Tag: deep neural networks
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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? Das ist auch unsere Mission! Die HeiGIT gGmbH ist ein forschungsorientiertes, gemeinnütziges Start-up mit den…
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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 are based on more than 7k reference points collected in the past month through a…
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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 of information. The mapathon comes in four themes: nature, urban, agriculture or expert. While the expert…
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Contiguous high resolution OSMlanduse map of the European Union by combining Copernicus data and OpenStreetMap
Find here a new update of the OSMlanduse.org map. By injecting known tags provided by OpenStreetMap (OSM) into a remote sensing feature space using deep learning, tags were predicted when absent thus creating a contiguous map – initially for the member states of the EU. By design our method can be applied when- and wherever…
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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 copy here and enjoy a nice summer reading: Li, H., B. Herfort, W. Huang, M. Zia,…
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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 journals main page. Novack T, Vorbeck L, Lorei H, Zipf A. (2020): Towards Detecting Building Facades…
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Classification of 3D ALS Point Clouds using End-To-End Deep Learning
In a new publication, we show how deep neural networks can be used in an end-to-end manner for the classification of 3D point clouds from airborne laser scan data. The research, based on the award-winning diploma thesis of Lukas Winiwarter at TU Wien, has now been published in “PFG – Photogrammetrie, Fernerkundung, Geoinformation“, the Journal…
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Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks
Recently a new paper about Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks (DNNs) has been presented at the AGILE GIScience conference 2019 in Cyprus. Although built-up areas cover only a small proportion of the earth’s surface, these areas are closely tied to most of the world’s population and the economic output, which makes…
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Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping
Our paper about Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping is available online now. 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…
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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 solve humanitarian mapping tasks of a mobile software named MapSwipe. The current deep learning techniques…