research-article

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SRAI: Towards Standardization of Geospatial AI

Authors:

Piotr Gramacki,

Kacper Leśniara,

Kamil Raczycki,

Szymon Woźniak,

Marcin Przymus,

Piotr SzymańskiAuthors Info & Claims

GeoAI '23: Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

Pages 43 - 52

https://doi.org/10.1145/3615886.3627740

Published: 20 November 2023 Publication History

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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Cited By

Manda VChristy VHlali A(2024)Current Trends, Opportunities, and Futures Research Directions in Geospatial Technologies for Smart CitiesRecent Trends in Geospatial AI10.4018/979-8-3693-8054-3.ch009(239-270)Online publication date: 2-Dec-2024
https://doi.org/10.4018/979-8-3693-8054-3.ch009
Kolluru VMungara SChintakunta AKolluru LTelaganeni C(2024)Revolutionizing Land Cover Analysis: A Systematic Review Of Geospatial Intelligence With Classification And SegmentationSSRN Electronic Journal10.2139/ssrn.4988857Online publication date: 2024
https://doi.org/10.2139/ssrn.4988857

Index Terms

SRAI: Towards Standardization of Geospatial AI

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cover image ACM Conferences

GeoAI '23: Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

November 2023

135 pages

ISBN:9798400703485

DOI:10.1145/3615886

Editors:
Shawn Newsam
University of California, Merced, CA, USA
,
Lexie Yang
Oak Ridge National Laboratory, TN, USA
,
Gengchen Mai
University of Georgia, GA, USA
,
Bruno Martins
University of Lisbon, Portugal
,
Dalton Lunga
Oak Ridge National Laboratory, TN, USA
,
Song Gao
University of Wisconsin, Madison, WI, USA

Copyright © 2023 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 November 2023

Revised: 12 October 2023

Received: 15 September 2023

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SIGSPATIAL '23: The 31st ACM International Conference on Advances in Geographic Information Systems

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Overall Acceptance Rate 17 of 25 submissions, 68%

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Cited By

Manda VChristy VHlali A(2024)Current Trends, Opportunities, and Futures Research Directions in Geospatial Technologies for Smart CitiesRecent Trends in Geospatial AI10.4018/979-8-3693-8054-3.ch009(239-270)Online publication date: 2-Dec-2024
https://doi.org/10.4018/979-8-3693-8054-3.ch009
Kolluru VMungara SChintakunta AKolluru LTelaganeni C(2024)Revolutionizing Land Cover Analysis: A Systematic Review Of Geospatial Intelligence With Classification And SegmentationSSRN Electronic Journal10.2139/ssrn.4988857Online publication date: 2024
https://doi.org/10.2139/ssrn.4988857

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