Timely and accurate population statistic data plays an important role in many fields. To illustrate the demographic characteristics, population density is a crucial factor in evaluating population data. With a dynamic regional migration in population, it is a challenging job to evaluate population density without a census-based survey. We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach. In this paper, we use satellite imagery and community population density data. With convolutional neural networks, we evaluated the performance of CNN on population estimation with satellite images, found the features that are important in population estimation, and then perform the sensitive analysis.