Learning to count buildings in diverse aerial scenes

Determining the number of buildings in aerial images is an important problem because the information greatly benefits applications such as population estimation, change detection, and urbanization monitoring. In this paper, we address this problem by learning the relationship between low-level image features and building counts. Building footprints from public cartographic databases are used as labeled data. We first extract straight line segments from images. A classifier is then trained to identify line segments corresponding to building edges. Although there exist mismatches between resulting line segments and building edges, we observe a strong linear relationship between building numbers and line numbers for similar types of buildings. Based on this observation, we predict the building count for a given image using the following method. We find top k images with the most similar appearances from training samples and learn a linear regression model from this image set. The building count is computed based on the model. Our method avoids the difficulty in building detection and produces reliable results on large, diverse datasets.