Search results for: “deep learning”
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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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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 crowdsourced data is produced in the future. Hence, at the Heidelberg Institute for Geoinformation Technology…
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Paper on Analysis of Feature Relevance in Deep Learning for 3D Point Cloud Classification
A paper investigating the relevance of (pre-calculated) features for 3D point cloud classification using deep learning was just published in the ISPRS Annals of Photogrammetry and Remote Sensing. The study presents a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compares it with an implementation of the state-of-the-art…
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PhD Colloquium “Deep Learning in Photogrammetry, Remote Sensing and Geospatial Information Processing”
On 14th and 15th May, our 3DGeo group members Bernhard Höfle and Lukas Winiwarter were co-organizing and participating in the 4th colloquium for PhD students working on the topic of Deep Learning and its applications in Photogrammetry, Remote Sensing and Geoinformation Processing of the Deutsche Geodätische Kommission (DGK) and the Deutsche Gesellschaft für Photogrammetrie und…
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Call for Abstracts for PhD Colloquium on Deep Learning in Photogrammetry, Remote Sensing and Geospatial Information Processing
Call for Abstracts: 4th PhD Colloquium on “Deep Learning in Photogrammetry, Remote Sensing and Geospatial Information Processing” May 15-16, 2019 in Rostock The Division on Geoinformatics (Abteilung Geoinformatik) of the German Geodetic Commission (Deutsche Geodätische Kommission, DGK) and the Working Group on Geoinformatics (Arbeitskreis Geoinformatik) of the German Society for Photogrammetry, Remote Sensing and Geoinformation…
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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…
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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 features are further applied in some prediction tasks like poverty and population mapping. On the…
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DeepVGI: Deep Learning with Volunteered Geographic Information
Deep learning techniques, esp. Convolutional Neural Networks (CNNs), are now widely studied for predictive analytics with remote sensing images, which can be further applied in different domains for ground object detection, population mapping, etc. These methods usually train predicting models with the supervision of a large set of training examples. However, finding ground truths especially…
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Improving OpenStreetMap missing building detection using few-shot transfer learning in sub- Saharan Africa
OpenStreetMap (OSM) has been intensively used to support humanitarian aid activities, especially in the Global South. Its data availability in the Global South has been greatly improved via recent humanitarian mapping campaigns. However, large rural areas are still incompletely mapped. The timely provision of map data is often essential for the work of humanitarian actors…
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Job Offer: “Lead: Geo Machine Learning for Good” Senior Spatial Data Science Expert (m, f, d) 100% permanent, HeiGIT gGmbH
Job advertisement HeiGIT gGmbH Do you want to use your machine learning expertise for the benefit of society and the environment? Do you want to improve the availability and quality of geospatial data and further develop geoinformatics methods used for open, non-profit applications in the field of sustainability, mobility and humanitarian aid? That’s our mission…
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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…