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Proceedings Paper

Building and road detection from large aerial imagery
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Paper Abstract

Building and road detection from aerial imagery has many applications in a wide range of areas including urban design, real-estate management, and disaster relief. The extracting buildings and roads from aerial imagery has been performed by human experts manually, so that it has been very costly and time-consuming process. Our goal is to develop a system for automatically detecting buildings and roads directly from aerial imagery. Many attempts at automatic aerial imagery interpretation have been proposed in remote sensing literature, but much of early works use local features to classify each pixel or segment to an object label, so that these kind of approach needs some prior knowledge on object appearance or class-conditional distribution of pixel values. Furthermore, some works also need a segmentation step as pre-processing. Therefore, we use Convolutional Neural Networks(CNN) to learn mapping from raw pixel values in aerial imagery to three object labels (buildings, roads, and others), in other words, we generate three-channel maps from raw aerial imagery input. We take a patch-based semantic segmentation approach, so we firstly divide large aerial imagery into small patches and then train the CNN with those patches and corresponding three-channel map patches. Finally, we evaluate our system on a large-scale road and building detection datasets that is publicly available.

Paper Details

Date Published: 27 February 2015
PDF: 12 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050K (27 February 2015); doi: 10.1117/12.2083273
Show Author Affiliations
Shunta Saito, Keio Univ. (Japan)
Yoshimitsu Aoki, Keio Univ. (Japan)

Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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