles over directly hosting the map tiles in the cloud

uk: вхід без авторизації

name: Using COMTiles to reduce the hosting costs of large map tilesets in the cloud


uk: Комп'ютерні науки

description: This talk is about the development of an Environmental Impact Assessments(EIA) data visualization system using FOSS4G. The system is being developed by Gaia3D utilizing several open source projects such as PostGIS, GeoServer, Cesium, and mago3D. Although EIA has played an important role for environmental decision-making and sustainable development, most EIA statements are published as a mix of text and tabular data that is not easily accessible to or understandable for the public. The system was designed to improve the public’s understanding of stakeholders before and after a construction project by providing visualization of key environmental elements. The final goal of the system is to improve the EIA process so that not only experts but also non-experts, citizens can participate in the EIA process and easily understand the meaning of the EIA statements with help from 3D GIS, Easy Finger real-time simulation technology. This system development is 5 years long project funded by Ministry of Environment(MOE-2020002990005), South Korea. This talk will focus on the research outcome of Phase I and future plans. The final system will be opened as an open source with permission from Ministry of Environment.

uk: вхід без авторизації

name: Development of Environmental Impact Assessments(EIA) Data Visualization System using FOSS4G - Phase I


uk: Комп'ютерні науки

description: At our institute we manage a lot of input data and model outcomes of soil data to be shared online. We experienced that updating service configurations and metadata records can be quite a challenge, when managed manually at various locations. We've been working on tooling to help us automate the publication processes. These days data publications are set up as CI-CD processes on Gitlab/Kubernetes. These efforts resulted in a series of tools which we call the Python Data Crawler. The crawler spiders a folder of files, extracts and creates metadata records for the spatial files, as well as generates a Mapserver configuration for the data to be published as OGC services. Underneath we're building on the tools provided by the amazing FOSS4G community, such as GDAL, Mapserver, pygeometa, owslib, mappyfile, rasterio and fiona. A typical use case for this software is with many organizations maintaining a file structure of project files. The crawler would index all the (spatial) data files, register the metadata records in a catalogue and users would query the catalogue from QGIS Metasearch to find and load relevant data. We will present our findings around the project at the conference and hope to talk to institutes with similar challenges, to see if we can create an open source software project around the Python Geodata Crawler.

uk: вхід без авторизації

name: A crawler for spatial (meta)data as a base for Mapserver configuration


uk: Комп'ютерні науки

description: Mergin Maps (Mergin synchronization server and the Input app) is a package of free and open-source components developed by Lutra Consulting since 2017. It allows users to seamlessly share QGIS projects with others and keep a history of the geo-data. Moreover, it allows collecting data in the field with the mobile application Input, fully based on the QGIS core engine. No more paper for the collection of vital data in the field! We will briefly present published case studies to show the capabilities and features of the solution. We will talk about the recent development of the product. In the Input app, where we focused on improving the field survey experience by allowance to use of precise external GPS receivers, stake-out navigation mode or attaching multiple photos to a single feature. On the server-side, in the Mergin, we will demonstrate the ability to store, version and share your geo-data with your team. You will see the new feature to show a map overview of your Mergin project on the dashboard. To fully integrate into CDI, the DB-sync tool for two-way synchronization between Mergin and PostgreSQL will be presented. Advanced features for usage in large teams, such as selective synchronization and work packages (subprojects for teams within companies) will be explained. At the end of the talk, we will uncover the upcoming roadmap for the new features coming in the second half of 2022.

uk: вхід без авторизації

name: Liberate your QGIS projects out of office with Mergin Maps


uk: Комп'ютерні науки

description: Recent advancements in both raster and vector tile generation mean that TileJSON services can now serve tiles from on-the-fly sources as well as pre-built caches. Currently, Addresscloud uses CloudFront backed by S3 buckets to serve tile caches for its customer-facing applications. Whilst this configuration worked well for pre-built tile caches, it does not readily support on-the-fly generation and is limited by CloudFront's requirement for cookies or signed URLs for private tilesets. In this presentation we will look at the use of Amazon's API Gateway to provide a scalable interface for multiple TileJSON sources. This approach benefits from providing on-the-fly generation tile in a serverless manner and supporting multiple authorization configurations. The presentation will demonstrate the integration of API Gateway with three tile sources: (1) a Lambda function using rio-tiler for on-the-fly generation of raster tiles from a Cloud Optimised GeoTiff. (2) a Lambda function using Amazon Aurora's HTTP API for MVT generation from PostGIS. (3) a proxy interface to a pre-built cache of tile objects stored in an S3 bucket. The presentation will include publication of source code under an open license, which will be available to the community as a reference architecture. This presentation is of interest to anyone developing tiling services in the cloud.

uk: вхід без авторизації

name: Building a scalable tiling service using Amazon API Gateway


uk: Комп'ютерні науки

description: GeoJSON is one of the most common geospatial data formats. In simple terms, it is an extension of JSON with geometry property. It is text-based and designed with human readability in mind. For the sake of being eye-convenient, there is a performance trade-off when the browser renders it. GeoJSON consists of features containing redundant property keys, causing the size to be bloated as the feature size goes up. Commonly, drawing GeoJSON with the size of tens megabytes would be slow. Showing a hundred megabytes of GeoJSON data on the browser would most likely crash the browser. When we are in complete control of the system: back end, front end, or anything in between, we could probably change the source format to something more efficient like Vector Tiles. But what if we can only tweak the front end? When we can only tweak the front end, geojson-vt comes to the rescue. Initially designed for Mapbox, we can pair it with OpenLayers to render GeoJSON on the fly as Vector Tiles. We will compare the performance between direct GeoJSON rendering versus geojson-vt for different types of GeoJSON. The usage is straightforward, making it a pretty easy solution to improve our map’s performance. On top of that, we could still use Vector-specific Open Layers function like getFeatures when needed.

uk: вхід без авторизації

name: geojson-vt for Highly Efficient Geojson Rendering in Open Layers

name: Open Source Geospatial Foundation (OSGeo)


uk: Комп'ютерні науки

description: It seems to be conventional wisdom that a search engine for geodata is best implemented with a text search engine like OpenSearch or Solr. Most of available open-source geocoders follow that wisdom. Nominatim is the odd one out. OpenStreetMap's main geocoder was originally developed 12 years ago as a proof of concept that a geocoder can be efficiently implemented on top of a PostgreSQL/PostGIS database. Since then it has grown into mature project. And so have the PostgreSQL database and the OpenStreetMap project. In this talk, I will share some of the experiences of working with PostGIS on a growing OpenStreetMap dataset. The talk starts with a quick overview about what the Nominatim database looks like under the hood. It then goes on to present some of the lessons we have learned over the last 10 years on managing a PostGIS database with more than 270 million searchable places. We talk about features that improved performance and about some that are best avoided. The talk concludes with some general observation about implementing search on top of an SQL database.

uk: вхід без авторизації

name: Building a Geocoder on top of PostGIS - a Field Report


uk: Комп'ютерні науки

description: In many countries, access to schooling is one of the key measures of performance of the education system. It is not always known how long learners walk to school, even if the buffer distance is set by policy. GISPO teamed up with the UNESCO International Institute of Educational Planning (IIEP) to study the problem. The result is a new QGIS plugin (“Catchment”) which allows easily calculating catchment areas based on travel time (isochrones), for all schools across a whole territory. The plugin uses the open source Graphhopper routing server and OpenStreetMap data across the globe. This allows us to easily find out how many people live e.g. 15, 30 or 60 minutes away from education in different parts of a country. Further, the development of the plugin triggered a campaign of local OpenStreetMap mapping in Madagascar, which was one of the first countries to pilot the plugin. Having more roads mapped on OpenStreetMap has an impact far beyond educational planning. Naturally, the same plugin may also be used for calculating all kinds of service catchment areas in QGIS; it was also employed to e.g. calculate access to rail transit across Helsinki metropolitan region.

uk: вхід без авторизації

name: Calculating school catchment areas - an open source solution


Aggregating risk with H3 and PostGIS

uk: Комп'ютерні науки

description: In this talk we will look at how PostGIS and Uber's H3 index can be used for aggregating large amounts of data, in our case property insurance risk, in real-time. We will explore a number of different techniques from the H3 PostGIS extension generating GeoJSON, to generating MVTs from the database to pre-caching the H3 index and painting a vector tile layer client side. For our client side layer will use a React JS interface, Maplibre and will also look at Deck.GL for more advanced use cases. We will discuss how the stack can be deployed using a serverless architecture running on AWS Lambda and Aurora Serverless Postgres. This talk requires no prior knowledge however some experience with PostGIS and vector tiles will be useful. You will learn techniques which can be applied to any problem domain where there is the need to work with data volumes where processing individual points would not be practical.

uk: вхід без авторизації


MapServer - Make beautiful maps

uk: Комп'ютерні науки

description: This talk will be about the art of beautiful digital cartography. Some of the features in Mapserver can contribute to making maps that stand out a little extra. We will focus on advanced line symbology, the layer composition pipeline and the newly added GEOMTRANSFORM "centerline". Creating very complex line symbology can be tricky. We will go into detail about how to build such symbology. The layer composition pipeline offers many exciting possibilities. We will show various examples how to achieve some stunning symbology for different feature types. The geomtransform centerline function can produce beautiful labeling possibilities. My first experiences and lessons will be shared. Other things that could come up is possibilities with “Named Styles” To summarize it will be a talk about some new features and some older features in MapServer that are described in more detail. The talk is based on practical experiments and real problems that the author has experienced.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: In the last years the attention for gender equality in all context has increased all over the world. Nowadays the sensibility of Public Administrations towards the naming of streets, roads, squares and monuments after women has highlighted that, instead, the toponymy has always been oriented to the choice of male figures. In this work we present a Python script for QGIS, that allows to verify if a proper name, contained in a street directory, is of male or female gender. There are other Open Source projects that, starting from an address, verify the gender of the represented person; the most famous is the GeoChicas Project [1]; in Italy it is worth mentioning the "Toponomastica Femminile" Association [2] that manually verifies the streets dedicated to women, according to a predefined taxonomy (religious women, artists, etc.). The goal of the present work is to automate the gender reconnaissance starting from a list of names; however, unlike GeoChicas that use as a base parameter a dictionary of names with which to compare the list, we propose to make a query of DBpedia via SPARQL in order to identify the subject and derive its gender. If the address is the attribute of a spatial dataset, then it is possible to add a new attribute (the gender) to the vector layer table as a result of DBpedia query. This approach overcomes language limitations (which would require differentiated dictionaries) and the ambiguities that some names would have (for example the nome "Andrea" is used as both a masculine and feminine name). The script is created using the SPARQL language with a very simple structure, in which the triplet of data is constructed in order to obtain the gender from the name of a person through the query of DBpedia. The script can be run in QGIS environment associating the data outputs directly to the geometry or even outside of QGIS and as a result you will have a list of "genders". The process of relying on Wikipedia/DBpedia has the twofold advantage that, where the name dedicated to street exists, then the desired information is taken, the gender in our case, while if it missing it can be added or enriched. The script is currently under validation and will be published in the dedicated git repository [3]. [1] github.com/geochicasosm [2] toponomasticafemminile.com [3] github.com/skampus/toponomasticafemminile

uk: вхід без авторизації

name: A Python tool for QGIS for gender recognition in street directories


keywords: foss4g2022 generaltrack Education

uk: Комп'ютерні науки

description: The presentation will share how UNESCO’s International Institute for Educational Planning (IIEP) applies FOSS4G technologies to advance Ministries of Education’s use of geospatial data in planning better educational results among school children. Our work here at the IIEP-UNESCO is to design tools for educational planners all around the world, and FOSS4G has been the cornerstone of our work. Educational planners are the professionals who work in Ministries of Education –in district offices or in the central office-- that are tasked with designing the best possible strategies and interventions to make sure that all learners will get good quality access to relevant and efficient educational services. For decades, planners have been using geospatial insights with minimal computing capacity and- to be honest- very little spatial data. Over the last few years, we have been completely refurbishing the methods and the data that we use as planners, and working with the FOSS4G community has been instrumental in fulfilling our mission. This talk is about sharing concrete applications and use cases of geospatial data in educational planning. For example, we spatialize the number of students that will enrol in each grade in different communities, we plan for the training, recruitment, deployment, and retention of the teaching staff, we lead suitability analyses to check where to best build a new school or where to refurbish existing ones. So in this presentation we will show you examples of application of tools and methodologies all built on FOSS: - Spatialized school-age populations in Jamaica - Routing optimization of inspection circuits in Finland - Geographically-weighted regressions for improving learning in Colombia - School infrastructure and natural hazard risk model in Indonesia - Sea level rise and historical floods in Viet Nam - School catchment areas based on travel time (check out the presentation submitted by Riku Oja from GISPO, it’s our joint work!) As an institution here at IIEP we have sought to advance this line of work by (1) making a complete switch to free and open source software (FOSS) and open access documentation and data sources (OpenScience), (2) bringing geospatial approaches and big, small, and thick data, to update EDplanning processes, (3) creating technical partnerships with instances such as GISPO, UNOSAT, among others, and (4) collaborating on informing education policy-making with geospatial insights. This talk is an invitation to all geospatial data geeks to join us in shaping the future of educational planning.

uk: вхід без авторизації

name: Geospatial data science for planning education systems


uk: Комп'ютерні науки

description: The World Meteorological Organization (WMO) Information System (WIS) is a coordinated global infrastructure responsible for telecommunications and data management functions and is owned and operated by WMO Members. WIS 2.0 will provide users with seamless access to diverse information from a wide range of sources and will enable weather, water and climate information to be related to socioeconomic and other contexts. Through an open ecosystem of tools, applications and services, WIS 2.0 will allow all information providers to manage, publish and share their data, products and services, and will allow all users to develop value-added services and new products. The WIS 2.0 principles highlight and promote the value of standards, interoperability and the Web/mass market. This will extend the reach of weather/climate/water data for a number of societal benefits. WIS 2.0 is being designed to have a low barrier to entry for data providers. This will also result in enabling infrastructure and provide great benefit for less developed countries (LDCs). There is a strong motivation to provide LDCs easy to use tools and sustainable workflow for data exchange to 1./ ease the burden of exchanging data 2./ continue to provide valuable weather/climate/water data in WIS 2.0 over time. The WIS 2.0 in a box (wis2box) project enables LDCs free and open source onboarding technology to integrate their data holdings and publish them to WIS 2.0 in a manner consistent with the architecture for plug and play capability, supporting discovery, access and visualization. This presentation will provide an overview of the project and current capabilities highlighting the use of numerous FOSS4G tools and PubSub driven implementation of OGC API standards.

uk: вхід без авторизації

name: Introducing WIS 2.0 in a box: an open source and open standards platform for international weather, climate and water data discovery, access, and visualization


uk: Комп'ютерні науки

description: Micro animations are small animations on a website that support the user by attracting focus to where we want their attention. They can also be used to support relationships between elements in a web application, for example a list element and a map feature, or simply to spark a little joy. Users today have come to expect these animations in their online experiences. How can we provide these features in a web map? Map libraries gives you some animations out of the box today, but what if you want something custom? This presentation will give examples on how small animations can be used in web maps to support interactivity. We will walk through building our own, custom animation that can be used as a starting point for many types of animations in web maps. The technique is library-agnostic, so we’ll show examples in both MapLibre GL JS, Leaflet and OpenLayers.

uk: вхід без авторизації

name: Liven up your webmaps with custom microanimations


uk: Комп'ютерні науки

description: In this talk, we'll review the major milestones that have defined Spatial SQL as the powerful tool for geospatial analytics that it is today. From the early foundations of the JTS Topology Suite and GEOS and its application on the PostGIS extension for PostgreSQL, to the latest implementation in Spark SQL using libraries such as the CARTO Analytics Toolbox for Databricks, Spatial SQL has been a key component of many geospatial analytics products and solutions, leveraging the computing power of different databases with SQL as lingua franca, allowing easy adoption by data scientists, analysts and engineers. The CARTO Analytics Toolbox is a comprehensive library that provides advanced geospatial functionality through Spark SQL. It enables Spatial SQL analytics at scale providing the foundational tools for analyzing and visualizing geospatial data. In this talk we'll cover the technical aspects of the library implementation using Open Source technologies, as well as demonstrating the installation and practical usage with a real-life example. Our talk will go through some of the geospatial operations that can be performed directly in Spark and we will demonstrate how users of the Analytics Toolbox can create beautiful map visualizations leveraging the latest Open Source rendering tools; and how to address a wide variety of spatial use cases using other products built on top of open source technologies, like CARTO and Databricks.

uk: вхід без авторизації

name: A new SQL library to enable spatial analytics in Spark


What’s new in geospatial Elasticsearch

uk: Комп'ютерні науки

description: Elasticsearch (elastic.co/elasticsearch/) is a well-known and mature NoSQL database providing search and analytics services for big datasets. The “elasticity” of its name comes from the distributed design and easy scalability capabilities that have made it an industry leader for more than ten years. In this talk we will present two exciting new features that have been added recently to the product related with the geospatial topic: vector tiles support and line and hexagon aggregations. Vector tiles have become an industry standard to encode large amounts of data to be displayed in the browser by web mapping libraries like MapLibre or OpenLayers. Elasticsearch analytics & geo team has added a new API endpoint (elastic.co/guide/en/elasticsearch/reference/current/search-vector-tile-api.html) that renders search and aggregation queries as zipped protobuffers (developers.google.com/protocol-buffers), allowing developers to retrieve right from the datastore assets that are ready to be sent to the user's browser without much further processing. This will speed up the rendering of large datasets by avoiding transferring JSON assets from Elasticsearch to application middleware. Elasticsearch geospatial aggregation capabilities have been extended recently by two new methods, one is to allow combining related points into a new line geometry (elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geo-line.html) (think of a vehicle track) and the other is to aggregate geometries into an hexagon grid (elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-bucket-geohexgrid-aggregation.html). The new geo-line aggregation will be very useful for asset tracking use cases where the second enables Elasticsearch to perform powerful analytics combined with the extensive support for metric aggregations. In this talk we will present this project, going through the different use cases with some examples and demonstrations using both Kibana Elastic Maps (elastic.co/maps) and a simple ad-hoc web project that leverages this new feature.

uk: вхід без авторизації


State of PDAL 2022

uk: Комп'ютерні науки

description: PDAL is Point Data Abstraction Library. It is a C/C++ open source library and applications for translating and processing point cloud data. It is not limited to LiDAR data, although the focus and impetus for many of the tools in the library have their origins in LiDAR. PDAL allows you to compose operations on point clouds into pipelines of stages. These pipelines can be written in a declarative JSON syntax or constructed using the available API. This talk will focus on the current state of the PDAL Pointcloud processing library and related projects such as COPC and Entwine, for pointcloud processing. Coverage of the most common filters, readers and writers along with some general introduction on the library, coverage of processing models, language bindings and command line based batch processing. First part will be covering new features for current users. Some discussion of installation method including Docker, binaries from package repositories, and Conda packaging. For more info see pdal.io

uk: вхід без авторизації


pygeoapi project status 2022

uk: Комп'ютерні науки

description: pygeoapi is an OGC API Reference Implementation. Implemented in Python, pygeoapi supports numerous OGC APIs via a core agnostic API, different web frameworks (Flask, Starlette, Django) and a fully integrated OpenAPI capability. Lightweight, easy to deploy and cloud-ready, pygeoapi's architecture facilitates publishing datasets and processes from multiple sources. The project also provides an extensible plugin framework, enabling developers to implement custom data adapters, filters and processes to meet their specific requirements and workflows. pygeoapi also supports the STAC specification in support of static data publishing. pygeoapi has a significant install base around the world, with numerous projects in academia, government and industry deployments. The project is also an OGC API Reference Implementation, lowering the barrier to publishing geospatial data for all users. This presentation will provide an update on the current status, latest developments in the project, including new core features and plugins. In addition, the presentation will highlight key projects using pygeoapi for geospatial data discovery, access and visualization.

uk: вхід без авторизації


State of OSM in QGIS

uk: Комп'ютерні науки

description: QGIS is one of the most used Open-Source GIS Software. It is possible to display, edit, analyse, process different kind of data such as vector, raster, mesh, point clouds etc. QGIS has some native functionalities to work with OSM data. Either with raster layer as a basemap, or with vector, QGIS can deal with OSM data. Depending on the amount of data to work with, the need to "refresh" the data (from the main OSM database), the extent of the coverage, different plugins or technologies are possible. This presentation will try to give an overview how it's possible to use OpenStreetMap data within QGIS according to different situations (Geocoding, TMS/WMS, Overpass-API, Docker, PostgreSQL…). The presentation will show how you can contribute to QuickOSM to add some default « map preset » to QuickOSM core on GitHub. This feature in QuickOSM allows users to have a set of vector layer with styles in QGIS which are ready to be used, with a symbology.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: The MapTiler plugin is the easiest way to load styled vector tiles into QGIS. The plugin allows anybody to easily load map data of the entire planet (from OpenStreetMap project), with details down to the street level from Cloud or any other URL. The version 3.0 of MapTiler plugin brings several new features, maps and datasets. A new global DEM of the entire planet is ideal for terrain spatial analysis. New maps - both in vector and raster - OpenStreetMap (popular OSM Carto finally in vectors!) and a Winter map for all wintertime activities. A new Satellite map based on our new 2021 cloudless satellite imagery with 10m resolution for the entire planet. The plugin offers maps of the entire world in vector or raster tiles, but can also open maps from any other URL. You can load high-resolution aerial imagery, hillshading, global terrain data and contour lines for outdoor maps or official government open data from various countries. A ready-to-use list of beautiful map styles is available to QGIS users. Those who prefer customized maps can make their own map design in a few clicks using the Customize tool. Users can set their own colors, fonts, or choose the language of map labels. Use the power of QGIS and reproject, rotate and export vector tiles to various formats (including PDF, SVG or DWG) or use Print Composer to create beautiful high-detailed maps to fit your needs. The plugin is an open-source project with code available at GitHub repository and open to any contribution from developers and users.

uk: вхід без авторизації

name: QGIS MapTiler plugin v3- vector basemaps & global DEM for 3D terrain


uk: Комп'ютерні науки

description: I often hear complaints that the stackexchange sites are too mean to new users, that moderators are too quick to close a question that doesn't fit the guidelines or nitpick the questions to death. Also, that they didn't get a good answer anyway so what is the point. This talk will give you an introduction on how to ask a “good” question on gis.stackexchange.com from an experienced moderator of the site. As a bonus, you will also find out how to file a useful bug report (either internally or to an external project). This talk will cover the things that you might not think are useful but are in fact vital to someone who is trying to help you. I will discuss how the site works and how to make it work well for you, what sort of questions are “good” questions and which ones are better asked somewhere else. I will also cover what the difference between closing and deleting a question is and how to get your question reopened if it is closed. How the review queues work and how you can help improve the site for other users.

uk: вхід без авторизації

name: How to get a good response on stackexchange


OpenPlains - Is it the new web GRASS?

uk: Комп'ютерні науки

description: OpenPlains - Is it the new web GRASS?

uk: вхід без авторизації


uk: Комп'ютерні науки

description: This is a technical feedback about why the COG (Cloud Optimized GeoTIFF) format is valuable outside the cloud and can speed up productivity in many ways. During first months, remote work and COVID, IT department was overbooked and has to face to many issue such bandwidth limitation. Images display was suffer in GIS client. Of course, webservice was always available but user has to control on band order or radiometry settings. WCS is supposed to be the solution. Unfortunately, it offered degraded performance. COG is supposed to be serve from HTTP server or S3. But we’ve simply test from a network drive / mount point and it offer great performance. Depending internet connection, it could be as fast as it is in local ! COG advantage must be consider outside of the cloud as remote work tends to develop more and more. It could avoid to deploy heavy webservice infrastructure for only raster visualization. From other side, benchmark between publish some other format compare to COG in GeoServer. From Regional Data Infrastructure, it’s streamline storage data between raster format as input file for webservices and opendata raw downloading services as open archives. Finally, I will give some feedback and tips and tricks to find best parameters to convert orthophotography, DEM or DSM, etc. to COG.

uk: вхід без авторизації

name: The benefits of COG (Cloud Optimized GeoTIFF) outside the cloud


uk: Комп'ютерні науки

description: *GeoBlaze (geoblaze.io)* is a blazing fast raster analysis engine written in pure JavaScript. With geoblaze, you can run computations ranging from basic statistics (min, max, mean, median, and mode) to band arithmetic and histogram generation in either a web browser or a node application. ### presentation This presentation will go over recent updates to GeoBlaze, including the addition of support for Cloud-Optimized GeoTIFFs. We will also discuss the roadmap for the next couple years. ### use cases GeoBlaze can be used wherever vectors and rasters meet. You can use it to calculate the hectares of wheat in a country, the change in daily median earth temperature, and identify wildfires in satellite imagery. ### environment Because GeoBlaze is written in pure JavaScript it can be run in various environments, on an EC2 server, Lambda function, Cloudflare worker, or in the browser. It performs calculations using the CPU, so it is not restricted only to environments where a GPU is available. ### notable dependencies GeoBlaze is built on top of the following open-source projects: dufour-peyton-intersection (github.com/GeoTIFF/dufour-peyton-intersection), georaster (github.com/geotiff/georaster), geotiffjs (github.com/geotiffjs/geotiff.js), and calc-image-stats (github.com/danieljdufour/calc-image-stats). ### sample notebooks: - Time Series Analysis with GeoBlaze: Mean Daily Air Temperature for the Month of May: observablehq.com/@geosurge/time-series-analysis-with-geoblaze-mean-daily-air-temperat - Identifying Carr Wildfire with Landsat 8: observablehq.com/@geosurge/identifying-carr-wildfire-with-landsat-8 - Hectares of Rainfed Wheat in Ukraine: observablehq.com/@danieljdufour/hectares-of-rainfed-wheat-in-ukraine

uk: вхід без авторизації

name: State of GeoBlaze: A Blazing Faster Raster Analysis Engine in Pure JavaScript


uk: Комп'ютерні науки

description: WebGIS publishing platforms like MapStore, are usually very feature-rich, to cover a lot of different scenarios, from the QGIS-like do-it-all web application, to a simple interactive map for a company website. This comes with an important trade-off: even when removing most of the unneeded functionalities for the simplest use case, the cost of the platform needed to run your maps can be overwhelming. The problem here is that a single platform is not always the best choice for every kind of usage. This talk shows how to use the popular MapStore platform as an application builder, to publish interactive maps that can run on a very light engine, de facto scaling down a quite heavy platform to the needs of performance and simplicity that better suit a lot of general user oriented applications and websites. We will start by creating a map from different data sources, using MapStore, then we will export the map and publish it to our alternative light engine. We will then highlight all the advantages this approach can offer. Finally we will give some insights on the technical aspects of this project.

uk: вхід без авторизації

name: Scaling down web mapping: Find the tool that fits your needs!


uk: Комп'ютерні науки

description: Three dimensional representations of surface terrain and structure is essential for a range of widespread applications and forms a base dataset that underlies many decision making processes. A few examples include land use planning, areal overview, operational analysis, emergency handling, route and transport planning, geographical and meteorological modelling etc. Recently, the Norwegian Government and the Norwegian Mapping Authority tasked the acquisition of high resolution Light Detection and Ranging (LIDAR) data covering the entire mainland with a minimum of 2 point measurements per meter. In addition, all aerial lidar acquisitions that were tasked by the government since the early 2000s are also publically available for download. In this work using FOSS, we discuss the height accuracy of ground classified datasets (i.e. Digital Terrain Models, Digital Surface Models) with varying original acquisition ground point densities. We create classification pipelines that allow us to calculate derivative products such as a “normalized” vegetation density and further compare these over time. This work in progress discusses our experience with open source tools on open source data and some of the challenges we encountered scaling our methods for big data.

uk: вхід без авторизації

name: Lidar classification, accuracy and change detection using the Norwegian open lidar data archive.


Gleo: Reinventing WebGL maps

uk: Комп'ютерні науки

description: WebGL has enabled fast rendering of maps on the web (including MapLibreGL and OpenLayers renderers), but from the software development point of view, is a notoriously cumbersome technology to work with. This session introduces Gleo, a JavaScript+WebGL map display library aiming to cover similar use cases than Leaflet, OpenLayers, MapZen and MapLibreGL. A few architectural features of Gleo will be outlined, including: - "One GL shader per type of cartographic symbol" rendering & framebuffer compositing approach - Object-oriented design: symbols as instances; allocation/deallocation of GPU resources for each symbol - ES6 javascript features: classes, modules, private fields; symbol as DOM EventTarget; deprecation of mouse/touch events in favour of pointer events - Sliding window algorithm in a wrapped WebGL texture for tile caching - On-the-fly reprojection enabled by updating just one WebGL data structure - On-the-fly CRS offsetting to prevent floating-point precision artifacts - Coordinate wrapping and display tessellation to avoid antimeridian artifacts

uk: вхід без авторизації


uk: Комп'ютерні науки

description: OTBTF is a remote module of the Orfeo ToolBox enabling deep learning with remote sensing images. Created in 2018, it aimed to provide a generic framework for various kind of raster-oriented deep-learning based applications. Originally, OTBTF included user-oriented applications for patches sampling, model training, and inference on real world remote sensing images, and a few python scripts to help users with no coding skills to generate some ready-to-use models. A few years later, it has been used for a wide range of applications, like landcover mapping at country scale, super-resolution, optical image cloud removal, etc. This talk will present a few selected IA based applications powered by OTBTF in the framework of research projects, public policies support, or teaching. We will present the recent features added in OTBTF and we are very happy to introduce what is next! More details on the project on the github repository: github.com/remicres/otbtf remi.cresson@inrae.fr

uk: вхід без авторизації

name: Status of OTBTF, the Orfeo ToolBox extension for deep learning


uk: Комп'ютерні науки

description: The process of drawing new political boundaries in representative democracies has generally been done with closed source software. However, a number of open source products are changing the way governments draw their jurisdictions. The QGIS Redistricting Plugin has been used to redistrict communities in the United States, Canada, and Australia, and other open source software such as DistrictR has been used to redistrict the United States in their previous cycle, significantly cutting the cost needed to participate in this activity and allowing individuals to make better contributions. At its core, the software is simple but powerful: it allows users to change attributes in an attribute column using selection tools and displays aggregate statistics for other selected columns. Join John Holden, the plugin's developer, and Blake Esselstyn, a geographic and political consultant, for a plugin demonstration and a discussion of how governments and citizen groups have transitioned to using open source software in this important political area.

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name: Political Reapportionment: Drawing Boundaries with QGIS


uk: Комп'ютерні науки

description: Protomaps is a new, open source set of tools for vector cartography on the web. It’s designed to enable projects of any scale - from hobby projects of a neighborhood, to dense datasets covering the entire planet. It finally makes it simple to both host tiles and render them using web standards, and accomplishes it in the most affordable way possible. This talk will be an overview of the entire mapping stack, driven by an ethos of simplicity. Component projects include: - The OSM Express database for syncing and querying fresh OpenStreetMap data - The PMTiles cloud-optimized archive format for serverless hosting on platforms like S3 - The Protomaps JS renderer for custom cartography on the web using Canvas 2D - The relationship to complementary projects like GDAL, Leaflet, MapLibre, Tippecanoe and FlatGeobuf I’ll also describe successes and failures in adoption among users over the past two years, as well as future development plans.

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name: Web mapping at any scale: Bite-size, full-stack cartography with Protomaps + PMTiles open source tools


uk: Комп'ютерні науки

description: Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth one can build a variety of visually pleasing, well-crafted maps with cartography or GIS software. GeoServer GeoCSS is a CSS inspired language allowing you to build maps without consuming fingertips in the process, while providing all the same abilities as SLD. In this presentation we’ll show how we have built a world political map and a world geographic map based on Natural Earth, using CSS, and shared the results on GitHub. We’ll share with you how simple, compact styles can be used to prepare a multiscale map, including: - Leveraging CSS cascading. - Building styles that respond to scales in ways that go beyond simple scale dependencies. - Various types of labeling tricks (conflict resolution and label priority, controlling label density, label placement, typography, labels in various scripts, label shields and more). - Quickly controlling colors with LessCSS inspired functions. - Building symbology using GeoServer large set of well known marks. Join this presentation for a relaxing introduction to simple and informative maps.

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name: Styling Natural Earth with GeoServer and GeoCSS

name: Open Source Geospatial Foundation (OSGeo)


uk: Комп'ютерні науки

description: With our Lizmap hosting service, we provide and monitor several hundred of QGIS servers. These QGIS Servers receive and process 3.5 million requests per week, including 3 million WMS GetMap requests. We do not control the content of these QGIS projects, which are sent by our customers on our servers. Therefore, we need to deal with projects having some various kind of issues. Some QGIS projects can have very heavy SQL views which are slow to load. Our infrastructure may host projects having hundreds of layers with complex symbology. Users can publish QGIS PDF layouts (A4 and A3) with custom logos etc. This can lead to memory problems. GIS technicians can add different data sources : vector and raster files, PostgreSQL / PostGIS database, OGC WMS, WFS and WMTS web services into these QGIS projects. We need to ensure that QGIS Server is working properly, for all customers, to execute incoming requests when some external Web Services providers are too slow to respond or are temporarily offline. We need to take care of possible errors propagated by these projects. In some circumstances, we have about 10 thousand errors per week coming from QGIS server. The goal of this presentation is to give an overview of what QGIS Server can experience into the wild and what we need to do to make the Lizmap user experience the best possible: monitoring, proxy, caching.

uk: вхід без авторизації

name: QGIS Server into the wild: Feedback from the 3Liz team


pycsw project status 2022

uk: Комп'ютерні науки

description: pycsw is an OGC CSW server implementation written in Python and is an official OSGeo Project. pycsw implements clause 10 HTTP protocol binding - Catalogue Services for the Web, CSW of the OpenGIS Catalogue Service Implementation Specification, version 3.0.0 and 2.0.2. pycsw allows for the publishing and discovery of geospatial metadata, providing a standards-based metadata and catalogue component of spatial data infrastructures. The project is certified OGC Compliant, and is an OGC Reference Implementation. The project currently powers numerous high profile catalogues such as IOOS, NGDS, NOAA, US Department of State, US Department of Interior, geodata.gov.gr, Met Norway and WMO WOUDC. This session starts with a status report of the project, followed by an open question answer session to give a chance to users to interact with members of the pycsw project team. This session will cover how the project PSC operates, the current project roadmap, and recent enhancements focused on ESA's EOEPCA, Open Science Data Catalogue and OGC API - Records.

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uk: Комп'ютерні науки

description: The Apache Arrow (arrow.apache.org/) project specifies a standardized language-independent columnar memory format. It enables shared computational libraries, zero-copy shared memory, streaming messaging and interprocess communication without serialization overhead, etc. Nowadays, Apache Arrow is supported by many programming languages. Geospatial data often comes in tabular format, with one (or multiple) column with feature geometries and additional columns with feature attributes. This is a perfect match for Apache Arrow. Defining a standard and efficient way to store geospatial data in the Arrow memory layout (github.com/geopandas/geo-arrow-spec/) can help interoperability between different tools and enables us to tap into the full Apache Arrow ecosystem: - Efficient, columnar data formats. Apache Arrow contains an implementation of the Apache Parquet file format, and thus gives us access to GeoParquet (github.com/opengeospatial/geoparquet) and functionalities to interact with this format in partitioned and/or cloud datasets. - The Apache Arrow project includes several mechanisms for fast data exchange (the IPC message format and Arrow Flight for transferring data between processes and machines; the C Data Interface for zero-copy sharing of data between independent runtimes running in the same process). Those mechanisms can make it easier to efficiently share data between GIS tools such as GDAL and QGIS and bindings in Python, R, Rust, with web-based applications, etc. - Several projects in the Apache Arrow community are working on high-performance query engines for computing on in-memory and bigger-than-memory data. Being able to store geospatial data in Arrow will make it possible to extend those engines with spatial queries.

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name: Geospatial and Apache Arrow: accelerating geospatial data exchange and compute


uk: Комп'ютерні науки

description: I am working as the Technical Lead at Blue Sky Analytics, a climate-tech startup empowering the world’s decision-makers with accurate, real-time, and standardized climate data. All datasets that we are building here at Blue Sky Analytics, technically have one similarity - they all have a space and time component. We tried to build solutions like filling empty values in inconsistent temporal data, and dividing the data in specified time period chunks for faster queries, while these worked as POC, they were not easy to scale up. Working with structured data was much easier to understand, working on postgres with the addition of timescale and PostGIS gave us exactly what was needed. Building the solution at the database level with the existing open-source technologies has been an exhilarating experience. Imagine a dataset with hourly frequency going back years on a global level, with frequent inconsistencies, that not only you have to efficiently store but that should also be highly accessible in combination with other such datasets. If not for the open-source, we would not have been able to answer questions like: - How much have the lakes shrunk between the years 2010-2020 on a yearly basis? - Finding GHG emissions from biomass burning of "*all US states, for the last 10 years on a monthly, weekly, daily basis***".** Leveraging other open source solutions like h3-pg indexing also helped us to reduce the query time by an exponential factor for global level queries! While the database sounds pretty amazing, another challenge was putting it all together and deploying it on the cloud, which was a whole another challenge. The most intuitive solution was to deploy a bunch of Postgres instances. While it was not so hard to implement the basics, it became almost impossible to scale up or down, install rolling updates, account for failures. Keeping up with the tech, Kubernetes seemed like a great solution for building a high availability cluster service, and finding the postgres-operator (PGO) by crunchydata was exactly what we needed. It combined all the right tools like pgBouncer, pgBackRest, and monitoring solution using grafana and Prometheus all in one packaged easily to deploy service. While the learning curve with Kubernetes was a little steep, it lead to building a highly scalable and resilient database cluster. The PostgreSQL + PostGIS + Timescale + H3 stack helped us simulate the temporal and spatial nature of the world at the database level and gave a universal approach to store and query all our datasets. It can handle textual data like fires with time (recorded time) and spatial information (lat -long) or shapes of counties, water bodies, etc., and combine them with each other using few joins giving us a very powerful geospatial-temporal query engine. Without FOSS it would have been impossible to even imagine any of this but as of now, we are quantifying climate change!

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name: Spatio-temporal Database - Creating a high availability easily scalable Spatio-temporal database cluster with Postgres, PostGIS, and timescaleDB!


uk: Комп'ютерні науки

description: Work started at the end of 2019 to integrate MapStore (mapstore.readthedocs.io/en/latest/) as a WebGIS viewer (mapstore.geosolutionsgroup.com/mapstore/#/) for the geOrchestra SDI (georchestra.org/) (a free, open source, modular and interoperable Spatial Data Infrastructure software born in 2009 to meet the requirements of the INSPIRE directive in Europe). The work, led by GeoSolutions (geosolutionsgroup.com/), was funded by Rennes Mètropole (metropole.rennes.fr/) with the goal to meet the expectations of the large geOrchestra community for a new, more ergonomic, modular and customizable WebGIS based on updated technologies. The project also triggered a significant evolution of the MapStore product by developing several interesting new tools and enhancements to the MapStore framework. Thanks also to this powerful integration MapStore significantly increases its strengths by opening the door to further and more advanced developments and evolutions. Below is a list of main enhancements and new features that have been part of the integration: - Application Context Manager: an administrative tool designed to build and configure MapStore's viewers - General evolutions of common existing tools in MapStore to enrich the user experience: Map viewport enhancements, CRSs management, TOC, translations, styling of layers, advanced measure tool, layer metadata, various catalog tool extensions to support additional data sources (like TMS, WFS etc), Attribute Table enhancements for the editing mode and more - Enhancements on the MapStore security tier aimed to the integration - Extension Manager: extensions are plugins that can be distributed as a separate package (a zip file), and be installed, activated and used at runtime in an existing MapStore installation - MapStore Data Directory: to make more portable and manageable the MapStore configuration and installed extensions The first integration received positive feedback from the geOrchestra community but also from those who were already using MapStore as a standalone application as well as from GeoNode users. Thanks to MapStore, some application flows have also been strengthened and consolidated in geOrchestra; further developments made after the first integration work also had the aim of migrating other custom tools (such as Cadastrapp and Urbanisme) leveraging on the MapStore Extensions system and also the geOrchestra community has taken its own steps in this direction with the inclusion of further custom extensions for MapStore. This great collaboration is progressing fruitfully even today where further evolutions and developments are expected for 2022, including new features and functionalities for MapStore.

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name: MapStore and geOrchestra, a match made in heaven


uk: Комп'ютерні науки

description: The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (maap-project.org) and ESA (esa-maap.org/) landing pages.

uk: вхід без авторизації

name: Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)


uk: Комп'ютерні науки

description: Workflow engines like Apache Airflow are commonly used in data engineering nowadays. They provide an infrastructure for setting up, executing and monitoring a defined sequence of tasks, arranged as a workflow application. Tasks and dependencies are defined in a declarative way or in a programming language like Python. Airflow established using directed acyclic graphs (DAGs) to manage workflow orchestration. This talk compares a selected subset out of the huge number of available Open Source workflow engines, which are especially suited for workflows containing spatial data processing. It compares the well known Apache Airflow engine with Dagster, an other solution using DAGs and a BPMN-based workflow engine using Celery as distributed task queue. In the same space there is the new OGC API - Processes standard which is a modern REST API for wrapping computational tasks into executable processes. This talk gives an overview of the API and shows possible integrations with available workflow engines.

uk: вхід без авторизації

name: Spatial data processing with workflow engines


OpenAerialMap V2 Design and Development

keywords:
foss4g2022
generaltrack
Stateofsoftware
Benjamin, DK

uk: Комп'ютерні науки

description: OpenAerialMap.org (OAM) was built in 2015 to serve as a platform and tools for sharing openly licensed satellite and aerial imagery. For Humanitarian OpenStreetMap Team (HOT) and its partners, open imagery has been critical for disaster response and preparedness mapping projects. Those images have traditionally been difficult to share and access because of the large file sizes and technical skills required to publish them online. Since its inception, OAM provides an easy means of contributing to and accessing a large repository of open imagery, with over 11,000 images added. The OAM browser is designed to easily index, visualize and filter images, while the data itself is stored in Cloud Optimized GeoTIFF (COG) format in the Open Imagery Network, a federated network of highly available imagery buckets from different cloud providers. OpenAerialMap is the only platform built on open-source software that allows anyone to upload and share aerial imagery of anywhere on Earth. With advances in drone mapping technologies and their proliferation in places where cost and access used to be a limiting factor, there are now massive amounts of images that can be easily made available through OAM. Once uploaded, all imagery becomes instantly accessible via scalable TMS and WMTS services for mapping in OpenStreetMap or for any other use. Since its creation, OAM has been democratizing high resolution Earth observation and promoting the sharing of aerial imagery through open data licenses. This year HOT joined with Kontur to take a fresh look at OAM and redesign the platform. Using modern, equitable, human-centered design principles, we evaluated how this tool could be used to better support HOT’s vision that everyone has access to high quality map data and can use that data responsibly to improve their lives and their communities. The development will build on and integrate emerging standards for geospatial data such as the Spatio-Temporal Asset Catalog (STAC) specification. A broad range of users and stakeholders will be involved in the design process, to ensure that OAM v2 will result in a modern and accessible platform. In this talk we will present an update on the development of OAM as informed by the results of that design work and share a preview of its implementation using open-source geospatial software. The need for access to open imagery has never been greater, with advances in UAS imaging and processing technologies and their proliferation in places where cost and access are a major factor. This year HOTOSM joined with Kontur to take a fresh look at OAM and redesign the tool with modern, equitable, human-centered design principles to better support HOTOSM’s vision that everyone has access to high quality map data and can use that data responsibly to improve their lives and their communities. In this talk we will present an update on the development of OAM as informed by the results of that design work.

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uk: Комп'ютерні науки

description: In Postgis Topology a merge of two surfaces does not involve spatial operations, since the surface to border relation has foreign key structures in the database. This means that the border of the new object is spatially not touched/changed when two surfaces are merged. With simple feature the common border must be computed on the fly, which again may involve snapTo and cause tiny overlaps and gaps. With Postgis Topology you can easily make an API where the client only sends new borders which is a key issue to secure data integrity. This secures that old border are not are not moved by a client error or the by simple transport format, because existing points are never not passed back to the server. Postgis Topology makes it easy for the server to work with those new borders(delta), because there are standard methods for this in Postgis Topology and all relations between border and surfaces are stored in the database. Postgis Topology also has validation routines in addition to using standard database constraints to secure a healthy system. The principles that Postgis Topology is based on was used in spatial system many years ago, but one problem was to keep the border line work nice and clean and not end up in a spaghetti. So one of the first things we did together with Sandro Santilli was to create methods on top of Postgis Topology to avoid this, by throwing away any border parts that does not contribute to a new “valid” surface. Postgis Topology is built on a relational database model that is based on SQL-MM part 3. Your own domain data are easily linked to border, surface objects with more. For instance to check domain attributes on a surface on the other side of a border is not spatial query but a standard relational query. The following projects will also be touched in this talk: gitlab.com/nibioopensource/pgtopo_update_sql (Functions using Postgis Topology to make it easy to create spatial update clients.) github.com/strk/qgis_pgis_topoedit (Postgis Topology is very well integrated with QGIS.) github.com/larsop/resolve-overlap-and-gap (Show how we clean up, simplify, generalize simple feature tables with millions of rows using Postgis Topology) Is relational database structure a good choice for Postgis Topology? Yes I will mean and since it’s also linked up SQL-MM part3 and not a random private structure and with all great Postgis functions available this is very good combination. You may take take glance at ibm.com/ibm/history/ibm100/us/en/icons/reldb/ and other articles about relational databases. The plan now is to build a full ecosystem around Postgis Topology with a generic client to support declarative rules, where you can define attributes, rules for attribute handling and how to deal with overlap and gap. All the work NIBIO has done/is doing here would not have been possible with out the great support from Sandro Santilli.

uk: вхід без авторизації

name: Postgis Topology to secure data integrity, simple API and clean up messy simple feature datasets.


uk: Комп'ютерні науки

description: This presentation will show tips and tricks how to design dynamic and relatively complex forms in QGIS desktop - with the help of the drag and drop form designer, widget configurations, dynamic expressions, data-defined widget visibility, default values, constraints, embedded forms, relations, actions and more. In addition, we will show how you can use spatial joins from other layers to automatically fill in data from independent but spatially related layers. You will be walked through an application developed for the management of biodiversity subsidies in the Kanton of Solothurn, Switzerland. The application allows to collect data from eligible areas in the canton's biodiversity programme. Farmers and foresters can apply for separate subsidies for biodiversity support if the areas and their management methods meet certain criteria. The QGIS based application allows to collect data, automatically assigns parcel numbers, place names, community names, etc. and allows to define usage restrictions and record maintenance measures. Interfaces exist for a reporting generator (contract generation) and an SAP based disbursement system for the payment of subsidies. In the presentation we will present the result of the development work and show some tips and tricks with forms, widgets, expressions and actions and how we stitched everything together.

uk: вхід без авторизації

name: Designing dynamic forms in QGIS Desktop with expressions


uk: Комп'ютерні науки

description: The proliferation of client-side analytics and on-going vulnerabilities with shared code libraries have fueled the need for better safety standards for running executables from potentially unknown sources. WebAssembly (WASM), a compilation target that allows lower-level languages like Rust, C, and Go to run in the browser or server-side at near-native speeds. Much like Docker changed the way we run virtualized workflows, WASM runtimes create safe virtual environments where access to the host system is limited. In combination with a new free and open source full-stack geospatial platform, Matico, efforts are underway to enable portability across workflows and applications to more easily use WASM modules. WASM implementations of GDAL are in the works, and powerful open source Rust geospatial libraries are easily packaged for web usage through Wasm-Pack. Additional geo WASM libraries like jsgeoda provide spatial indices, binning, and autocorrelation functions. Shareable code can be a recipe for security vulnerabilities and attack vectors, potentially exposing personal or critical information, particularly if there is the opportunity to run code server-side. WASM implementation alleviates this by requiring access from the Virtual Machine (VM) to be limited and explicit, and for Javascript developers the lightweight AssemblyScript language is relatively familiar. An upcoming Javascript feature called ShadowRealms may enable even simpler and more familiar implementations to safely run Javascript code shared between module authors. These developments lay the groundwork for a hybrid front- and backend geospatial ecosystem of shareable code snippets and analytic functions, much like have emerged in the UI component Javascript ecosystem. The combination of emerging features positions web geospatial analytics and This talk explores the implementation and performance of running geospatial analytic modules through a WebAssembly virtual machine and through the upcoming Javascript ShadowRealm specification.

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name: Write once, run anywhere: safe and reusable analytic modules for WebAssembly, Javascript, or more!