6 papers
Modelling daily activity patterns from mobile phone location data via deep representation learning
Xinglei Wang, Junyuan Liu, Guangsheng Dong +4
Passively collected mobile phone location data provide large-scale, longitudinal observations of human mobility but do not directly reveal activity purposes. The functional charact…
LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling
Xinglei Wang, Stephen Law, Zichao Zeng +3
Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing…
CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
Junyuan Liu, Xinglei Wang, Zichao Zeng +5
Urban representation learning encodes complex urban environments into general-purpose embeddings for diverse downstream tasks and emerging urban foundation models. However, current…
Beyond AlphaEarth: Toward Human-Centered Geospatial Foundation Models via POI-Guided Contrastive Learning
Junyuan Liu, Quan Qin, Guangsheng Dong +4
Recent geospatial foundation models (GFMs) produce spatially extensive representations of the Earth's surface that capture rich physical and environmental patterns. Among them, the…
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law +6
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised…
CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
Ilya Ilyankou, Natchapon Jongwiriyanurak, Tao Cheng +1
We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos in the Geograph dataset--a crowdsourced image archive sp…