The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 545–552, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-545-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 545–552, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-545-2022
 
02 Jun 2022
02 Jun 2022

A METHOD FOR REGIONAL ANALYSIS USING DEEP LEARNING BASED ON BIG DATA OF OMNIDIRECTIONAL IMAGES OF STREETS

T. Oki1 and Y. Ogawa2 T. Oki and Y. Ogawa
  • 1Tokyo Institute of Technology, 2-12-1-M1-27 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
  • 2The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan

Keywords: Omnidirectional image, Deep learning, Big data, Semantic segmentation, Clustering, Computer vision

Abstract. In this paper, we propose a method for regional analysis using image recognition technology based on deep learning and big data of street images captured by omnidirectional cameras on vehicles. Specifically, we first construct a classification method of regions based on street images using a pretrained deep learning model (VGG16) for image recognition as a feature extractor. Next, we develop a method to evaluate the landscape and safety of streets based on the ratio of street components (such as buildings, roads, fences, vegetations, sky, street lights) at each shooting point, which is calculated by semantic segmentation.