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 [...]
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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 [...]
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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 [...]
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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 [...]
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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 [...]
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Posted in Research, VGI Group on Feb 25th, 2019
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 [...]
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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 [...]
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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 [...]
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Posted in Publications on Aug 16th, 2018
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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Posted in Publications, VGI Group on Jan 10th, 2018
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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