11 to 20 of 36 Results
Jul 24, 2023 -
Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]
Comma Separated Values - 95.8 KB -
MD5: e7610e57ac4f1415f3d11a64963c8181
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Jul 24, 2023 -
Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]
Comma Separated Values - 7.7 MB -
MD5: 923572a230d9fd56a22c4db0c40c8c86
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Jul 24, 2023 -
Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]
Python Source Code - 6.5 KB -
MD5: 3e159b643fbeb7a266a39e997a0f80d9
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Jul 24, 2023 -
Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]
Comma Separated Values - 3.4 MB -
MD5: a241c5fa94335ceb6bb16b6eba3528ec
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Jul 20, 2023
Zahs, Vivien; Anders, Katharina; Kohns, Julia; Stark, Alexander; Höfle, Bernhard, 2023, "Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [Data and Source Code]", https://doi.org/10.11588/data/D3WZID, heiDATA, V1
Automatic damage assessment by analysing UAV-derived 3D point clouds provides fast information on the damage situation after an earthquake. However, the assessment of different damage grades is challenging given the variety in damage characteristics and limited transferability of... |
ZIP Archive - 9.5 GB -
MD5: 71093af0413740aeecc28c761c281115
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ZIP Archive - 271.5 MB -
MD5: fb49a6c8c1680a8df2c0c3d3350f5f6a
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ZIP Archive - 839.0 MB -
MD5: 2d91458b26f1d812e025bc83186f8768
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ZIP Archive - 115.4 MB -
MD5: 2e622f3e1f45ff24bac25b19beefacc9
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Python Source Code - 4.2 KB -
MD5: f255b27958baf1e7e577353dcc43290c
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