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Tag Archive 'deep neural networks'

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 [...]

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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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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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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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We cordially invite everybody interested to our next open GIScience colloquium talk
The speaker is Lukas Winiwarter
TU Wien, Department of Geodesy and Geoinformation, Research Group Photogrammetry
When: Monday 18.06.2018, 2:15 pm
Where: INF 348, room 015 (Institute of Geography, Heidelberg University)
Classification of 3D Point Clouds using Deep Neural Networks
Per-point classification (semantic labeling) is an important step in processing topographic 3D [...]

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