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

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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Bibliographic Details
Main Authors: Yu Li, Patrick Matgen, Marco Chini
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Built-up area
Cross-attention
SAR
Data fusion
Convolutional neural networks
Noisy label learning
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843225001712
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