Fine-Tuning-Based Transfer Learning for Building Extraction from Off-Nadir Remote Sensing Images

Building extraction—needed for urban planning and monitoring—is affected by the misalignment between labels and off-nadir remote sensing imagery. A computer vision approach to teacher–student learning between large–noisy and small–clean data has been introduced as a solution, but with limited accuracy and efficiency. This paper proposes fine-tuning-based transfer learning (FTL) to adapt a pre-trained model from a noisy source to a clean target dataset, improving segmentation accuracy in off-nadir images. A standardized experimental framework is developed with three new building datasets containing large–noisy and small–clean image–label pairs of multiple spatial resolutions. These datasets cover a range of building types, from low-rise to skyscrapers. Additionally, this paper presents one of the most extensive benchmarking efforts in teacher–student learning for building extraction from off-nadir images. Results demonstrate that FTL outperforms the existing methods with higher F1 scores—0.943 (low-rise), 0.868 (mid-rise), 0.912 (high-rise), and 0.697 (skyscrapers)—and higher computational efficiency. A notable gain in mean difference is observed in taller buildings from complex urban environments. The proposed method, datasets, and benchmarking framework provide a robust foundation for accurate building extraction and broader remote sensing applications.