Proceedings of the 2017 conference on Big Data from Space (BIDS' 2017) : 28th-30th November 2017 Toulouse (France)

Big Data from Space refers to Earth and Space observation data collected by space-borne and ground-based sensors. Whether for Earth or Space observation, they qualify being called 'big data' given the sheer volume of sensed data (archived data reaching the exabyte scale), their high velocity (new data is acquired almost on a continuous basis and with an increasing rate), their variety (data is delivered by sensors acting over various frequencies of the electromagnetic spectrum in passive and active modes), as well as their veracity (sensed data is associated with uncertainty and accuracy measurements). Last but not least, the value of big data from space depends on our capacity to extract information and meaning from them. The goal of the Big Data from Space conference is to bring together researchers, engineers, developers, and users in the area of Big Data from Space. It is co-organised by ESA, the Joint Research Centre (JRC) of the European Commission, and the European Union Satellite Centre (SatCen). The 2017 edition of the conference was hosted by CNES and held at the Pierre Baudis Convention Centre in Toulouse (France) from the 28th to the 30th of November 2017. These proceedings consist of a collection of 126 short papers corresponding to the oral and poster presentations presented at the conference. They are organised in sections matching the order of the conference sessions followed by the contributions that were presented during the poster session, also organised by topics. They provide a snapshot of the current research activities, developments, and initiatives in Big Data from Space. While a continued number of contributions are devoted to infrastructures and platforms enabling to exploit the value behind the volume, velocity, and variety of Big Data from Space, this third edition of the Big Data from Space conference shows a sharp increase of applications particularly related to large scale analysis including the temporal dimensions in view of better understanding the dynamics of the processes that are shaping our planet and our universe. Other new trends regard the information extraction using advanced machine learning techniques such as those based on deep learning and convolution neural networks. The development of new standards to ensure the interoperability of Big Data from Space is also gaining attention similarly to data cubes and multidimensional array representations. All these topics as well as other generic key aspects of big data are mirrored onto dedicated sections in these proceedings.