Understanding Historical Cityscapes from Aerial Imagery Through Machine Learning

Abstract

Understanding cityscapes using remote sensing data has been an active research field for more than two decades. Meanwhile, machine learning provides generalization capabilities compared to hierarchical and rule-based methods. This paper evaluates several machine learning algorithms in order to fuse shadow detection and shadow compensation methods for building detection using high resolution aerial imagery. Three complex and real-life urban study areas were used as test datasets with various: (i) kinds of buildings structures of special architecture, (ii) pixel resolutions and, (iii) types of data. Objective evaluation metrics have been used for assessing the compared algorithms such recall, precision and F1-score as well as rates of completeness, correctness and quality. For both approaches, i.e., shadow detection and building detection, the computational complexity of each machine learning algorithm was examined. The results indicate that deep learning schemes, such a Convolutional Neural Network (CNN), provides the best classification performance in terms of shadow detection and building detection.