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Category Archive for 'Land use'

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to [...]

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The Horizon 2020 LandSense project was concluded successful. Please find a selection of the produced publications and deliverables here. The project has enabled our group to pursue quality aspects of voluntarily collected geo information data and to ramp up efforts related to OSMlanduse.
Together with the University of Nottingham (Giles Foody) and Institut national de l’information géographique [...]

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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 [...]

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As a user‐generated map of the whole world, OpenStreetMap (OSM) provides valuable information about the natural and built environment. However, the spatial heterogeneity of the data due to cultural differences and the spatially varying mapping process makes the extraction of reliable information difficult. A newly published study investigates the variability of association rules extracted from [...]

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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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Land use decision involve trade-off with respect to the ecosystem service produced by the different land use systems. Agricultural use versus natural ecosystems is the main alternative for which trade-offs need to be estimated. Given the varying climatic and soil conditions this trade-off differs in space. Therefore, it is essential how [...]

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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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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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Land use change in Brazil is often associated with a loss of natural habitat (Amazon, Cerrado, Pantanal,…) driven by farmland expansion. A current analysis by a team of Brazilian and German researchers (involving HeiGIT member Sven Lautenbach) shows that for Rio de Janeiro state farmland abandonment has been an important process. Approximately 1 million hectares [...]

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