Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

The increasingly mature computer vision (CV) technology represented by convolutional neural networks (CNN) and available high-resolution remote sensing images (HR-RSIs) provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large scale. Based on the advanced CNN method high-resolution net (HRNet) and multi-temporal HR-RSIs, a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed (CBR). The proposed framework consists of an expert module focusing on scenes analysis, a CV module for automatic detection, an evaluation module containing thresholds, and an output module for data analysis. Based on this, the changes in the adoption of different CBR technologies (CBRTs), including light-translucent CBRTs (LT-CBRTs) and non-light-translucent CBRTs (NLT-CBRTs), in 24 villages in southern Hebei were identified from 2007 to 2021. The evolution of CBRTs was featured as an inverse S-curve, and differences were found in their evolution stage, adoption ratio, and development speed for different villages. LT-CBRTs are the dominant type but are being replaced and surpassed by NLT-CBRTs in some villages, characterizing different preferences for the technology type of villages. The proposed research framework provides a reference for the evolution monitoring of vernacular buildings, and the identified evolution laws enable to trace and predict the adoption of different CBRTs in a particular village. This work lays a foundation for future exploration of the occurrence and development mechanism of the CBR phenomenon and provides an important reference for the optimization and promotion of CBRTs.