Work

Gully erosion studies using artificial intelligence approaches: A systematic review

Year: 2026

Type: review

Abstract: • Data-backed identification of the best performing AI models in gully erosion studies • Development of framework for accuracy metrics • Development of hierarchical framework for semantic-segmentation • Identification of the most important conditioning factors in gully erosion studies • Influence of climate and soil type on gully erosion conditioning factors

Source: CATENA

DOI: 10.1016/j.catena.2026.110249

Authors: Obinna Uzodimma Ubani, Esdras Ngezahayo, Ian Jefferson

Institution: University of Birmingham

Language: English

Topic: Soil erosion and sediment transport

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Life on land, Climate action

Open Access status: hybrid

Estimated APC paid: $3,680