The problem: Geo-information applications aimed at solving real large-scale problems based on GNSS networks must deal with huge volumes of dynamically varying data; multiple, potentially conflicting, and often changing objectives; and highly complex models for describing the problem environment. For critical real-life applications, the ability to rapidly develop an efficient, robust, and accurate working model that quickly produces solutions is essential for their outcome to be of value. Even a few minutes’ delay in reaching a decision could make the difference between life and death. The solution: Our idea, a generic GNSS network for disaster monitoring and a management-modelling tool that combines large-scale database searches with artificial intelligence techniques at the core of its decision-making calculation engine. This system is capable of dealing with dynamically evolving and potentially unpredictable environments and constraints. Key to the capabilities of this tool is real-time dynamic optimisation and the development of an intelligent self-learning system upon which the decision-making criteria are based. The system can easily be modified and adapted in near real time.