SRAI: Towards Standardization of Geospatial AI

Abstract

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.

Accepted for the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 2023)

References in corpus (5)

• Array Programming with NumPy
• Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags
• Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data
• highway2vec -- representing OpenStreetMap microregions with respect to their road network characteristics
• gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregions