Deep Learning-Driven Visual Analytics Framework for Next-Generation Environmental Monitoring

Abstract

In this paper, we propose a deep learning-based visual analytics pipeline for next-generation environmental monitoring with multispectral and temporal remote sensing data. We used large-scale benchmark images (MODIS, Landsat-8, Sentinel-2 to record a wide range of the land-use/land-cover, vegetation cover, atmospheric and water-body features. The pre-processing pipeline consisted of noise filtering, normalization and dimensionality reduction through PCA to improve the quality of data and model parsimony. The key hyperparameters like learning rate, batch size and layers depth- of system were optimized with hybrid PSO optimization technique which enhanced the convergence behaviour and classification ability of model.Deep learning models, such as convolutional neural networks (CNNs) like VGG16, GoogleNet, and ResNet50, and transformer-based ones, have been used to extract spatial-temporal information out of the satellite images. The three different types of networks provided more generalization based on transfer learning to utilize the already trained ImageNet weights and then fine-tune them in the domain. The models proposed were tested in various environmental surveillance problems such as land-cover classification, vegetation health monitoring and detection of water-quality anomalies, which proved to be robust and adjustable to a variety of remote sensing problems.Experiments illustrate that ResNet50 can outperform other architectures in all datasets, i.e., it attains highest accuracy 95.2%, 94.6% and 90.8% for Sentinel-2, Landsat-8 and MODIS data sources, respectively with corresponding F1-score greater than 94% and AUC >0:96. These results demonstrate the successful application of optimized deep-learning models, which can ensure real-time and scalable deployed monitoring with high precision for remote-sensing images.