Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Spatial Decision Support Systems with Automated Machine Learning: A Review

Version 1 : Received: 28 September 2022 / Approved: 29 September 2022 / Online: 29 September 2022 (10:06:18 CEST)

A peer-reviewed article of this Preprint also exists.

Wen, R.; Li, S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS Int. J. Geo-Inf. 2023, 12, 12. Wen, R.; Li, S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS Int. J. Geo-Inf. 2023, 12, 12.

Abstract

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

Keywords

Spatial; Decision Support; Machine Learning; Automation; Framework; System; SDSS; AutoML; GIS

Subject

Environmental and Earth Sciences, Remote Sensing

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