News of Heidelberg University’s GIScience Research Group.
Posted in Land use, OSM, Research, Services on Nov 16th, 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 [...]
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 [...]
Posted in Land use, OSM, Research, Services on Oct 6th, 2020
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 [...]
Posted in Land use, OSM, Research, Services on Sep 25th, 2020
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 [...]
Posted in Crowd Analyser, Publications on Aug 20th, 2020
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 [...]
Posted in Events, Publications on Aug 17th, 2020
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, A. Zipf [...]
Posted in OSM, Research, Software, VGI Group on Jul 20th, 2020
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, [...]
Posted in Digital Earth, Land use, Publications, Research, VGI Group on Jun 30th, 2020
Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this recent paper, we propose a novel multi-sensor fusion framework (CResNet-AUX) for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations:
A unique adaptation of the [...]
Posted in Crowd Analyser, OSM, Publications, Research, VGI Group on Jun 8th, 2020
Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while often the availability and quality of OSM remains a major concern. The majority of existing [...]
Posted in Publications, VGI Group on Mar 11th, 2020
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 with [...]