Maria Despoina Siampou
MobiltyAI
Authored Publications
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Mobility-Embedded POIs: Learning What a Place Is and How It’s Used from Human Movement
Shushman Choudhury
Shang-Ling Hsu
Cyrus Shahabi
Forty-third International Conference on Machine Learning (2026)
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Recent progress in geospatial foundation models (GeoFMs) has highlighted the
importance of learning general-purpose representations for real-world locations,
particularly Points of Interest (POIs) where human activity concentrates. Yet, ex-
isting POI representations remain largely static, drawing from textual metadata
(e.g., category labels, descriptions) and spatial attributes (e.g., coordinates, neigh-
borhood context), all of which describe what a place is, but not how it is actu-
ally used. We argue that human mobility provides a complementary and dynamic
signal, capturing real-world visitation patterns that reveal how places function in
practice. To this end, we introduce Mobility Embedded POIs (ME-POIs), a
pretraining framework that learns POI representations directly from sequences of
human visits. Each visit is encoded as a contextualized embedding that captures
the POI’s static attributes as well as its temporal and sequential context, includ-
ing when the visit occurs and which visits surround it. These visit embeddings
are aligned with learnable POI embeddings via a contrastive objective, grounding
POI representations in their real-world usage patterns. To address the long tail of
sparsely visited POIs, we transfer visitation distributions from data-rich anchors
to sparse locations, leveraging multi-scale spatial proximity to capture local and
regional patterns, and functional similarity to enable transfer across semantically
related POIs. We demonstrate the utility of ME-POIs for a set of automated map
enrichment tasks, critical in geospatial intelligence. We show empirically that
by embedding visitation dynamics, ME-POIs outperform text- and location-only
baselines, proving that mobility-informed embeddings provide a stronger founda-
tion for modeling place function and change.
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Toward Foundation Models for Mobility Enriched Geospatially Embedded Objects
Shang-Ling Hsu
Shushman Choudhury
Cyrus Shahabi
Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, ACM (2025)
Preview abstract
Recent advances in large foundation models (FMs) have enabled
learning general-purpose representations in natural language, vi-
sion, and audio. Yet geospatial artificial intelligence (GeoAI) still
lacks widely adopted foundation models that generalize across
tasks. We argue that a key bottleneck is the absence of unified,
general-purpose, and transferable representations for geospatially
embedded objects (GEOs). Such objects include points, polylines,
and polygons in geographic space, enriched with semantic context
and critical for geospatial reasoning. Much current GeoAI research
compares GEOs to tokens in language models, where patterns of
human movement and spatiotemporal interactions yield contextual
meaning similar to patterns of words in text. However, modeling
GEOs introduces challenges fundamentally different from language,
including spatial continuity, variable scale and resolution, temporal
dynamics, and data sparsity. Moreover, privacy constraints and
global variation in mobility further complicates modeling and gen-
eralization. This paper formalizes these challenges, identifies key
representational gaps, and outlines research directions for build-
ing foundation models that learn behavior-informed, transferable
representations of GEOs from large-scale human mobility data.
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