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GIScience and HeiGIT contributions to AGILE 2021 conference

The AGILE 2021 conference is taking place this week. It is the the 24rd AGILE conference on GIScience. AGILE is the Association of Geographic Information Laboratories in Europe and the 2021 conference is for the first time held as a … Read More

Automatic building detection with ohsome2label and Tensorflow

Accurate and complete geographic data of human settlement is crucial for humanitarian aid and disaster response. OpenStreetMap (OSM) can serve as a valuable source, especially for global south countries where buildings are largely unmapped. In a previous blog, we introduced … Read More

Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks

Recently, a new research paper “Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks” (Pisl, J., Li, H., Herfort, B., Lautenbach, S., Zipf, A. 2021) has been accepted at the the 24th AGILE conference 2021. The conference will take … 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

OSM Missing Areas Identification paper is featured in August by ISPRS Journal of Photogrammetry and Remote Sensing

We are pleased that our article has been selected by the editors of ISPRS Journal of Photogrammetry and Remote Sensing as the featured Article in August 2020. This means it will be available open access for 1 year. Get your … Read More

Introducing ohsome2label tool to generate training samples from OpenStreetMap for geospatial deep learning

After more than a decade of rapid development of volunteered geographic information (VGI), VGI has already become one of the most important research topics in the GIScience community. Almost in the meantime, we have witnessed the ever-fast growth of geospatial … 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

ISPRS IJGI highlights our work on deep learning of Street Art from VGI and Street View Images

We are pleased to share that because of the response to our work, ISPRS IJGI selected our paper on Detecting Graffiti with Street View Images and Deep Learning to be highlighted as a title story through some graphics on the … Read More

Invited talk at the Spatial Data Science Symposium 2019, Santa Barbara

The Center for Spatial Studies, Department of Geography at the University of California, Santa Barbara is hosting the Spatial Data Science Symposium 2019 this coming week with the title “Setting the Spatial Data Science Agenda” Over 40 selected participants will … Read More

Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning

Our new paper on Machine Learning and Humanitarian Mapping Nowadays, Machine Learning and Deep Learning approaches are steadily gaining popularity within the humanitarian (mapping) community. New tools such as the ML Enabler or the rapId editor might change the way … Read More

Put the world’s most vulnerable people on the map with MapSwipe

Humanitarian organizations can’t help people if they can’t find them. This was the simple reason to create MapSwipe back in 2016 and it is still as pressing as in the very beginning. In the last 2,5 years volunteers have contributed … Read More

Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping

Satellite images are widely applied in humanitarian mapping which labels buildings, roads and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In a recently accepted study, we utilize deep learning to … 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