<![CDATA[Natural resource monitoring increasingly relies on satellite and airborne remote sensing to observe land cover change, forest loss, agricultural conditions, and water dynamics over large and often inaccessible areas. Conventional pixel-based and shallow machine-learning classifiers struggle to exploit the spatial, spectral, and temporal complexity of modern multi-sensor archives. This paper reviews and synthesizes recent developments in artificial intelligence (AI) and deep learning (DL) for remote sensing image analysis, focusing on four application domains central to natural resource management: land use/land cover classification, forest and deforestation monitoring, agricultural and crop monitoring, and water resource and flood mapping. Literature spanning convolutional neural networks, encoder-decoder segmentation architectures, and attention-based transformer models is compared in terms of methodology, sensor modality, and reported performance. A generalized processing workflow and an integrated cross-domain conceptual framework are proposed to guide the design of operational AI-based monitoring systems; unlike prior single-domain reviews, this synthesis is explicitly cross-domain, linking architecture choice to operational constraints shared across land cover, forest, agriculture, and water monitoring rather than treating each application silo in isolation. The review finds that transformer and hybrid CNN-transformer architectures tend to outperform purely convolutional baselines on scene-level classification tasks, although this advantage depends on application domain, dataset characteristics, and evaluation protocol and is less consistently observed for dense pixel-level segmentation, where U-Net-family encoder-decoders remain the dominant choice due to their balance of accuracy and computational cost. Persistent challenges include scarcity of high-quality labeled data, limited cross-region generalization, high computational demand, and limited interpretability of deep models for policy-relevant decision making. The paper concludes that multimodal data fusion, self-supervised pretraining, and explainable AI represent the most promising directions for advancing AI-driven natural resource monitoring toward operational, trustworthy deployment.]]>
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