Remote Sensing and Deep Learning Techniques for Building Detection

Abstract

his research thesis enhances the state-of-the-art building detection in very high resolution aerial images by fine-tuning pre-trained state-of-the-art deep models on three diverse benchmarking datasets: the Massachusetts Buildings Dataset, WHU Buildings Dataset, and INRIA Aerial Image Labeling Dataset. Through the use of Dual-stream Asymmetric Fusion Networks, the Dice Loss, IoU Score, Precision, Recall, Accuracy, and F1 Score were enhanced. Our approach was to tune the existing model architecture using different datasets to enhance generalization capability and accuracy. Performance after training and validation consistently showed improvements, indicating that the model was effectively learning and enhancing the building segmentation accuracy. Visual results indicated the model's robustness to different urban environments, where the ground truth matched the predicted masks. These advancements can have high importance for urban planning, disaster management, and GIS, where precise building detection becomes an important criterion. This work emphasizes the importance of using advanced model architectures in conjunction with comprehensive data integration to enhance the building detection system. The improvements achieved prove the efficiency of our approach in real-world applications and greatly contribute to remote sensing and building detection. Our research entails practical solutions and insights, paving the path for future improvements in detecting buildings in aerial images accurately and efficiently.