07-03, 16:00–16:30 (Europe/Tallinn), Omicum
Soil loss by water erosion is projected to increase by 13 – 22.5% in the European Union (EU) and United Kingdom (UK) by 2050, leading to loss of cultivable land and soil structure degradation. Accurate mapping of soil erosion is crucial for identifying vulnerable areas and implementing sustainable land management practices. In this study, we introduce machine learning (ML) models to map soil erosion, leveraging their capabilities in categorical mapping. Unlike previous applications that primarily mapped the absence or presence of a soil erosion class, we propose an ensemble strategy using three ML ensemble models (CatBoost, LightGBM, XGBoost) with remote sensing data to map four classes of soil erosion (i.e No Gully/badland, Gully, Badland, Land-slides). The proposed model effectively 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 targeted conservation strategies and sustainable land management practices.
Ayomide Oraegbu holds a Bachelor’s degree in Remote Sensing and GIS from the Federal University of Technology, Akure, Nigeria. He is a certified 365 Data Scientist, who has worked with various organizations as a Data Scientist. His skills and expertise include machine learning, deep learning, and software development. He has contributed to several projects, one of which involved developing an innovative model for predicting Soil Organic Carbon (SOC) density in space and time. This model was presented at the European Space Agency symposium on Earth Observation for Soil Protection and Restoration. In addition to his professional background, he is passionate about applying his knowledge in data science to solve earth science-related problems. Currently, Ayomide is an MSc student in Geospatial Technologies at the Universitat Jaume I (Spain) and the University of Münster (Germany).
Earth Observation and Geospatial Software Engineer.