News of Heidelberg University’s GIScience Research Group.
Posted in Events on Dec 7th, 2019
The Center for Spatial Studies, Department of Geography at the University of California, Santa Barbara is hosting the Spatial Data Science Symposium 2019 this coming week with the title
“Setting the Spatial Data Science Agenda”
Over 40 selected participants will gather to discuss the future of Spatial Data Science at this expert meeting. Instead of being [...]
Posted in OSM, Press release, Research, VGI Group on Nov 4th, 2019
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 Volunteered [...]
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 Publications, Research, VGI Group on Jul 31st, 2019
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 and [...]
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 Lidar Group, Publications, Research on Jun 14th, 2019
This week, the 3DGeo participated in the ISPRS Geospatial Week 2019 with two presentations among the sessions of the Laser Scanning Workshop with many interesting talks and poster.
Presentations were given by Ashutosh Kumar in the Machine Learning Session and Katharina Anders in the Change Detection Session.
Highlight: The work by Ashutosh Kumar on feature relevance in [...]
Posted in 3D, Lidar Group, Publications, Research on May 31st, 2019
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 [...]
Posted in 3D, Digital Earth, Events, Lidar Group, Research on May 17th, 2019
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 [...]
Humanitarian organizations can’t help people if they can’t find them.
This was the simple reason to create MapSwipe back in 2016 and it is still as pressing as in the very beginning. In the last 2,5 years volunteers have contributed more than 18,000,000 results, which help humanitarian organizations to create maps of human [...]
Posted in Publications on Oct 16th, 2018
Our feature paper “Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks” is now published online.
Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images [...]