Development of Population Distribution Map and Automated Human Settlement Map Using High Resolution Remote Sensing Images

摘要

In this study, we present population distribution map and automated human settlement map for Maputo. To identify buildings from high-resolution remote sensing (HRRS) images of highly dense area, we used pixel-based classification frame with Ensemble Convolution Neural Network (ECNN) algorithm for building detection and used these identified buildings with zone based population data provided by the Japan International Cooperation Agency (JICA) to create population distribution map. Because of enormous HRRS image data, the training was performed on TSUBAME, a supercomputer operated by Tokyo Institute of Technology. The system used up to 28 Physical CPU cores, 128 GB of system memory and 16 GB of GPU memory for training. The result from building detection algorithm achieved overall accuracy of 96.8% and the average kappa of 70% and the population map created from it has a resolution of 10m × 10m.