AI-aided satellite imagery has been increasingly employed for land use mapping in various regions, including Africa. In Equatorial Guinea, such technologies offer potential benefits for monitoring and managing natural resources. The study utilizes high-resolution satellite data collected over two years from multiple sources. Artificial Intelligence algorithms were applied for image processing and classification tasks. Comparative metrics such as precision, recall, and F1 score were used to evaluate outcomes. Initial findings indicate an accuracy rate of 92% in land use mapping with AI-assisted methods versus 85% using conventional techniques. The study also reveals a significant reduction in processing time by 40%, emphasising the efficiency gains. The results suggest that AI-aided satellite imagery can be a reliable and efficient tool for land use monitoring, offering substantial benefits over traditional methods in terms of accuracy and speed. Given the promising outcomes, further research should focus on integrating these technologies into existing management frameworks to enhance sustainable resource utilization practices. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.