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Tag Archive 'deep learning'

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 [...]

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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 [...]

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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 [...]

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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 [...]

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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 [...]

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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, [...]

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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 [...]

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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 [...]

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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 [...]

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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 [...]

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