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 LSSM results. To address this limitation. https://herbsdailyes.shop/product-category/coconut-water/
DBPFNet: a double branch parallel fusion neural network method for land subsidence susceptibility mapping with InSAR observation data
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