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.
CITATION STYLE
Sakurada, K., Okatani, T., & Kitani, K. M. (2015). Massive city-scale surface condition analysis using ground and aerial imagery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9003, pp. 49–64). Springer Verlag. https://doi.org/10.1007/978-3-319-16865-4_4
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