Novel Building Detection and Location Intelligence in Aerial Satellite Imagery

The accurate detection and extraction of building information from aerial imagery is of paramount importance in urban planning, land use analysis, and disaster management. This study presents a comprehensive investigation into the development of a robust and efficient methodology for building detection in satellite imagery utilizing state-of-the-art deep learning techniques. We conducted a comparative analysis of three distinct semantic segmentation models based on the U-Net architecture: a baseline U-Net trained from scratch, a U-Net incorporating a pre-trained ResNet34 encoder, and a U-Net with custom architectural enhancements. Our methodological approach encompassed data augmentation strategies, transfer learning techniques, and ensemble methods to optimize model performance. The Inria Aerial Image Labelling Dataset served as the primary source for model training and validation. We explored the efficacy of various loss functions, including dice loss, focal loss, and weighted cross-entropy, to address class imbalance and enhance segmentation accuracy. Model performance was rigorously evaluated using a comprehensive set of metrics, including pixel-wise accuracy, Intersection over Union (IoU), and F1-score. Our highest-performing individual model achieved a dice score of 92 percent on the validation set, while the implementation of ensemble techniques further improved detection accuracy to 93 percent on the heldout test set. Post-processing algorithms, incorporating traditional computer vision methods, were applied to refine building polygon delineation. This research demonstrates the efficacy of deep learning-based segmentation approaches for building detection in aerial imagery and offers valuable insights into potential applications across various domains, including urban planning, construction monitoring, and disaster response. Future research directions may explore building classification, change detection analysis, and model optimization for real-time applications in dynamic urban environments.