Since right after the occurrence of the Great East Japan Earthquake on March 11th, 2011, we have been periodically capturing the images of the disaster areas in the north-eastern Japan coastline by using a vehicle having a camera on its roof. The motivation behind the periodic image capturing is to archive not only the damages of the areas but also the process of their month-by-month recovery or year-by-year reconstruction. The image data thus being obtained amount to about 30 terabytes as of today. Although they can be used as they are, i.e., by viewing the images one-by-one in a manner similar to Google Street View, we think that the data contain rich information, often in an invisible form, which can be used in many more ways. Thus, in parallel with the archiving activity, we have been studying the method for extracting such useful information from the image data by using all sorts of computer vision techniques. The goal is to visualize what extent and type of damages the earthquake and tsunami gave each city and how damaged cities change their shapes as their recovery and reconstruction proceed.
This dataset contains the image sequences of city streets captured by a vehicle-mounted camera at two different time points. We make them publicly available for the researchers who are interested in the problem of the image-based detection of temporal changes of 3D scene structures. Although we own its copyright, you can freely use it for research purposes. We request that you cite the following paper if you publish research results utilizing these data:
Ken Sakurada, Takayuki Okatani, Koichiro Deguchi, Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-mounted Camera, Proc. Computer Vision and Pattern Recognition, 2013.
This study proposes a method for detecting temporal changes of the three-dimensional structure of an outdoor scene from its multi-view images captured at two separate times. For the images, we consider those captured by a camera mounted on a vehicle running in a city street. The method estimates scene structures probabilistically, not deterministically, and based on their estimates, it evaluates the probability of structural changes in the scene, where the inputs are the similarity of the local image patches among the multi-view images. The aim of the probabilistic treatment is to maximize the accuracy of change detection, behind which there is our conjecture that although it is dicult to estimate the scene structures deterministically, it should be easier to detect their changes. The proposed method is compared with the methods that use multi-view stereo (MVS) to reconstruct the scene structures of the two time points and then differentiate them to detect changes. The experimental results show that the proposed method outperforms such MVS-based methods.