Population Density Estimation with Deconvolutional Neural Networks

Workshop on Large Scale Computer Vision at NIPS 2016

By: Amy Zhang, Xianming Liu, Tobias Tiecke, Andreas Gros


This work is part of the Internet.org initiative to provide connectivity all over the world. Population density data is helpful in driving a variety of technology decisions, but currently, a microscopic dataset of population doesn’t exist. Current state of the art population density datasets are at ~1000km2 resolution. To create a better dataset, we have obtained 1PB of satellite imagery at 50cm/pixel resolution to feed through our building classification pipeline.

Our contribution is a fast building classification pipeline, that can run through a country in 8 hours on Facebook infrastructure. It is composed of an edge detection method for fast bounding box proposals, and a weakly supervised deconvolutional neural network that is trained for pixel-level classification, then mean pooled over the bounding box to output a probability of a building or buildings present in the bounding box. We train a global model and obtain precision and recall of > 90% in most countries. Countries with poorer results we use active learning techniques to re-sample data and fine-tune a new model. We also developed a weakly supervised footprint segmentation model that processes larger images with more context and produces a mask of location and shape of each building and a denoising network to clean up poorer quality source data.