Search results for: “machine learning”
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Inferencing indoor room semantics using random forest and relational graph convolutional networks (deep learning)
Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but often still neglect room usage. To mitigate the issue, a new published paper…
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MAN & MACHINE portrayed in the Forschungsmagazin “Ruperto Carola”
Can the human brain be described as a kind of machine – and, by extension, human memory as a time machine? What do software and the human mind have in common, and what are the ethical dilemmas involved in the use of artificial intelligence (AI)? Neurobiologist Hannah Monyer and geoinformatics expert Bernhard Höfle present talking…
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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 deep learning technologies to develop smart GIServices and to address remote sensing tasks, for instance,…
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New DFG project: IdealVGI – Deep Learning with OSM
Recently a new DFG project proposal was accepted to the GIScience Research Group Heidelberg within the DFG priority programme VisVGI (Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” [SPP 1894]). It is joint collaboration project together with Prof. Begüm Demir from TU Berlin. IDEAL-VGI: Information Discovery from Big Earth Observation Data Archives by Learning from…
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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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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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Chasing Permafrost: Insights from the Aklavik Expedition
In 2022, we embarked on an insightful expedition to Aklavik, Canada, in collaboration with our partners, the Alfred Wegener Institute (AWI) Helmholtz Center for Polar and Marine Research and the German Aerospace Center (DLR). This joint effort within the UndercoverEinAgenten project aimed to study the extent and velocity of permafrost thawing, a crucial aspect of…
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IDEAL-VGI: Analyzing and Improving the Quality and Fitness for Purpose of OpenStreetMap as Labels in Remote Sensing Applications
We are happy to announce that the IDEAL-VGI project by GIScience has been successfully completed. IDEAL-VGI was a tandem project in cooperation with Begüm Demir from the TU Berlin and was conducted under the umbrella of the VGIscience Second Phase Projects which ran from 2020 to 2022. VGIscience received funding as a Priority Programme by…