Massive city-scale surface condition analysis using ground and aerial imagery

Ken Sakurada, Takayuki Okatani, Kris M. Kitani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
EditorsIan Reid, Ming-Hsuan Yang, Hideo Saito, Daniel Cremers
PublisherSpringer Verlag
Number of pages16
ISBN (Electronic)9783319168647
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 2014 Nov 12014 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Asian Conference on Computer Vision, ACCV 2014


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