Search results for: “deepvgi”
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DeepVGI at HPI Future SOC Lab Day
Our research member Jiaoyan Chen attended the HPI Future SOC Lab Day – Spring 2017 in Potsdam, in April 25, 2017. The HPI Future SOC (Service-Oriented Computing) Lab Day is a cooperation of the Hasso Plattner Institute (HPI) and the industrial partners Dell EMC, Fujitsu, SAP and Hewlett Packard Enterprise (HPE). Its mission is to enable and promote exchange and…
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DeepVGI at WWW2017 Perth
Our group member Jiaoyan Chen attended the 26th World Wide Web Conference from 3 April to 7 April in Perth, Australia. The topic of the conference includes web-related machine learning, social network, knowledge base, crowdsourcing, urban data mining, etc. He gave a 30-minues presentation in the co-conference Big 2017 as well as a poster presentation…
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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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Online seminar ‘Deep learning from Volunteered Geographical Information: a case study of humanitarian mapping with OpenStreetMap’
We invite to the upcoming online seminar at the Urban Analytics Lab seminar series at the National University of Singapore (NUS): ‘Deep learning from Volunteered Geographical Information: a case study of humanitarian mapping with OpenStreetMap’ on 29 April (9am German time, 3pm Singapore time) By Hao Li, GIScience Research Group, Heidelberg University @GIScienceHD As an emerging topic,…
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Where to map in OpenStreetMap next? Experiences from Mozambique, India, and Tonga
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 and due to the efforts of local communities. However, large rural areas are still incompletely mapped. The timely provision of map data is…
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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 natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems. In…
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Alexander Zipf selected as Marsilius Fellow 2021/2022 – Project with HIGH on Climate Change and Health
Many pressing problems of our time – climate change, aging societies, questions of modern medicine – cannot be solved by one discipline alone. It is becoming ever more urgent for scholars to collaborate across disciplines – natural sciences, life sciences, and social sciences, law, and humanities. The Marsilius Kolleg at Heidelberg University is an institutional…
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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 our recent work in detecting OpenStreetMap missing buildings, so this time we will show you…
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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 place virually on June 8 to 11, 2021. Accurate and complete geographic data of human…
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Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning
Determining the location of a building’s entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, in a recently published paper in…