The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B4-2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-261-2022
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. Liu, B. Zhou, and X. Yi

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.