11 citations · 12 across the 5 of their papers we have counts for
6 papers
Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts
Abhishek Potnis, Jacob Arndt
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a natural extension, however its re…
Electronic Navigational Chart Change Classification
Jacob Arndt, Abhishek Potnis, Alexandre Sorokine
Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and navigational information such as depths, na…
Emerging Flexible Designs for Geospatial Multimodal Foundation Models
Philipe Dias, Waqwoya Abebe, Abhishek Potnis +4
Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities. However, their architectural diversity…
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
Toan Tran, Waqwoya Abebe, Abhishek Potnis +4
This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can pro…
OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery
Philipe Dias, Aristeidis Tsaris, Jordan Bowman +4
While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to bil…
Pretraining Billion-scale Geospatial Foundational Models on Frontier
Aristeidis Tsaris, Philipe Ambrozio Dias, Abhishek Potnis +3
As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples incre…