Enhanced large-scale building extraction evaluation: developing a two-level framework using proxy data and building matching

Deep learning-based building extraction methods have widespread applications in diverse fields. However, the evaluation of large-scale extraction results remains challenging, due to traditional evaluation metrics rely on manually created ground-truth samples and the lack of comprehensive reference-building data for developing countries. To address these problems, we proposed a two-level framework for evaluating large-scale footprint extraction. First, we utilised global open-source population and land use data as the proxy data, to assess grid-level completeness for the areas with insufficient reference data. Second, we introduced an improved two-way area-overlapping method to match the extracted footprints with the reference buildings, thereby enabling a comprehensive evaluation of the study region. Tested in Hyogo Prefecture and Numazu City, Japan, the results demonstrated a 2.6-% improvement in grid classification accuracy and an increase of 0.53 in the completeness correlation, compared with the results obtained using a single proxy indicator. Moreover, the optimised matching method achieved an outstanding semantic matching accuracy of 99%, with high efficiency and robustness in multi-scale matching. Therefore, the proposed approach can effectively evaluate large-scale footprint extraction results and interpret their semantic relationship with actual buildings, applicable globally regardless of the availability of reference building datasets.