Abstract
Earth observation (EO) with satellites has been applied in different fields such as environment monitoring, natural disasters response, emergencies management, civil security, intelligence, maritime surveillance, and many others. Some of the application fields are very demanding in terms of system revisit time and product delivery time. This is the case of responses to natural disasters.
However EO still presents critical challenges to overcome in order to cover the actual demand of services: (i) high revisit time, (ii) high response time, and (iii) easy and instant access to EO products. To increase the revisit time and broaden the applications of the remote sensing, new space concepts such as constellations and formations of satellites have been developed. However, the traditional ground infrastructures, which are required to process and store data, are expensive. Moreover, their limited scalability as well as their limited flexibility to manage large and variable amounts of imagery data shall also be considered.
Along this work, we propose a cloud infrastructure for data management to be validated with a constellation of 17 satellites acquiring the Earth’s surface on a daily basis in order to offer high added value services for highly demanding applications. The satellites download the raw data images in a network of 12 ground stations distributed around the world to provide global coverage. The cloud system is based on previous works carried out by the research group. Thus the cloud infrastructure is tested and evaluated for its use in the EO sector and applied to different realistic scenarios, including an intensive comparison with a traditional system responding to the Lorca’s earthquake, which occurred in Spain in 2011.