Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks

Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and o...

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Detalhes bibliográficos

Principais autores:	Yu Li, Patrick Matgen, Marco Chini
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2025-05-01
coleção:	International Journal of Applied Earth Observations and Geoinformation
Assuntos:	Built-up area Cross-attention SAR Data fusion Convolutional neural networks Noisy label learning
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S1569843225001712
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