Applying a Deep Learning Approach for Building Extraction From High-Resolution Remote Sensing Imagery

Abstract

As data science applies to mapping buildings, great attention has been given to the potential of using deep learning and new data sources. However, given that convolutional neural networks (CNNs) dominate image classification tasks, automating the building extraction process is becoming more and more common. Increased access to unstructured data (such as imagery and text) and developments in deep learning and computer vision algorithms have improved the possibility of automating the extraction of building attributes from satellite images in a cost-effective and large-scale manner. By applying intelligent software-based solutions to satellite imageries, the manual process of acquiring features such as building footprints can be expedited. Manual feature acquisition is time-consuming and expensive. The buildings may be recovered from RGB photos and are extremely properly identified. This chapter offers suggestions to quicken the development of DL-centred building extraction techniques using remotely sensed images.