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, 261–266, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-261-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 261–266, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-261-2022
 
01 Jun 2022
01 Jun 2022

A PILOT STUDY OF URBAN POI MAPPING USING CROWDSOURCED STREET-LEVEL IMAGERY AND DEEP LEARNING

L. Liu1,2, B. Zhou1,2, and X. Yi3 L. Liu et al.
  • 1Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, 430079 Wuhan, China
  • 2College of Urban and Environmental Sciences, Central China Normal University, 430079 Wuhan, China
  • 3School of Earth Sciences and Engineering, Hohai University, 211100 Nanjing, China

Keywords: Crowdsourced Data, Street-Level Imagery, Object Detection, Point of Interest, Deep Learning

Abstract. Point-of-interest (POI) data contains rich semantic and spatial information, having a wide range of applications including land use, transport planning and driving navigation. However, urban POI mapping traditionally requires a lot of manpower and material resources, which only few institutions or enterprises can afford to. With the increasing amount of street-level imagery, it is possible to directly extract POI-related information from such data and automatically map the distribution of urban POIs. In the pilot study, we mainly focused on extracting POIs from billboards in street-level imagery. Firstly, the you only look once (YOLO) algorithm was considered to locate billboards in the imagery, then an optical character recognition (OCR) model was adopted to extract POI-related semantic information from the detected billboard, and finally the extracted semantic text was further processed to obtain POI results. The preliminary study shows that it is a promising way of mapping urban POIs from crowdsourced street-level data using deep learning techniques.