5 papers
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…
TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Maria Despoina Siampou, Gengchen Mai, Ni Lao +4
Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL methods are mainly designed…
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…
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.…
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…