An Intelligent Incremental Update Method for Building Data Across Multiple Scales Supported by Categorical Boosting

Leveraging larger-scale data with higher currency to incrementally update smaller-scale data, thereby upholding consistency across multiple scale databases, has become a core focus of contemporary map production tasks centered on data updates. Existing methods rely on rule-based constraints to extract change information and identify update-required objects, which have notable limitations in terms of method generalization and constraints on results. To address the above issues, we propose an intelligent incremental updating method for different scale building datasets supported by Categorical Boosting (CatBoost). The proposed method forms a general incremental updating framework for buildings through three steps: change information extraction, change information classification, and change information updating. Experiments conducted on different scale datasets from Ningbo, China, demonstrate that the proposed method can effectively identify update-required objects and generate more reasonable updated smaller-scale data than the comparative methods.