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Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning

Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning (ML). High-resolution maps of inland surface water bodies are important for water supply and … Read More

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery … Read More

OSMlanduse European Union validation effort EuroSDR conference 11/24/2020

During the EuroSDR workshop we will present our OSMlanduse product (earlier post) to the land use (LU) and land cover community (LC) and highlight class accuracies and a benchmark comparison towards existing national authoritative products. Accuracy estimated to be presented … Read More

OSMlanduse wird auf Geonet.MRN Meetup zu Flächennutzung und Flächenmanagement vorgestellt: Donnerstag 29.10.2020, 16:30

Am am 29.10.20, 16:30 Uhr veranstaltet das Netzwerk Geoinformation der Metropolregion Rhein-Neckar GeoNet.MRN zum Thema: Flächennutzung und Flächenmanagement: Ein Geoinformation Meetup Teilnahme: Kostenlos und ohne Anmeldung mit Teams unter diesem Link. Themen des Meetups sind die Online-Beteiligung von Kommunen, Bürgern … Read More

OSMlanduse European Union validation effort

We launched a validation campaign of our new 10meter resolution OSMlanduse product for the member states of the European Union. Please contribute to the validation here. A technique where contributions are checked against each other is implemented to promote quality … Read More

Going green with MeinGrün – Today App launch in Heidelberg and Dresden

Today the time has come: The “meinGrün” web app for Dresden and Heidelberg is officially launched. With the mobile application you can (re-)discover known and unknown green spaces and find a pleasant route to those. Users can learn about the … Read More

Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique

Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while … Read More

NASA uses Openrouteservice for study on disaster response times

According to a recent post by NASA, researchers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, calculated the time that could have been saved if ambulance drivers and other emergency responders had near-real-time information about flooded roads, using the … Read More

MS Wissenschaft beendet Tour zur Künstlichen Intelligenz – aber weiter geht es im Web – auch mit unserem Exponat zu Trainingsdaten für Satellitenbilder

Gerade beendete die MS Wissenschaft ihre Tour durch 31 Städte zwischen Berlin und Wien in diesem Wissenschaftsjahr zum Thema “Künstliche Intelligenz“. 85.000 Menschen – Schulklassen, Familien und Interessierte aller Altersklassen – besuchten die Ausstellung zum Thema lernende Computersysteme an Bord … Read More

OSMlanduse.org + Remote Sensing = New conterminous land use data set for Germany released – filling gaps in OSM through machine learning

Land use data created by humans (OSM) was fused with satellite remote sensing data, resulting in a conterminous land use data set without gaps. The first version is now available for all Germany at OSMlanduse.org. When human input (OSM data) … Read More

Deep Learning with Satellite Images and Volunteered Geographic Information

Recently, deep learning has been widely applied in pattern recognition with satellite images. Deep learning techniques like Convolutional Neural Network and Deep Belief Network have shown outstanding performance in detecting ground objects like buildings and roads, and the learnt deep … Read More