LiDAR Image-Based Climate Change Detection with Their Economic Ecosystem Modelling: Artificial Intelligence Application

Over the past few decades, change detection has been the subject of more research in both close-quarters and distant sensing due to its significance in environment monitoring and database updating. The advancement of sensing technology has made data collecting more accessible and economical, which has increased the amount of data available from different sensing platforms. Studying these atmospheric components has become much easier because of LiDAR technology, which can measure backscattered signals and explore the atmosphere at very high spatial and temporal resolution. This research proposes a novel technique in satellite data security analysis with climate change detection utilizing ML algorithm. Input has been collected as input as satellite data based on security and processed for noise removal as well as normalisation. Processed data has been segmented and classified using SegNet histogram graph cut contour model and transfer AlexNet adversarial Markov model. The experimental analysis has been carried out in terms of data integrity, precision, normalised coefficient, recall and F-1 score. The proposed technique attained data integrity of 98%, F-1 score 95%, PRECISION of 97%, normalised coefficient of 93% and RECALL of 92%.