Working Paper Review Version 1 This version is not peer-reviewed

Machine Learning of Spatial Data

Version 1 : Received: 27 July 2021 / Approved: 6 August 2021 / Online: 6 August 2021 (14:18:55 CEST)

A peer-reviewed article of this Preprint also exists.

Nikparvar, B.; Thill, J.-C. Machine Learning of Spatial Data. ISPRS Int. J. Geo-Inf. 2021, 10, 600. Nikparvar, B.; Thill, J.-C. Machine Learning of Spatial Data. ISPRS Int. J. Geo-Inf. 2021, 10, 600.

Journal reference: ISPRS Int. J. Geo-Inf. 2021, 10, 600
DOI: 10.3390/ijgi10090600

Abstract

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

Keywords

spatial machine learning; spatial dependence; spatial heterogeneity; scale; spatial observation matrix; learning algorithm; deep learning

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