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 [...]
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 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 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 [...]
Posted in OSM, Publications, Research, VGI Group on Feb 5th, 2020
As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In a newly published paper, we [...]
Posted in 3D, Lidar Group, Publications, Research on Oct 2nd, 2019
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 [...]
Posted in Events, Publications, VGI Group on Jun 26th, 2019
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 the [...]
Posted in Publications on Oct 12th, 2018
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 learning [...]