Sie sind eine hochmotivierte Person, die gerne Systeme für maschinelles Lernen und Deep Learning konzipiert und entwickelt? Sie möchten Ihr Fachwissen im Bereich des maschinellen Lernens zum Wohle der Gesellschaft und der Umwelt einsetzen? Sie wollen die Verfügbarkeit und Qualität … Read More
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Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection
Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common … Read More
Job Offer: “Lead: Geo Machine Learning for Good”, Senior Spatial Data Science Expert (m, f, d), 100%, permanent, HeiGIT gGmbH
Du willst Deine Machine Learning Kompetenz zum Wohle der Gesellschaft und Umwelt einsetzen? Du willst die Verfügbarkeit und Qualität von Geodaten verbessern und geoinformatische Methoden weiterentwickeln, die für offene, gemeinnützige Anwendungen im Bereich Nachhaltigkeit, Mobilität und humanitäre Hilfe eingesetzt werden? … Read More
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
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
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
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