Abstract
Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework’s performance with increasing training data and found that it converged even when the training samples were limited. This framework’s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China’s new and old rural buildings viable.
Highlights
To test how HTMask Region-Based Convolutional Neural Network (R-CNN) can achieve a converged performance with a limited amount of training data, the training process has involved an incremental number of samples
The recent advances of multi-angle imaging technologies and vision technologies integrated with deep learning that emerged in civil engineering provide new opportunities in the 3D reconstruction of rural building models [42,43]
Half of the Chinese population live in the rural areas of China
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The understanding of rural settlements would involve instance segmentation instead of semantic segmentation, as settlement’s numbers and areas are needed.Amongst the instance segmentation methods, Mask Region-Based Convolutional Neural Network (R-CNN) has been unprecedentedly popular. To achieve human-like segmentation for China’s vast and varying geography, we might need to build models dedicated to different regions In this regard, the bottleneck is the manual effort for annotating a large amount of training data. If all building footprints are given, a few predicted labels of new and old buildings in the same remote sensing image could validate if such a bimodal distribution exists and find the valley point In this regard, we can reduce the number of training samples while retaining the algorithm’s capability.
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