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...

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Detalles Bibliográficos
Autores principales: Francis, John, Bright, Jonathan, Esnaashari, Saba, Hashem, Youmna, Morgan, Deborah, Straub, Vincent J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322896/
https://www.ncbi.nlm.nih.gov/pubmed/37407750
http://dx.doi.org/10.1038/s41598-023-38100-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322896/
https://www.ncbi.nlm.nih.gov/pubmed/37407750
http://dx.doi.org/10.1038/s41598-023-38100-1

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