activity
20222026
most citedThree-dimensional Geospatial Interlinking with JedAI-spatial

24 citations · 31 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations

Maria Despoina Siampou, Gengchen Mai, Ni Lao +4

Many urban prediction tasks depend not only on the static attributes of a location, such as the visual appearance of its built environment, but also on its dynamic function: how pe…

cs.LG2026

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

Maria Despoina Siampou, Shushman Choudhury, Shang-Ling Hsu +2

Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POI…

cs.LG2024★ 1 cited

WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series

Arash Hajisafi, Maria Despoina Siampou, Bita Azarijoo +2

Clinical time series are often irregularly sampled, with varying sensor frequencies, missing observations, and misaligned timestamps. Prior approaches typically address these irreg…

cs.DB2024

TrajRoute: Rethinking Routing with a Simple Trajectory-Based Approach -- Forget the Maps and Traffic!

Maria Despoina Siampou, Chrysovalantis Anastasiou, John Krumm +1

The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective…

cs.LG2024★ 1 cited

Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications

Maria Despoina Siampou, Jialiang Li, John Krumm +2

Encoding geospatial objects is fundamental for geospatial artificial intelligence (GeoAI) applications, which leverage machine learning (ML) models to analyze spatial information.…

cs.LG2023★ 5 cited

Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

Arash Hajisafi, Haowen Lin, Sina Shaham +4

Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and…

cs.DB2022★ 24 cited

Three-dimensional Geospatial Interlinking with JedAI-spatial

Marios Papamichalopoulos, George Papadakis, George Mandilaras +3

Geospatial data constitutes a considerable part of (Semantic) Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking…