Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations
Huaiyi Huang, Yuqi Zhang, Qingqiu Huang, Zhengkui Guo, Ziwei Liu, and, Dahua Lin

TL;DR
Placepedia is a large-scale, richly annotated dataset for comprehensive place understanding, enabling multi-faceted recognition and socioeconomic analysis from visual data.
Contribution
The paper introduces Placepedia, a vast dataset with multi-level annotations, and develops PlaceNet and city embedding methods for advanced place recognition and socioeconomic insights.
Findings
Placepedia contains over 35 million photos from 240,000 places.
Developed PlaceNet for multi-level place recognition.
Created city embedding capturing visual and socioeconomic data.
Abstract
Place is an important element in visual understanding. Given a photo of a building, people can often tell its functionality, e.g. a restaurant or a shop, its cultural style, e.g. Asian or European, as well as its economic type, e.g. industry oriented or tourism oriented. While place recognition has been widely studied in previous work, there remains a long way towards comprehensive place understanding, which is far beyond categorizing a place with an image and requires information of multiple aspects. In this work, we contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places. Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc., and labels at multiple levels, including function, city, country, etc.. This dataset, with its large amount of data and rich annotations, allows various studies to…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Geographic Information Systems Studies · Video Surveillance and Tracking Methods
