# SRAI: Towards Standardization of Geospatial AI

arXiv: https://arxiv.org/abs/2310.13098 · HTML: https://mlsanity.com/paper/2310.13098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning](https://mlsanity.com/paper/2104.14558/) (2021)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags](https://mlsanity.com/paper/2111.00970/) (2021)
- [Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping](https://mlsanity.com/paper/2108.07323/) (2021)
- [gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregions](https://mlsanity.com/paper/2111.00960/) (2021)
- [Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data](https://mlsanity.com/paper/2111.00990/) (2021)
- [highway2vec -- representing OpenStreetMap microregions with respect to their road network characteristics](https://mlsanity.com/paper/2304.13865/) (2023)

## Built on by (most cited first, 5 of 5)

- [Inferring Height from Earth Embeddings: First insights using Google AlphaEarth](https://mlsanity.com/paper/2602.17250/) (2026)
- [OBSR: Open Benchmark for Spatial Representations](https://mlsanity.com/paper/2510.05879/) (2025)
- [Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments](https://mlsanity.com/paper/2409.06730/) (2024)
- [Geospatial Machine Learning Libraries](https://mlsanity.com/paper/2510.02572/) (2025)
- [CartoVec: General-Purpose Location Embeddings from Open Map Data](https://mlsanity.com/paper/2610.17190/) (2026)

## 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.