Felix Delattre

This presentation will delve into the intricacies of packaging geospatial software for Debian Linux and its derivatives, including Ubuntu, OSGeoLive, and others. It will begin by contrasting the differences between packaging for an operating system and application-level package managers. The presentation will then provide an introduction to the Debian GIS Team and their established practices for packaging, including resources for finding information. The focus will then shift to the crucial steps involved in preparing the software for distribution, such as creating metadata and dependencies, building the package, testing its functionality, and ultimately making it available to end-users for easy installation and use.

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What are the perspectives and challenges around the new wealth of building data in OpenStreetMap?

OpenStreetMap (OSM) is constantly expanding into emerging fields, particularly around building datasets. Through its participatory character, OSM provides an incomplete, but global coverage of buildings, nowadays already outnumbering roads. Their distribution and properties are the best indicator to population density, economic activity patterns, and risks of natural hazards. Thus, OSM constitutes a perfect base to provide answers to many related questions of various stakeholders from communities, governments, private sector and research. We present an overview of how worldwide building data is used and will be in the near future. We will guide through our latest projects, reaching from the creation of risk models, via automated building extraction from satellite sensor data to building completeness estimates, and set these developments in context with other community-driven activities. We report on the challenges of managing global building data, and combining attributes from other open and public domain big datasets towards rich handling of building data in and around OSM. A special focus is on the ways how this new information can be made useful to support mappers to achieve higher completeness of building data.

Other people: Danijel Schorlemmer

The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been developed further significantly. This talk will give an update on the newest developments and the emerging community around the application.

The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been further developed significantly. This talk will give an update on the newest developments and the emerging community around the application. Based on tales of data storytelling representatives of the development team of the Tasking Manager are going to discover remarkable curiosities and insights out of a wider application and user analysis, of course backed by hard stats. From this starting point we explain the solutions we implemented in Tasking Manager in the last year to improve the user experience and increase the data quality by mappers using the tool. Three stories will be told, about group of users that are coming to the Tasking Manager frequently and how the changes affected their flow. New mappers, validators and project creators are the main actors of the exciting adventures of combating a low beginners retention rate, lifting data relicts, guiding a mapper through the labyrinth of OSM contribution and learning about the magic of people’s using technology to achieve what they want. We will talk about the new ingredients of the Tasking Manager, from the redesign of the users interface and the interaction among them, to how it now fits into a wider spectrum with much more applications of the OpenStreetMap ecosystem in order to improve direct access to more efficient mapping and data validation.

Other people: Wille Marcel Lima Malheiro Ramya Ragupathy

Deep learning methods for feature extraction using computer vision are giving concrete results. This talk provides an overview of feature detection from satellite imagery and how machine learning can provide a seamless mapping experience for mappers, allowing them to utilize their knowledge to enrich maps further.

Deep learning methods for feature extraction using computer vision are giving concrete results. This talk provides an overview of feature detection from satellite imagery and how machine learning can provide a seamless experience for mappers, allowing them to utilize their knowledge to enrich maps further. When using OpenStreetMap for disaster response and humanitarian action, time and data quality are critical. Automated tools around OpenStreetMap have revolutionized disaster response, allowing mappers to leverage their local knowledge to organize themselves in action and to contribute to highly relevant maps. Over the last year, Tasking Manager team at HOT conceptualized an initiative to test using data derived using machine learning models to improve task creation, quality of edits and overall experience for mappers. Within Tasking Manager Working Groups we are collaborating between different actors to test concepts directly applied to two countries: Tanzania and Uganda. Two tools in pilot phase to assist mappers are around 1) Task Creation and facilitation: identify areas that need most work and/ or need an experienced mapper based on task complexity and gap analysis 2) Assisted Mapping: leverage machines to do the heavy lifting around digitizing features, such as buildings and roads, one by one and allowing mappers to focus on their essential craft of mapping. In this talk the presenters will share their findings, learning and existing challenges with the technology.

Other people: Surabhi Singh