An Investigation on Deep Learning Approaches to Combining Nighttime and Daytime Satellite Imagery for Poverty Prediction

Poverty prediction is an important task for developing countries that lack the key measures of economic development. The prediction can help governments to allocate scarce resources for sustainable development. Nighttime satellite imagery offers an opportunity to address the task. However, as the nighttime satellite data contain a large amount of noise, directly leveraging it is not very effective. Previous studies have shown that relying on deep learning techniques nighttime satellite data can be a good proxy between daytime satellite imagery and the poverty index. In this letter, based on the proxy, we leverage four deep learning approaches, namely, VGG-Net, Inception-Net, ResNet, and DenseNet, to extract deep features from daytime satellite imagery and then apply least absolute shrinkage and selection operator (LASSO) regression for poverty prediction. To further enhance the performance, we also integrate the squeeze and excitation (SE) module and focal loss into ResNet and DenseNet. Experimental results demonstrate the effectiveness of the investigated approaches, and the DenseNet with SE module and focal loss performs the best.