Precise mapping of water bodies is crucial for flood monitoring, disaster risk and response reduction, as well as sustainable water resource management. In this paper, we introduce a deep learning model for effective segmentation of rivers, lakes, and reservoirs from high-resolution Gaofen-2 satellite images. Leveraging the Five-Billion-Pixels dataset-more than 5 billion annotated pixels for 24 land cover classes—our approach solves the problem of segmenting water bodies on various terrains and environmental conditions. The proposed U-Net and ViT-UNet models, with the former employing Vision Transformers to enhance global context perception. For enhancing generalization, the dataset is augmented using Albumentations and flipping, rotation, and scaling transformations. Hybrid loss functions of Dice Loss, Binary Cross-Entropy, and Focal Loss are employed to handle class imbalance, especially for slender river segments. The ViT-UNet model attained 98.8% pixel accuracy, which mirrors its ability to preserve fine detail and large-scale spatial pattern. Mixed-precision training and the AdamW optimizer has enhanced the computational efficiency. Further, demonstrates the potential of transformer-based segmentation models for remote sensing achieved accuracy of 98% for environmental risk management and decision support in disaster-prone areas.

Agriculture is a big sector in nations like India, and it provides a living for many people. To improve crop productivity, it’s very necessary to identify and classify plant diseases and prevent them from spreading further so that they do not affect the whole plant. Artificial intelligence (AI) and computer vision can help detect plant diseases that humans cannot always catch and overcome the shortcomings of continuous human monitoring. In this article, we aim to detect and classify diseases in tomato and apple leaves using deep learning approaches and compare the results between different models. Because tomatoes and apples are important components of the human diet, crop waste can result in losses for both farmers and ordinary people. These plant diseases have an immediate and negative impact on both the amount and quality of yield. Crop diseases must be identified and prevented as soon as possible to improve crop yield. Therefore, we need to monitor and analyze the growth stages of the plants so that the farmers can produce disease-free and with minimal losses to the crop. Furthermore, we used the sequential convolutional neural network (CNN) model followed by transfer learning models like VGG19, Resnet152V2, Inception V3, and MobileNet and compared the models based on accuracy. The performance of the models was evaluated using various factors such as dropout, batch size, and the number of epochs. For both, the datasets, the tomato, and apple MobileNet architecture performed better than the other existing models.