Environmental Monitoring Using Satellite Imagery And Deep Learning Technique

Abstract

Environmental monitoring is noteworthy in sustainable management of natural resources, urban planning, reduction of disaster as well as redressing of the climate change. The application of space and time has been a significant limitation of space and time with the use of traditional methods of monitoring hence inappropriate in its application into large-scale real-time monitoring. The combination of satellite imagery and the positive results of deep learning technologies has become a possible solution to these issues. The large region enables a satellite image to achieve the very high-resolution and multi-spectral data which is very hard to achieve with deep learning models, especially, Convolutional Neural Networks (CNNs), which excel at extracting meaningful patterns in huge datasets. In this paper, an exhaustive technique of environmental observation using satellite images and deep learning is presented. Its methodology involves application of satellite image preprocessing, feature extraction, model training and performance measure in identification of environmental changes of deforestations, urban growth and water body change and land degradation. Experiments reveal that the offered method is effective and the accuracy of environmental changes detection is high in opposition to traditional machine learning methods. In this article, there are prominent limitations, including the reliance on high-quality labeled data, overlap by the cloud cover during the optical satellite-based observation, and computational resource consumption. The research directions automatically involve incorporation of multi Sensor datas, real-time monitoring aspects, as well as came up with lightweight models involving resource constrained setups in the future.