A Two-Stage CNN Based Satellite Image Analysis Framework for Estimating Building-Count in Residential Built-Up Area

The assessment of population within a certain region holds significant importance in the field of resource allocation and urban planning. The central objective of our proposed research efforts is to aid in assessing the population of a region by estimating building counts through a two-stage framework that analyses low-resolution satellite images using deep learning models based on convolutional neural network (CNN). The first phase of our framework focuses on segmentation of the built-up areas in a satellite image using a Mask-RCNN model, while the second phase employs a regression model based on convolutional neural network (CNN) for estimating the number of buildings within each segmented built-up area without the need for individual extraction of roof-tops. The extraction of roof-tops from low-resolution satellite images for population estimation still poses a huge challenge to the researchers because of poor visual clarity. Further, in densely populated areas, the low contrast of the built-up areas causes huge difficulty in the detection of roof-tops individually. In view of such challenges, we develop a Mask-RCNN model for segmentation of probable built-up areas in low-resolution satellite images instead of attempting to extract every building individually. Subsequently, a CNN-based regression model is developed for estimation of the building-count within the segmented built-up areas in low-resolution satellite images. The proposed framework exhibited a promising level of accuracy while working with low-resolution satellite images. The experimental results with publicly available data validated the capability of the proposed framework, which provides a cost-effective solution for estimating the population in a region. Such an automated framework for population estimation is useful to assess demographic variation, resource allocation for disaster management, smart city construction, and many other socio-economic planning activities.