Global urban and rural settlement dataset from 2000 to 2020 - Zhitao Liu; Sheng Huang; Chuanglin Fang; Luotong Guan; Menghang Liu (Scientific Data)
Abstract Accurate mapping of global urban and rural settlements is crucial for understanding their distinct expansion patterns and ecological impacts. However, existing global datasets focus mainly on urban settlements and ignore the delineation of rural settlements. Therefore, this study proposed a framework for delineating between urban and rural settlements based on dynamic thresholds defined by area and light brightness and constructed the first global 100-meter resolution urban and rural settlements dataset (GURS) spanning from 2000 to 2020, integrating GHS-BUILT-S R2023A, NPP-VIIRS-like nighttime light, and OpenStreetMap data. An accuracy assessment of 44,474 independent samples showed that GURS achieved an overall accuracy of 91.22% with a kappa coefficient of 0.85, outperforming nine multi-scale reference datasets in delineating global urban and rural settlements. GURS offers deep insights into the dynamics of global settlements, facilitating urban-rural comparative studies on socio-economic characteristics, environmental impacts, and governance modes, thereby enhancing the sustainable management of settlements.