TY - GEN
T1 - Massive city-scale surface condition analysis using ground and aerial imagery
AU - Sakurada, Ken
AU - Okatani, Takayuki
AU - Kitani, Kris M.
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers 25135701, 25280054.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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 (streetview) 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 4million 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, cityscale debris and greenery estimation, to show the abil ity of our method to generalize to a diverse set of estimation tasks.
AB - 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 (streetview) 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 4million 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, cityscale debris and greenery estimation, to show the abil ity of our method to generalize to a diverse set of estimation tasks.
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U2 - 10.1007/978-3-319-16865-4_4
DO - 10.1007/978-3-319-16865-4_4
M3 - Conference contribution
AN - SCOPUS:84938870905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 64
BT - Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Reid, Ian
A2 - Yang, Ming-Hsuan
A2 - Saito, Hideo
A2 - Cremers, Daniel
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -