Img2Index: An integrated framework for automated extraction of spatial morphological indicators from satellite Images—A case study of 8155 traditional villages in China

Settlements offer critical insights into the historical development of human societies through their spatial morphology. In the digital era, morphometrics enables more precise and comparable analysis across regions. However, many regions still face significant challenges in acquiring high-quality morphological data and applying digital techniques. These constraints have hindered the advancement of quantitative morphology studies, particularly in traditional settlements. To address these challenges, this study proposes an integrated framework that establishes a systematic data foundation for spatial morphology analysis. The framework employs machine learning models to identify building footprints, applies graphical algorithms to perform spatial computation, and develops modules to automate index calculation. It is applied to a large-scale case study of 8155 traditional Chinese villages, from which morphological images, spatial features, and quantitative indicators were extracted. On one hand, the statistical analysis of indicator values establishes a unified, national-level quantitative standard for morphological assessment. On the other hand, this study identifies three distinct evolutionary patterns of morphological characteristics, contributing to more refined and differentiated strategies for spatial renewal. Overall, the proposed framework provides a new data-driven approach for understanding the morphological evolution of traditional settlements amid modernization, offering methodological support for intelligent management and resilient development of cultural heritage.