AMFENet: An Adaptive Multiscale Feature Fusion Enhancement Network for Sinkhole Detection

Sinkholes are natural depressions or cavities on the Earth’s surface, and accurate detection of sinkholes can prevent them from posing significant risks to human life, infrastructure, and the environment. Due to their varied and complex surface manifestations, sinkholes are relatively rare and pose challenges in precise detection when compared with other geological features. To achieve precise sinkhole detection, this letter introduces a network with multiscale feature fusion and enhancement capabilities (AMFENet). AMFENet is the first to propose considering the irregular shapes, varying sizes, relatively small target areas, and uneven distribution characteristics of sinkholes. To capture the contextual global information of sinkholes, AMFENet uses the Swin Transformer as its encoder. To address the scarcity and uneven distribution of sinkholes, a selective amplification block (SAB) is introduced, enhancing the significance of sinkhole features. To handle complex and diverse sinkhole features, the semantic integration block (SIB) is introduced. It integrates both global and local semantic information and adaptively enhances crucial semantic features from both spatial and channel dimensions. According to extensive experiments conducted on the sinkhole DEM dataset in Kentucky, AMFENet outperforms other semantic segmentation methods.