The rapid changes in Deep Learning (DL) have made better performing models for Remote Sensing Images (RSIs), particularly for semantic segmentation and multi-class classification. This study looks at how two common DL structures, U-Net and DeepLabV3+, perform on segmentation tasks, while 201-layer Densely Connected Convolutional Network (DenseNet201- CNN) is compared to Visual Geometry Group 16-layer network (VGG16) for classification tasks. The dataset for segmentation has aerial images of Dubai labeled with pixel-level segmentation across six classes: Building, land, road, vegetation, water, and unlabeled. The classification data set called Remote Sensing Image-Collection of Benchmark 256 (RSI-CB256) has Remote Sensing (RS) pictures sorted into four groups: Cloudy, desert, green_area, and water. DeepLabV3+ demonstrated better training performance and convergence behavior compared to U-Net, exhibiting more stable learning and efficient boundary detection during segmentation. While both models performed competitively, DeepLabV3+ consistently showed stronger generalization capability, making it more effective in delineating complex land cover boundaries. In contrast, U-Net displayed sensitivity to hyperparameters and greater variation in performance, indicating the need for further tuning and regularization. U-Net was good initially but had varied performance and was sensitive to hyperparameters suggesting the need of better regularization techniques. In terms of the classification, DenseNet201-CNN did better than VGG16 in precision, recall, and F1-score for all categories. Notable performance gains were observed in “cloudy” and “desert” classes where DenseNet201-CNN model demonstrated significantly fewer misclassifications. Overall, DenseNet201-CNN outperformed VGG16 in terms of total classification accuracy. These results establish DenseNet201-CNN as a superior choice in RSI classification tasks in this study.

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