Work

Integrating Time-Series Nighttime Light Data With Static Remote Sensing and Village View Images for Hollow Villages Identification

Year: 2025

Type: article

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.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3548420

Authors: Hailong Zhao, Xun Li, Yu Gu, Weihuan Deng, Yaofu Huang, Suhong Zhou

Institution: Sun Yat-sen University

Language: English

Topic: Impact of Light on Environment and Health

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,800