Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Jens Bongartz"'
Autor:
Edvinas Rommel, Laura Giese, Katharina Fricke, Frederik Kathöfer, Maike Heuner, Tina Mölter, Paul Deffert, Maryam Asgari, Paul Näthe, Filip Dzunic, Gilles Rock, Jens Bongartz, Andreas Burkart, Ina Quick, Uwe Schröder, Björn Baschek
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 954 (2022)
Riparian zones fulfill diverse ecological and economic functions. Sustainable management requires detailed spatial information about vegetation and hydromorphological properties. In this study, we propose a machine learning classification workflow to
Externí odkaz:
https://doaj.org/article/9e00b29ea3444b00b7f88a0b7d30232d
Autor:
Paul Naethe, Maryam Asgari, Caspar Kneer, Michel Knieps, Alexander Jenal, Immanuel Weber, Tina Moelter, Filip Dzunic, Paul Deffert, Edvinas Rommel, Michael Delaney, Björn Baschek, Gilles Rock, Jens Bongartz, Andreas Burkart
Publikováno v:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 91:43-58
Autor:
Katharina Fricke, Björn Baschek, Alexander Jenal, Caspar Kneer, Immanuel Weber, Jens Bongartz, Jens Wyrwa, Andreas Schöl
Publikováno v:
Remote Sensing, Vol 13, Iss 8, p 1489 (2021)
Over the Hahnöfer Nebenelbe, a part of the Elbe estuary near Hamburg, Germany, a combined aerial survey with an unmanned aerial system (UAV) and a gyrocopter was conducted to acquire information about the water surface temperatures. The water temper
Externí odkaz:
https://doaj.org/article/46826cbf334140c3bac7e9dca9ffc161
Autor:
Alexander Jenal, Hubert Hüging, Hella Ellen Ahrends, Andreas Bolten, Jens Bongartz, Georg Bareth
Publikováno v:
Remote Sensing, Vol 13, Iss 9, p 1697 (2021)
UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNI
Externí odkaz:
https://doaj.org/article/2685346c58864de5ac552b0ecd0d5d7c
Publikováno v:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 90:93-101
Airborne remote sensing with optical sensor systems is an essential tool for a variety of environmental monitoring applications. Depending on the size of the area to be monitored, either unmanned (UAVs) or manned aircraft are more suitable. For surve
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 175:158-170
Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A majo
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Autor:
Andreas Bolten, Martin L. Gnyp, Jörg Dr.rer.nat. Jasper, Ulrike Lussem, Georg Bareth, Jens Bongartz, Alexander Jenal, Jürgen Schellberg
Publikováno v:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 88:493-507
Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 917-924 (2020)
Detecting objects in aerial images is an important task in different environmental and infrastructure-related applications. Deep learning object detectors like RetinaNet offer decent detection performance; however, they require a large amount of anno
Publikováno v:
IGARSS
In order to enable the development of powerful machine learning methods for remote sensing-based Earth observation tasks, benchmarks are needed to evaluate the methods and compare them to other methods comprehensively. We present ArtifiVe-Potsdam, a