SIGSPATIAL papers benchmark satellite foundation models against costly travel censuses
flosalim · x · 2026-10-20
Two of the author's lab papers accepted at SIGSPATIAL 2026 tackle urban mobility. The first benchmarks vision encoders (RemoteCLIP, DINOv3, SatCLIP, AlphaEarth) via a WeDAN graph diffusion pipeline across 1,925 US counties, 325 UK districts, and 14 cities: language-supervised models like RemoteCLIP recover 98.3% of census performance locally, while geographically grounded AlphaEarth wins cross-continental transfers by 33%. The second builds on the Massive-STEPS dataset, evaluating classical sequential, graph/transformer, and LLM-based models (LLM-Mob, LLM-ZS, LLM-Move) on supervised, zero-shot next-POI recommendation and spatiotemporal reasoning, with attention to long-tail trajectories, cold-start users, and temporal distribution shift.
Related event: Satellite Imagery with VLMs Can Recover 98.3% of Commute Survey Data(2 posts)→
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