Zobrazeno 1 - 10
of 107
pro vyhledávání: '"K. Schindl"'
Autor:
E. D. Hafner, T. Kontogianni, R. Caye Daudt, L. Oberson, J. D. Wegner, K. Schindler, Y. Bühler
Publikováno v:
The Cryosphere, Vol 18, Pp 3807-3823 (2024)
For many safety-related applications such as hazard mapping or road management, well-documented avalanche events are crucial. Nowadays, despite the variety of research directions, the available data are mostly restricted to isolated locations where t
Externí odkaz:
https://doaj.org/article/adc2d12306cc42d484508b5046d0f752
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 605-612 (2023)
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene underst
Externí odkaz:
https://doaj.org/article/36dc7c9d907045b898c3ad7299681142
Publikováno v:
Journal of Micropalaeontology, Vol 42, Pp 117-146 (2023)
Agglutinated foraminiferal assemblages of the Turonian–Coniacian from the GSSP (Global Boundary Stratotype Section and Point) of Salzgitter–Salder (Subhercynian Cretaceous Basin, Germany) and other sections, including Bielefeld–Ostwestfalendamm
Externí odkaz:
https://doaj.org/article/f55b540f959e4e30ae822bf314449b05
Publikováno v:
Natural Hazards and Earth System Sciences, Vol 23, Pp 2895-2914 (2023)
Consistent estimates of avalanche size are crucial for communicating not only among avalanche practitioners but also between avalanche forecasters and the public, for instance in public avalanche forecasts. Moreover, applications such as risk managem
Externí odkaz:
https://doaj.org/article/0ec65682a18347c38dccfc28d282e25b
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-1-2023, Pp 161-167 (2023)
Forest fires have devastating effects on biodiversity, climate, and humans. Producing detailed and reliable forest fire susceptibility maps is crucial for disaster management. Data-driven machine learning methods can be applied for forest fire suscep
Externí odkaz:
https://doaj.org/article/ba4118984519439384dec755438eefd0
Publikováno v:
The Cryosphere, Vol 16, Pp 3517-3530 (2022)
Spatially dense and continuous information on avalanche occurrences is crucial for numerous safety-related applications such as avalanche warning, hazard zoning, hazard mitigation measures, forestry, risk management and numerical simulations. This in
Externí odkaz:
https://doaj.org/article/d3d0b3d5888a42db999b3fb67624d1c7
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2022, Pp 143-150 (2022)
The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completio
Externí odkaz:
https://doaj.org/article/572fe424b4ae4dea9a505e2036941dd7
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2022, Pp 193-201 (2022)
High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in practice, rather noisy and tend to miss small geometric features that are
Externí odkaz:
https://doaj.org/article/6806aa60236345f28e0f4f747a86f1df
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2021, Pp 247-254 (2021)
Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors
Externí odkaz:
https://doaj.org/article/e87920bcf10f4a42a999a7f75cbc8921
Publikováno v:
Hydrology and Earth System Sciences, Vol 25, Pp 2567-2597 (2021)
Grain size analysis is the key to understand the sediment dynamics of river systems. We propose GRAINet, a data-driven approach to analyze grain size distributions of entire gravel bars based on georeferenced UAV images. A convolutional neural networ
Externí odkaz:
https://doaj.org/article/14f8614fdedf472ebf5c3e60b273197d