Royal Military Academy

The Royal Military Academy is a military institution of university education responsible for the academic, military and physical training of future officers, and for the continuing advanced training of officers during their active career in the Defence department. Two entities within RMA are participating to the SafeShore proposal: The Unmanned Vehicle Centre and the Signal and Image Centre.

The Unmanned Vehicle Centre of the Department of Mechanics of the Belgian Royal Military Academy is a research laboratory specialized in the design, development and control of unmanned systems, both unmanned ground vehicles and unmanned aerial vehicles. The lab conducts research in varied domains, ranging from very fundamental aspects to the development of prototype products. Research activities include robotics for crisis management operations and humanitarian demining.

The Signal & Image Centre SIC is a research centre having its main expertise in signal and image processing applied to the fields of remote sensing, pattern recognition, data fusion, image restoration and image compression. The research activities include the processing of panchromatic, multi-spectral, hyper-spectral and full polarimetric SAR remote sensing data.

RMA not only acts as a pure research institute, but is closely committed to the end users and has a proven track record of operating in the field with the end-users. As a testimony of this statement, the recent relief support mission to Bosnia by RMA can be mentioned. RMA deployed an RPAS to help with the relief operations after the floods in Spring 2014, together with the Belgian First Aid and Support Team. During this very successful operation, RMA assisted the local civil protection agencies and demining teams and international relief teams and brought them into contact with the advantages of using RPAS systems.

As a military institute, RMA has access to an international network of relevant end-users (border protection agencies, procurement agencies). Homepage:

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SafeShore is a project funded by the European Commission
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700643