Map Generation from Large Scale Incomplete and Inaccurate Data Labels
| Autor: | Siyuan Lu, Conrad M. Albrecht, Rui Zhang, Wei Zhang, Xiaodong Cui, Ulrich Finkler, David S. Kung |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning I.2.10 Computer science Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Image segmentation Semi-supervised learning Electrical Engineering and Systems Science - Image and Video Processing computer.software_genre Convolutional neural network Pipeline (software) Machine Learning (cs.LG) Task (project management) 020204 information systems FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Scale (map) computer |
| Zdroj: | KDD |
| Popis: | 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. This paper is accepted by KDD 2020 |
| Databáze: | OpenAIRE |
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