SRAI: Towards Standardization of Geospatial AI
Piotr Gramacki, Kacper Le\'sniara, Kamil Raczycki, Szymon Wo\'zniak,, Marcin Przymus, Piotr Szyma\'nski
TL;DR
SRAI is an open-source Python library that standardizes geospatial AI workflows by providing tools for data acquisition, segmentation, and embedding model training, facilitating comprehensive geospatial analysis pipelines.
Contribution
It introduces the first standardized, open-source library for geospatial AI, integrating data handling, segmentation, and modeling in a unified framework.
Findings
Supports multiple algorithms for micro-region segmentation
Includes baseline and advanced embedding models
Enables complete geospatial AI pipeline development
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.
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Taxonomy
TopicsData Management and Algorithms · Geographic Information Systems Studies · Time Series Analysis and Forecasting
MethodsLib