Map Generation from Large Scale Incomplete and Inaccurate Data Labels
This work addresses the challenge of large-scale map generation for applications like routing and disaster response, but it is incremental as it builds on existing neural network architectures and distributed training methods.
The paper tackles the problem of generating accurate and complete maps of human infrastructure across the contiguous United States using publicly available aerial imagery and map data, achieving nearly linear speed-up through distributed computing.
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.