Application of deep-learning techniques to very-high-resolution satellite images supporting population censuses in developing countries

Abstract

Knowledge of demographic data is valuable information for planning initiatives.Typically, census, survey, and population projection exercises provide this information. In some developing countries, these operations pose a variety of economic and logistical challenges, thereby depriving authorities of accurate and timely information on their populations. To provide approaches for solving this situation, our study evaluates a population estimation method that is based on detection of residential geo-objects (houses) on very-high-resolution (VHR) satellite images using convolutional neural networks (CNN). The approach would be applicable to countries where a complete census is difficult to perform due to resource constraints or political instability. A 2008 VHR satellite image of Sudan is annotated according to seven classes of buildings to create a dataset that was used to train an object detection model, faster region-based CNN, by transfer learning. The model obtained mean average precision of 79% and 99% during training and validation, respectively. This unusual difference is due to the dominance of well detected classes in the validation dataset. The model was fine-tuned to detect the same building classes on images in 2021. A link between residential geo-objects and population size was established using 2008 population data and available field data. Subsequent characterization of the current population should assist in preparation of the 2023 census. Limitations of this approach were raised, but it could be used to improve the framework for population data collection in developing countries.