Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations
This work addresses the need for richer place understanding in computer vision, though it is incremental as it builds on existing place recognition research by adding new data and methods.
The authors tackled the problem of comprehensive place understanding by introducing Placepedia, a large-scale dataset with over 35M photos from 240K unique places and multi-faceted annotations, and developed PlaceNet for multi-level place recognition and a city embedding method that links visual data to socioeconomic factors.
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 be conducted. Particularly, in our studies, we develop 1) PlaceNet, a unified framework for multi-level place recognition, and 2) a method for city embedding, which can produce a vector representation for a city that captures both visual and multi-faceted side information. Such studies not only reveal key challenges in place understanding, but also establish connections between visual observations and underlying socioeconomic/cultural implications.