Multi-scale representation learning for spatial feature distributions using grid cells

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On the opportunities and challenges of foundation models for geospatial artificial intelligence

G Mai, W Huang, J Sun, S Song, D Mishra… - ar**
E Cole, G Van Horn, C Lange… - International …, 2023 - proceedings.mlr.press
Estimating the geographical range of a species from sparse observations is a challenging
and important geospatial prediction problem. Given a set of locations where a species has …
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Learning urban region representations with POIs and hierarchical graph infomax

W Huang, D Zhang, G Mai, X Guo, L Cui - ISPRS Journal of …, 2023 - Elsevier
We present the hierarchical graph infomax (HGI) approach for learning urban region
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …
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LSTM-TrajGAN: A deep learning approach to trajectory privacy protection

J Rao, S Gao, Y Kang, Q Huang - arxiv preprint arxiv:2006.10521, 2020 - arxiv.org
The prevalence of location-based services contributes to the explosive growth of individual-
level trajectory data and raises public concerns about privacy issues. In this research, we …
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