CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series
DOI:
https://doi.org/10.1609/aaai.v38i20.30216Keywords:
GeneralAbstract
Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces in a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.Downloads
Published
2024-03-24
How to Cite
Huang, T., Wu, Z., Wu, J., Hwang, J., & Rajagopal, R. (2024). CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22123-22131. https://doi.org/10.1609/aaai.v38i20.30216
Issue
Section
AAAI Technical Track on AI for Social Impact Track