Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Valentin Tertius Bickel"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6589-6600 (2024)
The NASA Lunar Reconnaissance Orbiter (LRO) has returned petabytes of lunar high spatial resolution surface imagery over the past decade, impractical for humans to fully review manually. Here, we develop an automated method using a deep generative vi
Externí odkaz:
https://doaj.org/article/c58efd51b7c540818b56a9a8a0981a06
Publikováno v:
Geosciences, Vol 13, Iss 12, p 371 (2023)
Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digi
Externí odkaz:
https://doaj.org/article/95f384a839c04e69af9e3e5dc60ee5dd
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-7 (2020)
In this study, the authors present a global map of rockfalls on the lunar surface and determine impact events as short- and long-term driver for rockfall events.
Externí odkaz:
https://doaj.org/article/dbee2ba850084f4281deb7b50b144623
Autor:
Valentin Tertius Bickel, Susan J. Conway, Pierre-Antoine Tesson, Andrea Manconi, Simon Loew, Urs Mall
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 2831-2841 (2020)
The analysis of rockfall distribution and magnitude is a useful tool to study the past and current endogenic and exogenic activity of Mars. At the same time, tracks left by rockfalls provide insights into the mechanical properties of the Martian surf
Externí odkaz:
https://doaj.org/article/dc45180e05114cf1b1758a96f6bc5344
Publikováno v:
Remote Sensing, Vol 10, Iss 6, p 865 (2018)
We evaluate the capability of three different digital image correlation (DIC) algorithms to measure long-term surface displacement caused by a large slope instability in the Swiss Alps. DIC was applied to high-resolution optical imagery taken by airb
Externí odkaz:
https://doaj.org/article/70df1157c5d24a169f49318a3f537b0c
Publikováno v:
Journal of Geovisualization and Spatial Analysis, 6 (2)
Multi-temporal, high-resolution, and homogeneous geospatial datasets acquired by space- and/or airborne sensors provide unprecedented opportunities for the characterization and monitoring of surface changes on very large spatial scales. Here, we demo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::250eaca1c41d6706271b877b07cb9163
Publikováno v:
Journal of Geophysical Research: Planets, 126 (10)
The long‐ and short‐term drivers and transport mechanisms of lunar rockfalls are currently not well understood, but could provide valuable information about the geologic processes that still shape the surface of the Moon today. Here, we compare t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::683284c2bbce27601a652071dd1ff894
http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9866
http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9866
Publikováno v:
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Nature Communications, 12
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Nature Communications, 12
The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characte
Publikováno v:
CVPR
Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these appro
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 57:3501-3511
This paper implements a novel approach to automatically detect and classify rockfalls in Lunar Reconnaissance Orbiter narrow angle camera (NAC) images using a single-stage dense object detector (RetinaNet). The convolutional neural network has been t