AI-Enabled Satellite Data Analysis for Mapping Soil Erosion Hotspots and Uncovering Sediment Yield Drivers.
Keywords:
Artificial Intelligence, Machine Learning, Remote Sensing, GIS, Soil Erosion, Sediment Yield, Deep Learning, Satellite Image Processing, Spatial Analysis, Environmental InformaticsAbstract
The integration of Artificial Intelligence (AI), satellite remote sensing, and geospatial computing has revolutionized environmental monitoring and predictive analytics. Soil erosion and sediment yield estimation are critical challenges affecting ecological sustainability, agricultural productivity, and watershed management. Traditional soil assessment techniques are often constrained by limited spatial coverage, high operational costs, and low predictive efficiency. The present study explores the application of AI-enabled satellite data analysis for identifying soil erosion hotspots and determining sediment yield drivers using advanced computational techniques. The research incorporates machine learning algorithms, cloud-based geospatial processing, image classification techniques, and spatial data mining for environmental analysis.
Multi-source satellite datasets including Landsat-8, Sentinel-2, and Digital Elevation Models (DEM) were integrated within GIS and AI frameworks. Algorithms such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Deep Learning-based Convolutional Neural Networks (CNN) were employed to classify erosion-prone areas and evaluate sediment transport behavior. The findings indicate that AI-driven geospatial systems significantly improve prediction accuracy, automate large-scale environmental monitoring, and support real-time decision-making processes.
The study highlights the interdisciplinary role of computer science in remote sensing analytics, spatial intelligence, environmental modeling, and sustainable resource management. The proposed framework contributes to intelligent environmental monitoring systems capable of supporting climate adaptation strategies, watershed planning, and precision agriculture.
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