The big data issue
I was fortunate a couple of weeks ago to visit the European Space Agency (ESA) in Noordwjik, The Netherlands. The place is really buzzing with the imminent launch of the first of five Sentinel missions. Sentinel 1 is due to be launched in spring 2014, a polar-orbiting constellation of two satellites focusing on weather, day and night radar imaging mission for land and ocean services.
The main objective of the Sentinels under GMES (now renamed Copernicus) is to provide data, information, services, and knowledge that supports the European goal for sustainable development and global governance of the environment. The idea is that the data generated will support decisions that will effect projects and challenges we face in many applications like: urban area management, sustainable development, nature protection, regional and local planning, agriculture, forestry and fisheries, health, emergency management, infrastructure, transport, and tourism. The issue that I and others see, is how to ensure that the plethora of data captured by the Sentinels, is used in a close to 'real time' environment as possible, rather than latency building up between capturing data and consuming data.
In support of this onslaught of data, image analysts will need more products and solutions that not only focus on the environmental analytics, but also provide services that create, process, store, exploit, and disseminate data and information. All of this will ultimately allow us to solve problems using big data.