Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Digital terrain model (DTM)"'
Autor:
Nahed Osama, Zhenfeng Shao, Yue Ma, Jianguo Yan, Yewen Fan, Shaimaa Magdy Habib, Mohamed Freeshah
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 2, Pp 327-346 (2024)
ABSTRACTHigh-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results fo
Externí odkaz:
https://doaj.org/article/66801407f2f74d749f87fb21d62c6332
Autor:
Wojciech Maleika
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7292-7304 (2024)
The article presents the results of research on the effect of the density of measurement points (depth) from the multibeam echosounder (MBES) on the accuracy of the generated sea bottom models and the effect of this density of points on the optimal p
Externí odkaz:
https://doaj.org/article/268a0829361a489f9749139469c70170
Publikováno v:
Journal of Water and Land Development, Iss No 59, Pp 135-144 (2024)
The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of to
Externí odkaz:
https://doaj.org/article/5a174891bf324783a913b35b20fc7f18
Publikováno v:
Geogaceta, Vol 75 (2024)
High-resolution Digital Terrain Models (DTMs) of Mars are both limited and crucial for studying surface processes. The available DTMs generated from Mars Orbiter Laser Altimeter (MOLA) and High-Resolution Stereo Camera (HRSC) data offer limited resol
Externí odkaz:
https://doaj.org/article/105e015d2c6440caaeca29e7886b8d3d
Akademický článek
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Publikováno v:
INCAS Bulletin, Vol 14, Iss 1, Pp 25-38 (2022)
Natural and man-made disasters are a justified concern causing life-threats damages, affects existing budgets or disrupts national economic and social development plans. In this paper, a digital terrain model (DTM) and a geographic information system
Externí odkaz:
https://doaj.org/article/9707b62d94314403bfaeb78000031c37
Autor:
Hao Chen, Xuanyu Hu, Philipp Glaser, Haifeng Xiao, Zhen Ye, Hanyue Zhang, Xiaohua Tong, Jurgen Oberst
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 9398-9416 (2022)
The production of high-resolution digital terrain models (DTMs) based on images is often hampered by the lack of appropriate stereo observations. Here, we propose a deep learning-based reconstruction of pixel-resolution DTMs from Lunar Reconnaissance
Externí odkaz:
https://doaj.org/article/e85f3604e7e64bc58d53d2d74955ab5c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4980-4989 (2022)
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annota
Externí odkaz:
https://doaj.org/article/8eca5ead4b5e40a89e4f6171a8d4705e
Autor:
Minyoung Jung, Jinha Jung
Publikováno v:
Remote Sensing, Vol 15, Iss 17, p 4289 (2023)
Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors
Externí odkaz:
https://doaj.org/article/f02e57367c2f4f5f98537f31fb2e5582
Autor:
Paul Lau Hua Ming, Azita Ahmad Zawawi
Publikováno v:
Environment and Natural Resources Journal, Vol 19, Iss 5, Pp 358-370 (2021)
Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often bee
Externí odkaz:
https://doaj.org/article/1f2af96bd89f4077bd5188f6cae8e05e