Deep Learning-Based Improved Automatic Building Extraction from Open-Source High Resolution Unmanned Aerial Vehicle (UAV) Imagery

Automatically extracting buildings from remotely sensed imagery has always been a challenging task, given the spectral homogeneity of buildings with non-building features as well as the complex structural diversity within the image. Traditional machine learning (ML) based methods deeply rely on a huge number of samples and are best suited for medium-resolution images. Unmanned aerial vehicle (UAV) imagery offers the distinct advantage of very high spatial resolution, which is helpful in improving building extraction by characterizing patterns and structures. However, with increased finer details, the number of images also increases many folds in a UAV dataset, which require robust processing algorithms. Deep learning algorithms, specifically Fully Convolutional Networks (FCNs) have greatly improved the results of building extraction from such high resolution remotely sensed imagery, as compared to traditional methods. This study proposes a deep learning-based segmentation approach to extract buildings by transferring the learning of a deep Residual Network (ResNet) to the segmentation-based FCN U-Net. This combined dense architecture of ResNet and U-Net (Res-U-Net) is trained and tested for building extraction on the open source Inria Aerial Image Labelling (IAIL) dataset. This dataset contains 360 orthorectified images with a tile size of 1500 m(2) each, at 30 cm spatial resolution with red, green and blue bands; while covering total area of 805 km(2) in select US and Austrian cities. Quantitative assessments show that the proposed methodology outperforms the current deep learning-based building extraction methods. When compared with a singular U-Net model for building extraction for the IAIL dataset, the proposed Res-U-Net model improves the overall accuracy from 92.85% to 96.5%, the mean F1-score from 0.83 to 0.88 and the mean IoU metric from 0.71 to 0.80. Results show that such a combination of two deep learning architectures greatly improves the building extraction accuracy as compared to a singular architecture.