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Tag Archive 'deep learning'

Stellenausschreibung Universität Heidelberg – GIScience
Wissenschaftliche Mitarbeiter:in Geoinformatik - Projekt GeCO

GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion
Du hast Interesse an Klimawandel, Treibhausgasemissionen und innovativen Geoinformatik-Methoden?
Im Rahmen des vom Heidelberg Center for the Environment (HCE) durch die Exzellenzstrategie geförderten Kooperationsprojektes GeCO suchen wir baldmöglichst nach einer wissenschaftlichen Mitarbeiter:in (m/f/d). Die Abteilung Geoinformatik entwickelt [...]

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Recently a new project has been starting in the context of Climate Change Action research:
GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion and atmospheric transport modelling
The spatiotemporal distribution of greenhouse gases and their sources on Earth has so far been considered mainly at relatively coarse resolutions. There is a lack of sound [...]

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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 natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems.
In a [...]

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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 virtual conference. As in earlier years GIScience Heidelberg and HeiGIT are contributing to the conference with several [...]

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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 our recent work in detecting OpenStreetMap missing buildings, so this time we will show you [...]

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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 place virually on June 8 to 11, 2021.
Accurate and complete geographic data of human settlements [...]

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Lukas Winiwarter of the 3DGeo group was invited by the Austrian Society of Surveying and Geoinformation (OVG) to give a talk on the application of deep learning on point clouds, which took place on March 24. In his talk, Lukas presented four different state-of-the-art approaches to consider the irregular, unordered structure of point clouds, which [...]

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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 are based on more than 7k reference points collected in the past month through a [...]

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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 sowie Firmen und Institutionen im Bereich Flächenmanagement mit Fokus auf die Siedlungs- und Verkehrsentwicklung und [...]

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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 of information. The mapathon comes in four themes: nature, urban, agriculture or expert.
While the expert campaign [...]

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