December 4, 2025Earth Surface Processes and LandformsOpen Access

GIS‐ and RS‐based models for gully erosion susceptibility mapping using machine learning and remote sensing data

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NRNazir Ur RehmanAga Khan University HEHela ElmannaiPrincess Nourah bint Abdulrahman University

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Implication

This analysis demonstrates improved gully erosion mapping using machine learning techniques, indicating better strategies for soil erosion management.

Key Points

Gully erosion susceptibility mapping identified different severity levels across the region, helping to target interventions.
The study utilized machine learning methods, successfully detecting 1287 inventory points through various satellite images.
Evaluation incorporated topographic and climatic factors to map gully erosion risks effectively in Northern Pakistan.
Findings suggest enhanced decision-making for policymakers in addressing the socioeconomic impacts of soil erosion.

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Rehman et al. (2025) studied this question.

synapsesocial.com/papers/6930dc78ea1aef094cca2337 https://doi.org/10.1002/esp.70198

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Implication

This analysis demonstrates improved gully erosion mapping using machine learning techniques, indicating better strategies for soil erosion management.

GIS‐ and RS‐based models for gully erosion susceptibility mapping using machine learning and remote sensing data

Discussion

Member takes

Implication

Key Points

Cite This Study

GIS‐ and RS‐based models for gully erosion susceptibility mapping using machine learning and remote sensing data

Discussion

Member takes

Implication

Key Points

Cite This Study

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