--The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we address the Agile Earth Observation Satellite Scheduling Problem (AEOSSP), which involves determining the optimal sequence of target observations to maximize overall observation profit. T o this end, we define a set of priority indicators and develop a constructive heuristic method, further enhanced with a Local Search (LS) strategy. The results show that the proposed algorithm provides high-quality information by increasing the resolution of the collected frames by up to 10% on average, while reducing the variance in the monitoring frequency of the targets within the instance by up to 83%, ensuring more up-to-date information across the entire set compared to a First-In First-Out (FIFO) method.

Dublin start-up Ubotica has brought its AI technology into orbit aboard a next-gen ESA satellite. Dublin-based Ubotica Technologies has announced that its AI tech has gone into orbit aboard the Earth observation satellite PhiSat-1, which was launched along with 52 other satellites on a European Space Agency (ESA) Vega rocket yesterday (3 September). The satellite is part of a programme funded by ESA and supported by Enterprise Ireland, in which deep-learning technology for the in-orbit processing of Earth observation data is being deployed on a European satellite for the first time. Ubotica's CVAI technology, built on the Intel Movidius Myriad 2 vision processing unit, will allow the satellite to make its own decisions rather than relying on humans down on the planet's surface, resulting in faster, more efficient applications being deployed on the satellite. In this instance, Ubotica's AI tech is being tasked with automatic cloud detection on images captured by the satellite's advanced hyperspectral sensor.

An artificial intelligence (AI) processing chip developed in Ireland has been included on board a satellite that blasted off into space from French Guiana early this morning. The PhiSat-1 is the first European satellite carrying out an experiment to demonstrate how on-board deep-learning technology can speed up the rate at which observation data is processed and transmitted to Earth. The AI chip, developed by Dublin-headquartered robotics start-up Ubotica Technologies, has been installed on the satellite so decisions can be made on board more quickly than on the ground. It also helps reduce the data load sent back to Earth. Ubotica's Myriad 2 AI chip uses architecture originally developed by another Irish technology company, Movidius, acquired by Intel in a multimillion euro deal in 2016.

The UK government is setting aside £2.6 million for new satellite and drone technology that could deliver essential supplies during the coronavirus lockdown. The UK Space Agency (UKSA) is funding new solutions to deliver equipment such as test kits, masks, gowns and goggles for frontline NHS staff. The joint initiative with the European Space Agency could lead to vital equipment soaring through British skies via drones to support the NHS in tackling COVID-19. Companies can submit their proposals, including ideas for deployment and a pilot phase, on the European Space Agency (ESA) website. The UK's space industry is also looking for ways to combat the spread of coronavirus and preventing future epidemics using satellites.

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

A few months from now will see the launch of the first European satellite to demonstrate how onboard artificial intelligence can improve the efficiency of sending Earth observation data back to Earth. Dubbed ɸ-Sat, or PhiSat, this revolutionary artificial intelligence technology will fly on one of the two CubeSats that make up the FSSCat mission--a Copernicus Masters winning idea. As the overall 2017 Copernicus Masters winner, FSSCat, was proposed by Spain's Universitat Politècnica de Catalunya and developed by a consortium of European companies and institutes. The two CubeSats, each about the size of a shoebox, will collect data, which will be made available through the Copernicus Land and Marine Environment services, using state-of-the-art dual microwave and hyperspectral optical instruments. They also carry a set of intersatellite communication technology experiments.