Satellite Image-Based Environmental Change Detection Using Deep Learning

Abstract

Environmental change detection through satellite imagery plays a crucial role in monitoring and assessing the impacts of human activities and natural phenomena on land cover, climate, and ecosystems. Traditional methods of environmental change detection often face limitations in terms of accuracy, scalability, and automation. This paper explores the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for improving the detection of environmental changes using satellite imagery. The study utilizes multi-temporal satellite data from various sources such as Landsat and Sentinel, with a focus on identifying significant changes in land use, deforestation, and urban expansion. Data preprocessing techniques, including image normalization and cloud removal, are employed to enhance model accuracy. The deep learning model is trained and evaluated using standard performance metrics, including accuracy, precision, recall, and Intersection over Union (IoU). Results demonstrate that deep learning-based models significantly outperform traditional methods in terms of both detection accuracy and computational efficiency. The findings highlight the potential of deep learning models for large-scale environmental monitoring and provide insights into overcoming existing challenges in satellite image analysis. This study contributes to the field by offering a robust, automated framework for environmental change detection, which can be utilized for various applications, including urban planning, agriculture, and disaster response.