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Learning to Detect Roads in High-Resolution Aerial Images

  • Volodymyr Mnih
  • Geoffrey E. Hinton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

Reliably extracting information from aerial imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. This task is a difficult vision problem because of occlusions, shadows, and a wide variety of non-road objects. Despite 30 years of work on automatic road detection, no automatic or semi-automatic road detection system is currently on the market and no published method has been shown to work reliably on large datasets of urban imagery. We propose detecting roads using a neural network with millions of trainable weights which looks at a much larger context than was used in previous attempts at learning the task. The network is trained on massive amounts of data using a consumer GPU. We demonstrate that predictive performance can be substantially improved by initializing the feature detectors using recently developed unsupervised learning methods as well as by taking advantage of the local spatial coherence of the output labels. We show that our method works reliably on two challenging urban datasets that are an order of magnitude larger than what was used to evaluate previous approaches.

Keywords

Road Network Hide Unit Aerial Image Stochastic Gradient Descent Aerial Imagery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Volodymyr Mnih
    • 1
  • Geoffrey E. Hinton
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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