Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Boudewijn van Leeuwen, Zalán Tobak, Ferenc Kovács

Journal of Environmental Geography 2020 article cited by 33

Source Open-access PDF More in Climate & Environment

importance 4/10 ai assisted Remote-Sensing Image Classification agricultural remote sensing stage: research open-source model

Problem

Temporary inundations in flat agricultural areas of southern Hungary disrupt farming and cause large financial loss; mapping land use/land cover, especially inundated areas, from medium-resolution satellite imagery is needed.

Why it matters

Inundations disrupt agricultural practices and can cause large financial loss.

What the AI did

Open-source implementations of random forest, support vector machine and an artificial neural network were trained on the same Sentinel-2 dataset and their classifications compared qualitatively and quantitatively.

Results

Qualitative only: accuracy high for all methods, neural network best in quantitative spatial comparison; no numeric values in abstract

Impact

Shows open-source ML can classify Sentinel-2 imagery for inundation mapping with high accuracy.

Record

Venue	Journal of Environmental Geography
Published	2020
Work type	article
Authors	Boudewijn van Leeuwen, Zalán Tobak, Ferenc Kovács
Citations	33 (OpenAlex, at import time)
References	32
Model or method	random forest, support vector machine, artificial neural network
Organization	not stated in source
Primary source	https://doi.org/10.2478/jengeo-2020-0005
Open access	diamond
Keywords	machine learning, land cover classification, Sentinel-2, inundation, random forest, neural network

Importance rating

4 / 10

real but narrower advance

Inundations disrupt farming and cause large financial loss per abstract, but regional focus, qualitative results only, no quantified scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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