ARTIFICIAL INTELLIGENCE-POWERED LANDSCAPE VISION TRANSFORMATION TECHNIQUE FOR EARLY FLOOD RISK DETECTION THROUGH SATELLITE MAPPING SYSTEM USING DEEP FEATURE ENGINEERING

Environmental development depends entirely on natural resources, such as covering landscapes based on human lives and technological improvement. Specifically, the sloping mountain regions of land and their nature depend on sliding partitions; due to large floods, human lives are affected mainly by sudden causes of disasters. This paper addresses the critical issue of early flood risk detection in sloping mountain regions, where human lives and technological infrastructure are heavily reliant on natural resources. Satellite images and AI techniques are crucial developments to make analysis effective. Still, traditional methods fail to analyze the feature mapping and presence in feature variability due to increasing time frames, resulting in a high recall, which means that fables are unable to detect the flood quantity from regions where rainfall occurred, regions, and landscape regions. To resolve this problem, we propose an AI-powered landscape Vision Transformation Technique (LVTT) For Early Flood Risk Detection through a Satellite Mapping System Using Deep Feature Engineering. Initially, the landscape region images are collected from the satellite monitoring system. An adaptive Gaussian filter carries out the preprocessing. The historic color difference-based Slice Window Canny Edge Detection (SWCES) is used to segment the two-layer flooding and water flow region with dependable features. Then, Region Scaled Ant Colony Optimization (RSACO) for selecting the variability region features mutually depends on detecting the flooding variation region. Finally, the Hyper Capsuled LSTM Gated Convolution Neural Network (LSTMG-CNN) predicts that the flood region will cover the risk region of landscapes for early detection of floods to safeguard human lives. The proposed system produces high performance by predicting in the early stage of flooding variation limits depending on the flood flow through the image, progressing to achieve a high precision rate and recall rate with improved detection accuracy than any conventional methods.