# SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling

DevFeed: [SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling](<https://devfeed.tech/articles/spatialvec-a-unified-representation-learning-framework-for-geospatial-objects-with-boundary-aware-sampling-113639.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.00111>)

Author: Samya Acharja, Kanchan Chowdhury

Published: 2026-11-03T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Databases](<https://devfeed.tech/sources/arxiv-databases.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Representation learning](<https://devfeed.tech/topics/representation-learning.md>), [geospatial](<https://devfeed.tech/topics/geospatial.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [boundary](<https://devfeed.tech/tags/boundary.md>), [classification](<https://devfeed.tech/tags/classification.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-db](<https://devfeed.tech/tags/cs-db.md>), [cs-ir](<https://devfeed.tech/tags/cs-ir.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [decoder](<https://devfeed.tech/tags/decoder.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [encoding](<https://devfeed.tech/tags/encoding.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine](<https://devfeed.tech/tags/fine.md>), [framework](<https://devfeed.tech/tags/framework.md>), [geospatial](<https://devfeed.tech/tags/geospatial.md>), [learning](<https://devfeed.tech/tags/learning.md>), [local](<https://devfeed.tech/tags/local.md>), [location](<https://devfeed.tech/tags/location.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [modeling](<https://devfeed.tech/tags/modeling.md>), [models](<https://devfeed.tech/tags/models.md>), [points](<https://devfeed.tech/tags/points.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [sampling](<https://devfeed.tech/tags/sampling.md>)

## AI overview

SpatialVec uses boundary-aware sampling to improve geospatial representations of topology-sensitive relationships involving complex shapes. It adaptively samples geometry boundaries and surrounding regions, then uses a transformer encoder-decoder trained through geometry reconstruction to create unified representations for points, polylines, and polygons. The abstract reports evaluation on two real-world datasets, with improved preservation of challenging polygonal topological relationships and significantly better downstream prediction and classification performance than state-of-the-art baselines; it gives no metrics or further methodological detail.

## Source excerpt

arXiv:2611.00111v1 Announce Type: cross Abstract: 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.