DBPFNet: a double branch parallel fusion neural network method for land subsidence susceptibility mapping with InSAR observation data

Current machine learning methods for land subsidence susceptibility mapping (LSSM) predominantly focus on the spatial features of land subsidence conditioning factors (LSCFs), overlooking the sequence relationships that merger after the superposition of these factors. This often leads to unreliable...

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Autori principali:	Yi He, Binghai Gao, Haowen Yan, Qing Zhang, Lifeng Zhang, Wende Li, Xu He, Jiangang Lu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Taylor & Francis Group 2025-08-01
Serie:	International Journal of Digital Earth
Soggetti:	Land subsidence susceptibility InSAR CNN LSTM GIS and RS
Accesso online:	https://www.tandfonline.com/doi/10.1080/17538947.2025.2499199
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