Deep Neural Network for Water Body Identification

Abstract

Water bodies such as lakes, rivers, reservoirs, and wetlands play a crucial role in environmental monitoring, agriculture, and urban planning. Accurate detection and mapping of these water resources are important for sustainable water management and disaster prevention. With the rapid growth of satellite and aerial imagery, automated techniques have become essential for analyzing large volumes of geospatial data. Traditional image processing approaches often face difficulties in handling variations in lighting conditions, shadows, and complex landscapes. Image classification techniques have been widely used to identify water regions from remote sensing images. However, manual feature extraction and threshold-based approaches may reduce detection accuracy. In recent years, intelligent image analysis methods have improved the capability of extracting meaningful patterns from images. These techniques enable more reliable classification of water and non-water regions. This research focuses on detecting water bodies using an advanced neural network-based image classification model. The proposed approach aims to enhance accuracy and efficiency in identifying water regions from satellite imagery. KEYWORDS: Deep Neural Network (DNN), Water Body Detection, Satellite Imagery, Remote Sensing, Image Classification