Massive City-scale Surface Condition Analysis using Ground and Aerial Imagery - Robotics Institute Carnegie Mellon University

Massive City-scale Surface Condition Analysis using Ground and Aerial Imagery

Ken Sakurada, Takayuki Okatani, and Kris M. Kitani
Conference Paper, Proceedings of 12th Asian Conference on Computer Vision (ACCV '14), pp. 49 - 64, November, 2014

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.

BibTeX

@conference{-2014-109818,
author = {Ken Sakurada and Takayuki Okatani and Kris M. Kitani},
title = {Massive City-scale Surface Condition Analysis using Ground and Aerial Imagery},
booktitle = {Proceedings of 12th Asian Conference on Computer Vision (ACCV '14)},
year = {2014},
month = {November},
pages = {49 - 64},
}