The Multi-Source Data-Model Constraint Method for Long-Time Series Extraction of Urban and Rural Settlement

Accurately extracting long-term urban and rural settlement (URS) information is crucial for studying urbanization processes and their impacts on the ecological environment. However, existing remote sensing extraction methods often rely on independent classification strategies for each period, leading to error accumulation and increased uncertainty in long-term sequence extraction. To address this, this study proposed a data-model constrained dynamic extraction method for URS and validated it on the Qinghai- Tibetan Plateau at five-year intervals from 1985 to 2020. Taking URS in 2015 as an example, the area of URS extracted using this method had a matching degree of 97.79% with the reference, and the overall accuracy was 98.33%, with a kappa of 0.96. The urban and rural settlement boundary (URSB) extracted by this method were more accurate than the Global Urban Boundaries (GUB) dataset, particularly in spatial completeness and boundary detail. The result provides technical support for uncovering urban development patterns and their environmental impacts.