Integrating Time-Series Nighttime Light Data With Static Remote Sensing and Village View Images for Hollow Villages Identification
Hailong Zhao, Xun Li, Yu Gu, Weihuan Deng, Yaofu Huang, Suhong Zhou
Abstract
Accurately identifying hollow villages (HVs) has long been a challenge in rural governance and revitalization. Traditional field surveys require significant human and material resources, making large-scale identification difficult. This study develops a model that integrates static and dynamic data for HV identification. The model uses a ResNet18 with an attention module to extract static features of villages from remote sensing imagery and village view images, and employs an LSTM-FCN to analyze periodic human activity changes from nighttime light (NTL) data to extract dynamic features. Evaluated in four Guangdong counties, the multisource data approach outperforms single-source models, achieving a test overall accuracy of 0.8451, a kappa index of 0.6391, and an F1 score of 0.8880. The human activity patterns reflected by time-series NTL data play a significant role in the identification of HVs. The multisource data model helps to mitigate the biases inherent in individual data types. This approach provides a reliable solution for the rapid identification of HVs.