Abdul Hanan
Field of work
I am skilled in the integration of remote sensing, earth observation, and advanced deep learning techniques. My primary interest is in wildfire prediction, using machine learning to anticipate and manage these natural calamities. Furthermore, I contribute to infrastructure monitoring by utilizing novel approaches to improve the resilience and sustainability of critical systems.
My research goes into the complex area of wildfire forecast by systematically analyzing thermal data from satellites and Synthetic Aperture Radar (SAR) data. I intend to find trends, discrepancies, and roots within these datasets using cutting-edge deep learning and machine learning approaches in order to improve the accuracy and timeliness of wildfire forecasts. This multimodal approach extends to the wider realm of infrastructure monitoring, where I investigate the integration of thermal and SAR data to develop effective techniques for protecting vital systems and improving catastrophe resilience.
Data Science Group in the Department of computer technology, electrical technology and science