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Mapping new and old buildings are of great significance for understandingsocio-economic development in rural areas. In recent years, deep neuralnetworks have achieved remarkable building segmentation results inhigh-resolution remote sensing images. However, the scarce training data andthe varying geographical environments have posed challenges for scalablebuilding segmentation. This study proposes a novel framework based on MaskR-CNN, named HTMask R-CNN, to extract new and old rural buildings even when thelabel is scarce. The framework adopts the result of single-object instancesegmentation from the orthodox Mask R-CNN. Further, it classifies the ruralbuildings into new and old ones based on a dynamic grayscale threshold inferredfrom the result of a two-object instance segmentation task where training datais scarce. We found that the framework can extract more buildings and achieve amuch higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. Wetested the novel framework's performance with increasing training data andfound that it converged even when the training samples were limited. Thisframework's main contribution is to allow scalable segmentation by usingsignificantly fewer training samples than traditional machine learningpractices. That makes mapping China's new and old rural buildings viable.

A Histogram Thresholding Improvement to Mask R-CNN for Scalable Segmentation of New and Old Rural Buildings