Abstract
Abstract: Satellite imagery plays a crucial role in overseeing and safeguarding natural resources, particularly water bodies like lakes, rivers, and oceans, which hold significant environmental and human value. Detecting water body pixels in satellite images is a key aspect of environmental management and planning. Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions for tasks like object detection and segmentation in image processing. This study leverages CNNs and RNNs to precisely identify water body pixels in satellite images, aiming to develop a model capable of accurately distinguishing between water and non-water pixels. Such a model holds practical significance for environmental monitoring and management efforts
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