mlsanityData to 7 Oct 2026

SRAI: Towards Standardization of Geospatial AI

arXiv 2310.13098 ↗First posted 19 Oct 2023cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model using various architectures. It includes baseline models as well as more complex methods from published works. Those capabilities make it possible to use srai in a complete pipeline for geospatial task solving. The proposed library is the first step to standardize the geospatial AI domain toolset. It is fully open-source and published under Apache 2.0 licence.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 19 Oct 2023.

Builds on

11 of 40 references.

References · 11

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2018Deep contextualized word representationscited by 2,285
  3. 2021Datasets: A Community Library for Natural Language Processingcited by 526
  4. 2021A Large-Scale Study on Unsupervised Spatiotemporal Representation Learningcited by 214
  5. 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 109
  6. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  7. 2021Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tagscited by 22
  8. 2021Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mappingcited by 6
  9. 2021gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregionscited by 3
  10. 2021Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Datacited by 2
  11. 2023highway2vec -- representing OpenStreetMap microregions with respect to their road network characteristicscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2026Inferring Height from Earth Embeddings: First insights using Google AlphaEarthcited by 4
  2. 2025OBSR: Open Benchmark for Spatial Representationscited by 2
  3. 2024Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environmentscited by 0
  4. 2025Geospatial Machine Learning Librariescited by 0