The paper explores how graph neural networks (GNNs) can classify changes in electronic navigational charts (ENCs) as critical or non‑critical for maritime safety. By representing ENC objects as graph nodes and their spatial/semantic relationships as edges, the authors encode both old and new chart data into paired graphs and apply GNN architectures to predict risk levels. Experiments with various GNN models, evaluated on expert‑reviewed ENC updates, show that graph‑based representations enhance classification accuracy, offering a scalable method to support ENC maintenance workflows.
By Abhishek Potnis, Jacob Arndt
The paper introduces a multi‑view contrastive learning framework that creates low‑dimensional spatial embeddings by combining satellite imagery and OpenStreetMap data across Europe. These embeddings align with coordinate‑based encodings, allowing any dataset with latitude‑longitude pairs to be enriched with meaningful spatial features without needing the original spatial inputs. In case studies on French real‑estate prices and Belgian flood claim counts, models using the embeddings outperform those using raw coordinates, improving predictive accuracy and territorial risk classification while offering explainable spatial effects.
By Freek Holvoet, Christopher Blier-Wong, Katrien Antonio
arXiv:2606. 02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale.
By Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer, Song Gao, WenWen Li
arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.
By Livia Betti, Sebastian Ricke, Ivica Obadic, Adam J. Stewart, Esther Rolf
arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.
By Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis
arXiv:2607. 18887v1 Announce Type: new Abstract: Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and autonomous navigation.
By Yuan Gui, Hongchen Luo, Liqi Qu, Longyue Fu, Jiao Wang