Satellite-based change detection in multi-objective scenarios: A comprehensive review

Satellite-based change detection is essential for addressing multi-objective scenarios. This review provides a comprehensive analysis of its applications and methodologies, focusing on the challenges and advancements in identifying changes in urban areas using multi-temporal remote sensing data. The abundance of remote sensing data enables long-term research on changes, considering the complexities of identifying changes in diverse locations. Advancements in remote sensing technology have led to numerous applications, including urban growth surveillance, disaster response, and land cover change tracking. The study explores real-time change detection applications, emphasizing areas such as land use, environmental changes, urban expansion, and disaster evaluations. Deep learning approaches have emerged as crucial tools, surpassing traditional techniques and demonstrating effectiveness across various remote sensing data types. The research acknowledges the strengths of deep learning while recognizing its limitations and constraints. The article also delves into unconventional change detection techniques, particularly those based on artificial intelligence, highlighting the promising results of deep learning algorithms in evaluating images with different timestamps. The ultimate goal is to provide comprehensive knowledge of change detection methods to meet the growing demand for remote sensing. Additionally, the article outlines the current state and suggests future research directions in urban change detection to guide further studies.