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Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

Automated visual analysis is an effective method for understanding changes in natural phenomena over massive city-scale landscapes. However, the view-point spectrum across which image data can be acquired is extremely wide, ranging from macro-level overhead (aerial) images spanning several kilometers to micro-level front-parallel (street-view) images that might only span a few meters. This work presents a unified framework for robustly integrating image data taken at vastly different viewpoints to generate large-scale estimates of land surface conditions. To validate our approach we attempt to estimate the amount of post-Tsunami damage over the entire city of Kamaishi, Japan (over 4 million square-meters). Our results show that our approach can efficiently integrate both micro and macro-level images, along with other forms of meta-data, to efficiently estimate city-scale phenomena. We evaluate our approach on two modes of land condition analysis, namely, city-scale debris and greenery estimation, to show the ability of our method to generalize to a diverse set of estimation tasks.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers 25135701, 25280054.

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Authors and Affiliations

  1. Tohoku University, Sendai, Japan

    Ken Sakurada & Takayuki Okatani

  2. Carnegie Mellon University, Pittsburgh, USA

    Kris M. Kitani

Authors
  1. Ken Sakurada
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  2. Takayuki Okatani
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  3. Kris M. Kitani
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Corresponding author

Correspondence to Ken Sakurada .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Sakurada, K., Okatani, T., Kitani, K.M. (2015). Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

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