Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Benjamin Bischke"'
Autor:
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, Birgit Kleinschmit
Publikováno v:
Earth System Science Data
Earth Syst. Sci. Data, 15, 681–695, 2023
Earth Syst. Sci. Data, 15, 681–695, 2023
Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::610545dcaf57c1077202824ea6734887
https://doi.org/10.5194/essd-15-681-2023
https://doi.org/10.5194/essd-15-681-2023
Autor:
Akhil Singh Rana, Caglar Senaras, Benjamin Bischke, Patrick Helber, Timothy Davis, Wanda Keersmaecker, Daniele Zanaga, Annett Wania, Ruben Van De Kerchove, Giovanni Marchisio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2be0f7e1ef3f9981ae398200098f55c1
https://doi.org/10.1002/essoar.10509476.1
https://doi.org/10.1002/essoar.10509476.1
Autor:
Marc Rußwurm, Tim G. J. Rudner, Piotr Bilinski, Ramona Pelich, Jakub Fil, Veronika Kopackova, Benjamin Bischke
Publikováno v:
AAAI
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a2020d618f52d46715b2244df4b8805
https://doi.org/10.1609/aaai.v33i01.3301702
https://doi.org/10.1609/aaai.v33i01.3301702
New catalogues of nearly daily or even intraday temporal data will soon dominate the global archives. However, there has been little exploration of artificial intelligence (AI) techniques to leverage the high cadence that is already possible to achie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9d21271b3a5d2c581192a840122004d
https://doi.org/10.5194/egusphere-egu21-13713
https://doi.org/10.5194/egusphere-egu21-13713
Publikováno v:
ICPR
Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental subtasks is semi-supervised / one-shot learning: given only the segmentation mask for the first frame, the task is to provide pixel-accurate masks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::464affccb95bf869c1be819ce2e79933
https://doi.org/10.1109/icpr48806.2021.9412409
https://doi.org/10.1109/icpr48806.2021.9412409
Publikováno v:
Proceedings of the 2021 conference on Big Data from Space
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::174a91e1cd65b0e337ea82ba0c21d780
Publikováno v:
IGARSS
In this paper, we describe a multi-scale machine learning approach to estimate socio-economic attributes of citizens based on the analysis of aerial images. To analyse the effectiveness of the proposed approach we predict building property value clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::356becab59037baa2b099452104276b2
https://doi.org/10.1109/igarss.2019.8900257
https://doi.org/10.1109/igarss.2019.8900257
Publikováno v:
IGARSS
In this paper, we analyze the feasability of learning a latent embedding space from aerial and satellite imagery in order to capture semantic properties of geographical locations. We show that deep neural network, trained with a triplet loss function
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ed509d9df0e6f6ee27f37aa29b4fa39
https://doi.org/10.1109/igarss.2019.8898305
https://doi.org/10.1109/igarss.2019.8898305
Publikováno v:
IGARSS
In this paper, we present how multi-spectral Sentinel-2 satellite images can be used in a machine learning approach based on an encoder-decoder semantic segmentation network to map human settlements. We show the effectiveness of the proposed CNN appr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ac92d033edbe166ddffc47884093725
https://doi.org/10.1109/igarss.2019.8898172
https://doi.org/10.1109/igarss.2019.8898172
In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b2af8534764eb5603dde05f86adfffe
http://www.alexandria.unisg.ch/258199/
http://www.alexandria.unisg.ch/258199/