CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images

ABSTRACT

Accurate extraction of built-up areas is helpful to urban development and map updating. Nighttime light (NTL) data can capture the lighting signal of ground objects. However, most built-up area extraction is conducted on public limited coarse spatial resolution NTL images. The Sustainable Development Science Satellite-1 (SDGSAT-1) provides 10 m spatial resolution panchromatic NTL images, making it possible to map detailed urban lighting structures. In urban extraction, the boundaries of urban areas are easily confused with background objects due to the similar spectral and textual features. To address this problem, we proposed a multi-task deep learning model, CG-CFPANet, to extract illuminated built-up areas by synthesizing SDGSAT-1 NTL data and optical remote sensing images. In CG-CFPANet, a convolutional feature pyramid attention (CFPA) module for better contextual recognition and a concatenation group (CG) module to merge the two remote sensing images are developed. Our proposed CG-CFPANet achieved 1.3% higher precision in built-up area extraction than ten other recently proposed network structures: UNet, UNet++, PSPNet, DeeplabV3, FCN, ExtremeC3Net, SegNet, BiseNet, Res2-UNet, and CBRNet. It shows higher applicability for large-scale built-up area extraction.

KEYWORDS:

• Built-up area
• deep learning
• remote sensing image
• SDGSAT-1
• semantic segmentation

Acknowledgements

The authors thank the International Research Centre of Big Data for Sustainable Development Goals (CBAS) for providing SDGSAT-1 data. The authors thank the National Geomatics Center of China (NGCC) for providing GlobaLand30 land use data. The authors thank the anonymous reviewers for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The research was financially supported by the National Key R&D Program of China (No. 2022YFC3800701), the China-ASEAN Big Earth Data Platform and Applications (CADA, guikeAA20302022), and the Youth Innovation Promotion Association, CAS (2022122).