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SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling — arXiv:2611.00111

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00111
License (as archived): CC0-1.0
Archived: 2026-11-03T18:39:20.326Z
SHA-256 of the archived text: 50dcfd0b28e5da1416a5900b62f410e18e551b7476f6be01f34e04cf56e11acd
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How to cite

SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling — arXiv:2611.00111 — arXiv (en), https://arxiv.org/abs/2611.00111 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_693a525ebfdb (Metraipolis archive dc_693a525ebfdb, sha256 50dcfd0b28e5da1416a5900b62f410e18e551b7476f6be01f34e04cf56e11acd)

BibTeX

@misc{dc693a525ebfdb,
  title = {SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling — arXiv:2611.00111},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00111},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_693a525ebfdb; sha256 50dcfd0b28e5da1416a5900b62f410e18e551b7476f6be01f34e04cf56e11acd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00111. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Spatial representation learning is essential for Geospatial Artificial Intelligence (GeoAI) applications, enabling machine learning models to capture the geometry, location, and spatial relationships of geographic entities such as points, polylines, and polygons. Existing spatial encoding methods often struggle to preserve fine-grained topological information, particularly for interactions involving complex geometries such as polylines and polygons. These approaches inadequately capture boundary structure and local spatial context, causing topology-sensitive relationships near geometry boundaries to be underrepresented in the learned embedding space. Consequently, accurately modeling complex spatial interactions remains a significant challenge for downstream spatial reasoning tasks. To fill the gap, we introduce SpatialVec, a boundary-aware spatial representation learning framework that adaptively samples geometry boundaries and their surrounding regions, capturing fine-grained geometric structures critical for topology-aware spatial reasoning. A transformer-based encoder-decoder trained through geometry reconstruction produces compact, geometry-aware, and unified representations for points, polylines, and polygons. Empirical evaluation on two real-world datasets shows that SpatialVec substantially improves the preservation of challenging topological relationships involving polygonal interactions and outperforms state-of-the-art baselines significantly on downstream prediction and classification tasks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00111 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:12:54.679Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).