activity
20242026
most citedSMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction

3 citations · 3 across the 5 of their papers we have counts for

collaborators

12 papers

cs.IR2026

Much of Geospatial Web Search Is Beyond Traditional GIS

Ilya Ilyankou, Stefano Cavazzi, James Haworth

Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what people ask of place, and how ofte…

cs.AI2026

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…

cs.HC2026

The Scenic Route to Deception: Dark Patterns and Explainability Pitfalls in Conversational Navigation

Ilya Ilyankou, Stefano Cavazzi, James Haworth

As pedestrian navigation increasingly experiments with Generative AI, and in particular Large Language Models, the nature of routing risks transforming from a verifiable geometric…

cs.AI2025

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…

cs.CV2025

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…

cs.CV2024

V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?

Natchapon Jongwiriyanurak, Zichao Zeng, June Moh Goo +7

Road safety assessments are critical yet costly, especially in Low- and Middle-Income Countries (LMICs), where most roads remain unrated. Traditional methods require expert annotat…