arXiv AI June 9, 2026 By Maria Despoina Siampou, Shushman Choudhury, Shang-Ling Hsu, Neha Arora, Cyrus Shahabi

Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement

arXiv:2601. 21149v3 Announce Type: replace-cross Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 1

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.

By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song

llms rag benchmarks

arXiv AI

Jun 12

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.

By Mohammad Hashemi, Hossein Amiri, Andreas Zufle

rag benchmarks

arXiv Machine Learning

Aug 11

Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking

arXiv:2608. 08646v1 Announce Type: new Abstract: Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services.

By Zhifeng Chu, Bin Wang

rag

arXiv Machine Learning

Jun 25

What's in an Earth Embedding? An Explainability Analysis of Location Encoders

arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.

By Livia Betti, Sebastian Ricke, Ivica Obadic, Adam J. Stewart, Esther Rolf

rag

arXiv Machine Learning

Jun 16

IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data

arXiv:2606. 16023v1 Announce Type: new Abstract: Human mobility appears highly diverse, yet much of a person's daily mobility can be explained by a small set of recurring behavioral templates, such as commuting, school-centered activities, caregiving, nightlife, or errand patterns.

By Bita Azarijoo, John Krumm, Cyrus Shahabi

benchmarks

arXiv AI

Jun 8

Textual Supervision Enhances Geospatial Representations in Vision-Language Models

arXiv:2606. 07172v1 Announce Type: cross Abstract: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning.

By Marcelo Sartori Locatelli, Fernando Tonucci, Jea Kwon, Luiz Felipe Vecchietti, Bryan Nathanael Wijaya, Cheng Yaw Low, Virgilio Almeida, Meeyoung Cha

llms multimodal

Related stories

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking

What's in an Earth Embedding? An Explainability Analysis of Location Encoders

IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data

Textual Supervision Enhances Geospatial Representations in Vision-Language Models