Deep Learning Applications in Remote Sensing for Climate Change Monitoring

As the threat of climate change intensifies, environmental monitoring has now become more critical. Remote sensing enables the extraction of complex patterns and insights from vast amounts of satellite and aerial imagery. These advanced algorithms enhance the detection, classification, and prediction of climate-related phenomena such as deforestation, glacial retreat, sea-level rise, and extreme weather events. Deep Learning Applications in Remote Sensing for Climate Change Monitoring explores deep learning techniques and remote sensing technology to monitor climate change. It provides cutting-edge research, methodologies, and real-world applications of deep learning in remote sensing for monitoring and mitigating climate change. Covering topics such as climate science, remote sensing, and deep learning tools, this book is an excellent resource researcher and academicians in remote sensing, climate science, and deep learning, as well as policymakers, industry professionals, and international organizations working in sustainability and climate resilience.

Uzair Aslam Bhatti was born in 1986. He received the Ph.D. degree in information and communication engineering, Hainan University, Haikou, Hainan, in 2019. He is pursuing the Postdoctoral degree in implementing Clifford algebra algorithms in analyzing the geospatial data using artificial intelligence (AI) with Nanjing Normal University, Nanjing, China. His areas of specialty include AI, machine learning, and image processing.