Deep Learning Methods for Remote Sensing

Dear Colleagues,

The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has been mainly driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep neural networks. This progress has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently. Among the various research areas that have been significantly impacted by this progress is remote sensing.

This Special Issue aims to gather cutting-edge contributions from researchers using deep learning for remote sensing. Contributions are accepted in different areas of application, including but not limited to environmental studies, precision agriculture, forestry, disaster response, building information modelling, infrastructure inspection, defense and security, benchmarking, and open-access datasets for remote sensing. Studies using active or passive sensors from satellites, airborne platforms, drones, and terrestrial vehicles are welcome.

Contributions can be submitted in various forms, such as research papers, review papers, and comparative analyses.

Prof. Dr. Moulay A. Akhloufi 
Dr. Mozhdeh Shahbazi
Guest Editors

Manuscript Submission Information

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