Artificial intelligence (AI) is paving the way for a new era of algorithms focusing directly on the information contained in the data, autonomously extracting relevant features for a given application. While the initial paradigm was to have these applications run by a server hosted processor, recent advances in microelectronics provide hardware accelerators with an efficient ratio between computation and energy consumption, enabling the implementation of AI algorithms “at the edge.” In this way only the meaningful and useful data are transmitted to the end-user, minimizing the required data bandwidth, and reducing the latency with respect to the cloud computing model. In recent years, European Space Agency (ESA) is promoting the development of disruptive innovative technologies on-board earth observation (EO) missions. In this field, the most advanced experiment to date is the $\Phi $ -sat-1, which has demonstrated the potential of artificial intelligence (AI) as a reliable and accurate tool for cloud detection on-board a hyperspectral imaging mission. The activities involved included demonstrating the robustness of the Intel Movidius Myriad 2 hardware accelerator against ionizing radiation, developing a Cloudscout segmentation neural network (NN), run on Myriad 2, to identify, classify, and eventually discard on-board the cloudy images, and assessing the innovative Hyperscout-2 hyperspectral sensor. This mission represents the first official attempt to successfully run an AI deep convolutional NN (CNN) directly inferencing on a dedicated accelerator on-board a satellite, opening the way for a new era of discovery and commercial applications driven by the deployment of on-board AI.

Earth Observation spacecraft play a pivotal role in various critical applications impacting life on Earth. Historically, these systems have adhered to conventional operational paradigms, namely the “mow-the-lawn” and “bent pipe” approaches. In these paradigms, operational schedules are formulated on the ground and subsequently uploaded to the spacecraft for execution. Execution involves either systematically acquiring vast amounts of data (mow-the-lawn) or targeting specific areas of interest as defined by end users or operators. We aim to depart from these traditional methodologies by integrating onboard Artificial Intelligence, near real-time communication, and new observing strategies in one system called CogniSAT-6. These innovations will amplify the amount, speed, and quality of the information yielded by such a system by up to an order of magnitude. This paper provides an overview of the current state of the art in autonomous Earth Observation spacecraft and the application of onboard processing in Earth Observation spacecraft. An overview is given of the CogniSAT-6 mission, its concept of operations, system architecture, and data processing design. In addition, the first results of our in-orbit functional tests are presented. Since we believe that the technology presented here will have a significant impact on society, an ethical framework for such systems is presented. Finally, the benefits of the technology and implications for Earth Observation systems going forward are discussed.

Star trackers are navigation sensors that are used for attitude determination of a satellite relative to certain stars. A star tracker is required to be accurate and also consume as little power as possible in order to be used in small satellites. While traditional approaches use lookup tables for identifying stars, the latest advances in star tracking use neural networks for automatic star identification. This manuscript evaluates two low-cost processors capable of running a star identification neural network, the Intel Movidius Myriad 2 Vision Processing Unit (VPU) and the STM32 Microcontroller. The intention of this manuscript is to compare the accuracy and power usage to evaluate the suitability of each device for use in a star tracker. The Myriad 2 VPU and the STM32 Microcontroller have been specifically chosen because of their performance on computer vision algorithms alongside being cost-effective and low power consuming devices. The experimental results showed that the Myriad 2 proved to be efficient and consumed around 1 Watt of power while maintaining 99.08% accuracy with an input including false stars. Comparatively the STM32 was able to deliver comparable accuracy (99.07%) and power measurement results. The proposed experimental setup is beneficial for small spacecraft missions that require low-cost and low power consuming star trackers.

The transformation of food production through digital tools and technologies requires not only advanced devices and processing capabilities, but also a holistic agricultural framework that unifies multiple types of stakeholders across the agri-food chain. This paper introduces the AGRARIAN platform, a stakeholder-centric, self-organizing Cyber-Physical System designed to answer the modern and future demands of sustainable agriculture, unifying farmers, developers and policy makers, all in one dynamic and scalable ecosystem. Integrating hybrid communications, lightweight services orchestration, continuous development and cross-farm decision support, the AGRARIAN platform offers a complete solution for the next generation of farm management. For the platform validation, field trials were performed for the use case of livestock monitoring in a remote area, using commercial communication links (5 G, WiFi and satellite) for real-world system evaluation. The measurements and the results analysis confirmed the capacity of the AGRARIAN platform to support the necessary functions and services, opening the way towards modern and sustainable farming.

Building Artificial Intelligence (AI) models for Earth Observation (EO) satellites can be a difficult task since there is often no, or very limited, real training data available for a particular sensor. Even if data exists from a previous mission, a change in the sensor may make the model unsuitable. Therefore, being able to update the trained model, using the actual data provided by the sensor after the satellite is in orbit, is highly important. Thus, the main purpose of this work is to investigate and develop how to update remotely a model placed in a satellite in orbit, and how to control or specify the update size using the training parameters. The goal is to improve the accuracy performance of the model, using new data acquired from the in-orbit satellite, and to achieve this performance improvement under the criterion of reducing the size of the model update that is uplinked to the satellite. The method proposed selects which Convolutional Neural Network (CNN) layer weights must be modified and which must be fixed during training in order to maximize the accuracy increase and minimize the update file size. For a sample network, and without the proposed method, results show an update size of 44.5MB for the retrained network (with an original network size of 48.9MB), with retraining using the new data (such as that acquired from a satellite post-launch) resulting in an accuracy improvement from 78.4% to 79.9%. With the proposed Efficient Network Update (ENU) method, the generated post-training network update is only 18MB in size, but still achieves an accuracy of 78.9%. This demonstrates the reduced data bandwidth requirement to update the model, while gaining accuracy on the original trained network.