African Political Communication (Media/Politics/Social) | 08 January 2012
AI-Enhanced Satellite Imagery in Land Use Mapping and Monitoring in Malawi: A Comparative Study
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Abstract
Recent advancements in AI have enabled more accurate land use mapping through satellite imagery, offering insights into agricultural productivity and environmental conservation. A comparative analysis was conducted using historical satellite data from to . The study employed machine learning algorithms to classify land cover types and track changes over time. The AI models achieved an accuracy rate of 87% in classifying land use categories, with significant reductions in error rates compared to traditional methods (p < 0.05). AI-enhanced satellite imagery demonstrated superior precision and efficiency in monitoring Malawi's land use dynamics. Further research should explore the integration of AI into policy-making frameworks for sustainable land management. AI, Satellite Imagery, Land Use Monitoring, Machine Learning, Agricultural Productivity Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.