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August 9, 2026Open Access

AI techniques for feature extraction, simplification and aggregation geospatial data

Polina Lemenkova

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Key Points

To utilize AI technologies for environmental mapping through the analysis of satellite images.
Applied Random Forest machine learning framework for satellite image analysis.
Analyzed land cover changes using GRASS GIS on Landsat data.
Evaluated image classification accuracy using kappa statistics and chi-square.
Mosaic vegetation and cropland changed by 8.42%, aquatic vegetation decreased by 13.59%.
Water bodies in the Etosha basin decreased significantly by 21.73% due to seasonal effects.
Stable development in rainfed croplands with 4.58% change, while salt hardpans changed significantly by 12.73%.

Implication

Demonstrates biodiversity changes in North Namibia via AI analysis of satellite images, highlighting environmental impacts.

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Cite This Study

Polina Lemenkova (2026) studied this question.

synapsesocial.com/papers/6a7c209506a85aed514b76fe https://doi.org/10.5281/zenodo.21875212

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