Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Ksenia Bittner"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 133, Iss , Pp 104096- (2024)
Building models are important for urban studies. Remote sensing multi-spectral (MS) images are widely used for its rich semantic information. The lack of geometry features is fulfilled by introducing photogrammetry derived digital surface models (DSM
Externí odkaz:
https://doaj.org/article/1bdcfde074f242c1adb99cb5c8ad7af4
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7186-7200 (2023)
Urban areas predominantly consist of complex building structures, which are assembled of multiple building sections. From very high resolution remote sensing imagery, not only roof-tops but also the separation lines between them are visible. Since fu
Externí odkaz:
https://doaj.org/article/9a3610bb09bd45f290702b5711be0f92
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 11, Iss 8, Pp 2615-2629 (2018)
Automatic building extraction and delineation from high-resolution satellite imagery is an important but very challenging task, due to the extremely large diversity of building appearances. Nowadays, it is possible to use multiple high-resolution rem
Externí odkaz:
https://doaj.org/article/474cb31666f748ee8f220003f9d60c09
Publikováno v:
Remote Sensing, Vol 11, Iss 11, p 1262 (2019)
Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and predictio
Externí odkaz:
https://doaj.org/article/df93439408e4464ea587bd5ed8120d3c
Autor:
Philipp Schuegraf, Ksenia Bittner
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 8, Iss 4, p 191 (2019)
Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatica
Externí odkaz:
https://doaj.org/article/a455f09789d24be3a5bdd38c53b5e1d0
Publikováno v:
Remote Sensing, Vol 10, Iss 12, p 1926 (2018)
A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, exis
Externí odkaz:
https://doaj.org/article/52ec3e4efd124d1fb16bebc2551ec54d
Publikováno v:
CVPR Workshops
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and panchromatic (PAN) image as input, we first filter out non-building objec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59dd7f13766a8d93e112687fe68ce5b3
http://arxiv.org/abs/2104.06485
http://arxiv.org/abs/2104.06485
Autor:
Miguel Pato, Peter Reinartz, Pablo d'Angelo, Nina Merkle, Jiaojiao Tian, Reza Bahmanyar, Ksenia Bittner, Maximilian Langheinrich, Kevin Alonso, Corentin Henry, Guichen Zhang, Xiangtian Yuan, Stefan Auer, Daniele Cerra
Publikováno v:
IGARSS
This paper describes the contribution of the DLR team ranking 2nd in Track 2 of the 2020 IEEE GRSS Data Fusion Contest. The semantic classification of multimodal earth observation data proposed is based on the refinement of low resolution MODIS label
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c80e126ffefecd87badf4668ee255590
https://doi.org/10.1109/igarss39084.2020.9547209
https://doi.org/10.1109/igarss39084.2020.9547209
Publikováno v:
IGARSS
In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed1018767594a5ef22ee34a8118ff0a9
https://elib.dlr.de/138187/
https://elib.dlr.de/138187/
Autor:
Ksenia Bittner, Peter Reinartz, Miguel Pato, Jiaojiao Tian, Maximilian Langheinrich, Stefan Auer, Kevin Alonso, Pablo d'Angelo, Guichen Zhang, Nina Merkle, Reza Bahmanyar, Xiangtian Yuan, Daniele Cerra, Corentin Henry
Publikováno v:
IGARSS
This paper describes the contribution of the DLR team ranking 3rd in Track 1 of the 2020 IEEE GRSS Data Fusion Contest, with results ranking 2nd in Track 2 of the same contest being reported in a companion paper. The classifications are based on refi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19da0f6c623d7a5753155dd817db1b17
https://elib.dlr.de/138093/
https://elib.dlr.de/138093/