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UID:pretalx-foss4g-europe-2024-JSMTZW@talks.osgeo.org
DTSTART;TZID=EET:20240703T160000
DTEND;TZID=EET:20240703T163000
DESCRIPTION:Soil loss by water erosion is projected to increase by 13 – 2
 2.5% in the European Union (EU) and United Kingdom (UK) by 2050\, leading 
 to loss of cultivable land and soil structure degradation. Accurate mappin
 g of soil erosion is crucial for identifying vulnerable areas and implemen
 ting sustainable land management practices. In this study\, we introduce m
 achine learning (ML) models to map soil erosion\, leveraging their capabil
 ities in categorical mapping. Unlike previous applications that primarily 
 mapped the absence or presence of a soil erosion class\, we propose an ens
 emble strategy using three ML ensemble models (CatBoost\, LightGBM\, XGBoo
 st) with remote sensing data to map four classes of soil erosion (i.e No G
 ully/badland\, Gully\, Badland\, Land-slides). The proposed model effectiv
 ely captures spatiotemporal variations over Europe in the period of 2000 
 – 2022\, with particular precision in mapping Land-slides. The proposed 
 method advances soil erosion mapping across different spatial and temporal
  scales particularly in the EU\, contributing to the development of target
 ed conservation strategies and sustainable land management practices.
DTSTAMP:20261016T025439Z
LOCATION:Omicum
SUMMARY:Mapping Soil Erosion Classes using Remote Sensing Data and Ensemble
  Models - Ayomide Oraegbu\, Emmanuel Jolaiya
URL:https://talks.osgeo.org/foss4g-europe-2024/talk/JSMTZW/
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