Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments
Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial ima...
| Автори: | , , , , , |
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| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
Springer Nature
2023
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