Review on Hardware Devices and Software Techniques Enabling Neural Network Inference Onboard Satellites

Abstract

Neural networks (NNs) have proven their ability to deal with many computer vision tasks, including image-based remote sensing such as the identification and segmentation of hyperspectral images captured by satellites. Often, NNs run on a ground system upon receiving the data from the satellite. On the one hand, this approach introduces a considerable latency due to the time needed to transmit the satellite-borne images to the ground station. On the other hand, it allows the employment of computationally intensive NNs to analyze the received data. Low-budget missions, e.g., CubeSat missions, have computation capability and power consumption requirements that may prevent the deployment of complex NNs onboard satellites. These factors represent a limitation for applications that may benefit from a low-latency response, e.g., wildfire detection, oil spill identification, etc. To address this problem, in the last few years, some missions have started adopting NN accelerators to reduce the power consumption and the inference time of NNs deployed onboard satellites. Additionally, the harsh space environment, including radiation, poses significant challenges to the reliability and longevity of onboard hardware. In this review, we will show which hardware accelerators, both from industry and academia, have been found suitable for onboard NN acceleration and the main software techniques aimed at reducing the computational requirements of NNs when addressing low-power scenarios.

Full text

Citation: Diana, L.; Dini, P. Review on
Hardware Devices and Software
Techniques Enabling Neural Network
Inference Onboard Satellites. Remote
Sens. 2024,16, 3957. https://doi.org/
10.3390/rs16213957
Academic Editor: Silvia Liberata Ullo
Received: 30 September 2024
Revised: 21 October 2024
Accepted: 22 October 2024
Published: 24 October 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Review
Review on Hardware Devices and Software Techniques Enabling
Neural Network Inference Onboard Satellites
Lorenzo Diana 1,*,† and Pierpaolo Dini 2,†
1Independent Researcher, 56100 Pisa, Italy
2Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56100 Pisa, Italy;
pierpaolo.dini@ing.unipi.it
*Correspondence: ldiana.res@libero.it
†These authors contributed equally to this work.
Abstract: Neural networks (NNs) have proven their ability to deal with many computer vision tasks,
including image-based remote sensing such as the identification and segmentation of hyperspectral
images captured by satellites. Often, NNs run on a ground system upon receiving the data from
the satellite. On the one hand, this approach introduces a considerable latency due to the time
needed to transmit the satellite-borne images to the ground station. On the other hand, it allows the
employment of computationally intensive NNs to analyze the received data. Low-budget missions,
e.g., CubeSat missions, have computation capability and power consumption requirements that may
prevent the deployment of complex NNs onboard satellites. These factors represent a limitation
for applications that may benefit from a low-latency response, e.g., wildfire detection, oil spill
identification, etc. To address this problem, in the last few years, some missions have started adopting
NN accelerators to reduce the power consumption and the inference time of NNs deployed onboard
satellites. Additionally, the harsh space environment, including radiation, poses significant challenges
to the reliability and longevity of onboard hardware. In this review, we will show which hardware
accelerators, both from industry and academia, have been found suitable for onboard NN acceleration
and the main software techniques aimed at reducing the computational requirements of NNs when
addressing low-power scenarios.
Keywords: TinyML; low power; AI hardware accelerators; onboard satellite; artificial intelligence;
machine learning; deep learning; computer vision; industrial internet of things; embedded systems
1. Introduction
1.1. Motivations and Contributions
The satellite market is experiencing significant growth due to several interconnected
factors. The growing demand for faster and more reliable connectivity is one of the main
drivers of this expansion. With the proliferation of digital technologies and the internet,
there is a growing need for satellite-based solutions, especially in remote and unserved
areas. This need is further amplified by the rise of autonomous vehicles, drones, and the
Internet of Things (IoT), which require robust satellite communication capabilities [
1
,
2
].
Technological innovations have made satellites more efficient and affordable. The develop-
ment of smaller and lighter satellites with advanced capabilities, such as electric propulsion
and miniaturization, has reduced launch costs and improved operational efficiency. This
has led to an increase in the deployment of low Earth orbit (LEO) satellites, which offer
lower latency and faster data transmission compared to traditional geostationary satel-
lites [
3
]. Satellite applications are diversifying, covering areas such as communication,
Earth observation, military intelligence, and scientific research. The commercial communi-
cations sector, especially satellite internet services, has seen substantial growth due to the
growing interest in satellite television and imaging. In addition, the growing number of
space exploration missions and private investment in satellite technology are improving
Remote Sens. 2024,16, 3957. https://doi.org/10.3390/rs16213957 https://www.mdpi.com/journal/remotesensing

Remote Sens. 2024,16, 3957 2 of 29
the market outlook. Governments around the world are also investing heavily in satellite
infrastructure for various applications, including homeland security and disaster manage-
ment. Integrating artificial intelligence (AI) models onboard satellites offers significant
advantages that improve satellite operations, data management, and enable autonomous
decisions in space. These advantages include the following:
•
Improved Autonomy and Responsiveness: Onboard AI enables satellites to make
real-time decisions by processing data and responding autonomously without waiting
for instructions from ground control. This responsiveness is crucial for tasks such as
collision avoidance, given the increase in space debris. Furthermore, autonomous
systems can adapt to changing conditions in space, improving the reliability and
efficiency of missions [4–8].
•
Optimized Data Management: AI plays a vital role in data management, enabling
onboard processing. By analyzing and filtering data before transmission, AI signifi-
cantly reduces the volume of information sent to Earth, minimising bandwidth use
and storage costs [9–13].
•
Advanced Applications: AI improves Earth observation capabilities by improving the
quality of collected data. For example, AI can improve image resolution and detect
environmental changes, which are essential for monitoring natural disasters or the
impacts of climate change. Furthermore, for planetary exploration or lunar rover mis-
sions, onboard AI facilitates autonomous navigation and obstacle detection, making
these missions more efficient and less dependent on human intervention [14–17].
•
Support for Complex Operations: AI enables the coordination of multiple satellites
working together as a swarm, enabling complex operations that are difficult to manage
manually from Earth. Furthermore, AI systems can monitor satellite health and predict
potential failures, enabling proactive maintenance actions that extend the operational
life of satellites [18–20].
The adoption of onboard data processing (OBDP) technologies represents a paradigm
shift, allowing satellites to process data in situ, minimizing the volume of data trans-
mitted and enabling near-real-time analysis. This approach mitigates the inefficiencies
related to the transmission of large volumes of raw data to Earth for back-end process-
ing [
21
,
22
]. Key technologies enabling OBDP include digital signal processors (DSPs) and
field-programmable gate arrays (FPGAs), which offer parallel processing capabilities and
optimizations for the balance between performance and power consumption. These de-
vices are particularly suited to applications such as synthetic aperture radar (SAR) image
processing, where large volumes of data must be processed in real time [
23
]. Examples of
onboard data processing systems include AIKO’s data processing suite, Fraunhofer EMI’s
data processing unit, and KP Labs’ Leopard technology, which integrate FPGAs and AI
processors for real-time analytics of hyperspectral images on-board satellites [24].
1.2. Limitations for Adopting AI Onboard Satellites
The adoption of Artificial Intelligence (AI) onboard satellites faces several significant
limitations. These challenges stem from technical, environmental, and operational con-
straints that impact the feasibility and effectiveness of AI implementations in space. We
can group these limiting factors in three main categories:
• Hardware-related limitations.
Ensuring the reliability of AI models in the face of errors caused by radiation is
paramount. Satellites typically use radiation-hardened (rad-hard) processors to with-
stand the harsh space environment [
25
–
27
]. However, these processors are often
significantly less powerful than contemporary commercial processors, limiting their
ability to handle complex AI models effectively [
28
]. The performance gap makes
it challenging to deploy state-of-the-art AI frameworks, which require substantial
computational resources. Testing and validation processes must ensure that the AI
models function correctly under extreme space conditions. This often involves cre-

Remote Sens. 2024,16, 3957 3 of 29
ating simulations of these conditions, which can be both complex and costly [
29
].
Designing fault-tolerant systems capable of detecting and correcting errors in real time
is a complex task and can introduce computational overhead. Balancing reliability
with the available resources becomes necessary. Implementing radiation mitigation
and fault tolerance solutions can increase costs and resource requirements, making it
important to balance cost, performance, and resilience [30].
Satellites’ onboard computational and hardware resources are limited. Onboard AI
models may need considerable working memory to store model parameters and inter-
mediate results during computations. Many satellite systems are not equipped with
the necessary memory capacity, which restricts the complexity of the AI models that
can be deployed, creating the need for optimal resource allocation techniques [
31
].
Moreover, the power available on satellites is limited, which restricts the use of high-
performance chips that consume more energy. This results in a trade-off where lighter
and smaller satellites may not be able to support the power demands of advanced
AI processing units. Finally, the space environment presents unique challenges such
as extreme temperatures and radiation exposure, which can affect the reliability and
longevity of electronic components used for AI processing. Additionally, these condi-
tions complicate the design of AI systems that must operate autonomously without
human intervention.
• Model-related limitations.
Lack of Large Datasets: Effective AI models, especially those based on deep learning,
require large amounts of labeled training data to perform well. In many cases, particu-
larly for novel instruments or missions to unexplored environments, such datasets are
not available. This scarcity can hinder the model’s ability to generalize and perform
accurately in real-world conditions [
32
]. Model Drift and Validation: Continuous
validation of AI models is essential, especially for mission-critical applications [
33
].
This involves downlinking raw data for performance assessment and potentially
retraining models onboard, a process complicated by limited communication band-
width and high latency in space. Moreover, post-launch updates to models need to
be carefully managed. Limitations in communication [
34
] and the risks associated
with system malfunctions make it challenging to perform updates during a mission.
It is crucial to monitor models continuously and, if necessary, implement updates to
ensure ongoing reliability.
• Other limitations.
There are also limitations that are broad and that may affect not only AI applications,
such as unauthorized access risks. The integration of AI into satellite systems increases
vulnerability to hacking and unauthorized control. Ensuring cybersecurity [
35
–
37
]
is critical but adds another layer of complexity to the deployment of AI systems in
space. Cybersecurity measures such as encryption and authentication are vital to
protect AI models and data from potential threats. However, implementing these
mechanisms can be complex and resource-intensive. Care must be taken to ensure
that these security measures do not compromise the system’s overall performance.
Continuous monitoring for potential threats is also necessary, but this requirement
adds to the system’s workload.
In this paper, we review the most interesting hardware and software solutions aimed
to enable the use of AI, in particular neural networks (NNs), onboard satellites. The
rest of this paper is organized as follows. In Section 2, we introduce the most relevant
hardware solution presenting both commercially available hardware accelerators and the
most recent Field-Programmable Gate Array (FPGA)-based designs from academia. In
Section 3, we introduce the most interesting software methodologies and solutions that can
help in porting Neural Network (NN)-based algorithms onboard satellites. In Section 4,
we provide an overview of real case applications that can benefit from using onboard AI
algorithms that have been addressed by the research community. Finally, in Section 5we
provide the conclusion.

Remote Sens. 2024,16, 3957 4 of 29
2. Hardware Solutions
NNs usually require a considerable amount of hardware resources, i.e., memory and
computation capability. Small satellites, e.g., CubeSat, cannot satisfy these needs due to
cost and technical requirements. In particular, power consumption can be a stringent
limiting factor when running inference of NNs onboard this kind of satellite [
38
,
39
]. A
possible solution to reduce the power consumption when running NNs is to leverage a
hardware accelerator. Thanks to the higher efficiency related to NN workload computation,
hardware accelerators represent a valuable solution to enable the use of NNs onboard
small satellites. On the one hand, there are numerous commercial off-the-shelf hardware
accelerators for NNs available on the market [
40
]. Despite none of those accelerators having
been specifically designed to meet space-grade requirements, some of them have already
been tested for and used in short-term low Earth orbit satellite missions [
41
–
43
]. On
the other hand, many NN accelerators have been developed to be deployed on FPGAs.
These accelerators can be effectively used onboard satellites when deployed on space-
grade FPGAs. Deploying AI models on dedicated hardware, such as Vision Processing
Units (VPUs) or FPGAs, can deliver high-performance acceleration while reducing power
consumption [
44
]. However, implementing and configuring AI models on these types of
hardware requires specialized knowledge, and the process can be complex. Customizing
the hardware and programming introduces additional challenges, particularly when it
comes to updating models. For example, updating models on FPGAs often requires
hardware re-programming, a process that must be managed carefully to avoid errors or
malfunctions. To address these limitations, some hardware solutions are better suited for
specific applications. For instance, Application-Specific Integrated Circuits (ASICs) like
the Google Edge Tensor Processing Unit (TPU) are designed for low-power, high-efficiency
inference and have been tested for radiation tolerance, making them suitable for space
applications. Similarly, Nvidia Jetson boards, such as the Jetson Orin Nano and Jetson
TX2i, offer robust performance and have been used in short-duration satellite missions,
demonstrating their adaptability in harsh environments. These solutions provide a balance
between performance, power consumption, and reliability, addressing the drawbacks of
VPUs and FPGAs in space-grade applications.
2.1. ASIC
As already said, to the best of our knowledge, there are no ASIC AI accelerators
specifically developed for space applications that are commercially available. Nonetheless,
in the last few years, some AI accelerators have been tested for radiation tolerance capability
and used in satellite missions. In this subsection, we will introduce these accelerators,
explaining how they can enable onboard inference of NNs for satellite missions.
2.1.1. Google Edge TPU
The Coral Edge TPU, developed by Google, is an Application-Specific Integrated
Circuit (ASIC) specifically engineered to accelerate TensorFlow Lite models while main-
taining exceptionally low power consumption [
45
]. This hardware is optimized for the
execution of quantized 8-bit neural network (NN) models, which are compiled for the
Edge TPU, allowing for highly efficient inference processing. It is essential to acknowledge,
however, that the Edge TPU does not support all operations available in TensorFlow [
46
].
In instances where unsupported operations are identified, the Edge TPU compiler auto-
matically partitions the neural network into two distinct sections: the initial segment is
executed on the Edge TPU, while the remaining operations are transferred to the central
processing unit (CPU). To further streamline the process of neural network inference on the
Edge TPU, Google offers the PyCoral API, which enables users to perform sophisticated
tasks with minimal Python code [
47
]. In addition, Google provides a suite of pre-trained
neural network models specifically tailored for the Edge TPU. These models cover a broad
range of applications, including audio classification, object detection, semantic segmenta-
tion, and pose estimation [
48
]. The Google Coral Edge TPU is available in multiple form

Remote Sens. 2024,16, 3957 5 of 29
factors, suitable for both development and production environments, and has garnered
significant attention for its potential in low Earth orbit (LEO) missions [
43
]. Moreover, it
has undergone performance and radiation testing to evaluate its suitability for onboard
satellite applications [
49
,
50
]. Given the increasing demand for low-power AI accelerators
in the aerospace industry, the Edge TPU stands out as a highly promising and efficient
solution [45]. Figure 1shows the main products featuring the Google Coral TPU.
Figure 1. The main products featuring the Google Coral TPU. Image taken from [45].
In [
51
], the authors present the design and capabilities of a CubeSat-sized co-processor
card, known as the SpaceCube Low-power Edge Artificial Intelligence Resilient Node (SC-
LEARN). This work aims to facilitate the use of advanced AI frameworks in space missions,
addressing challenges associated with traditional spacecraft systems that limit onboard
AI capabilities due to their reliance on radiation-hardened components. The motivation
behind this work resides in the fact that in the last few years, there has been a growing
importance of AI in different domains, including autonomous systems and remote sensing.
This highlights the need for specialized, low-power AI chips that can handle complex tasks
in space, particularly for Earth science applications such as vegetation classification and
disaster monitoring. The SC-LEARN card integrates the Google Coral Edge TPU to provide
high-performance, power-efficient AI applications tailored for space environments. It
complies with NASA’s CubeSat Card Specification (CS2), allowing for seamless integration
into existing SmallSat systems. The SC-LEARN card operates in three distinct modes:
• High-performance parallel processing mode for demanding computational tasks;
• Fault-tolerant mode designed for resilience against operational failures;
• Power-saving mode to conserve energy during less intensive tasks.
Moreover, the authors discuss the training and quantization of TensorFlow models
specifically for the SC-LEARN, utilizing representative open-source datasets to enhance on-
board data analysis capabilities. Finally, some future research plans are outlined, including
the following:
•
Radiation beam testing to assess the performance of the SC-LEARN in space condi-
tions;
•
Flight demonstrations to validate the effectiveness of the SC-LEARN in real mission
scenarios.
Overall, this work represents a significant step toward integrating advanced AI tech-
nologies into space missions, enabling more autonomous operations and efficient data
analysis directly onboard spacecraft.
2.1.2. Nvidia Jetson Orin Nano
The Nvidia Jetson Orin Nano [
52
], a significant advancement over the original Jet-
son Nano board, integrates a powerful combination of a multi-core CPU and an Nvidia
Ampere-based GPU. This advanced architecture offers flexible power consumption settings,
adjustable between 7 and 15 watts, making it suitable for a wide range of applications. The
Orin Nano is available in a convenient development kit, which facilitates rapid prototyping
and accelerates the design and testing of machine learning models and AI applications [
53
].
Running on a Linux-based operating system, the Jetson Orin Nano capitalizes on the robust
computational capabilities of its GPU to efficiently execute diverse neural network (NN)

Remote Sens. 2024,16, 3957 6 of 29
models. This platform supports a broad array of high-level machine learning frameworks,
enabling seamless neural network inference across multiple use cases. A key component of
its performance is Nvidia’s TensorRT, a deep learning inference optimizer that enhances
the speed and efficiency of NN models by optimizing them for the underlying hardware ar-
chitecture [
54
]. Of particular interest, certain devices in the Nvidia Jetson family, including
the Orin Nano, have proven to be well suited for short-duration satellite missions [
55
,
56
].
These applications highlight the Jetson Orin Nano’s durability and adaptability in harsh
and resource-constrained environments, demonstrating its potential for use in aerospace
and other challenging fields.
The SpIRIT satellite aims to utilize onboard AI to enhance data processing capabilities
while addressing constraints such as limited computational resources, cosmic radiation
resilience, extreme temperature variations, and low transmission bandwidths. In [
57
],
the authors introduce Loris, an imaging payload employed onboard the SpIRIT mission,
consisting of the following:
• Six visible light cameras (Sony IMX219);
• Three infrared cameras (FLIR Lepton 3.5);
• A camera control board;
• An NVIDIA Jetson Nano for processing.
Loris allows for advanced onboard computer vision experiments and supports in-
novative image compression techniques, including progressive coding. Such capabilities
are crucial for optimizing data handling in space. The authors adopted several design
considerations to enhance the robustness of the system:
•
Multiplexing Strategy: this approach mitigates the risk of individual sensor failures
by integrating multiple sensors into the system;
•
Thermal Management: the payload is designed to operate effectively within a sinu-
soidal thermal profile experienced in orbit, ensuring that components like the Jetson
Nano remain operational despite temperature fluctuations;
•
Cost-Effectiveness: the use of commercial off-the-shelf (COTS) components aligns
with budget constraints typical of nanosatellite missions, although it introduces higher
risks regarding component reliability.
Loris enables on-orbit fine-tuning of AI models and enhances remote sensing capabili-
ties. The imaging system is expected to facilitate a wide range of applications, including
Earth observation and environmental monitoring, thereby broadening the potential uses
of nanosatellites that generate vast amounts of data in scientific research and industry.
Figure 2shows the Loris architecture and electronic sub-module.
Figure 2. On the left, Loris architecture. On the right, the camera and multiplexing electronic
sub-module. Images taken from [57].

Remote Sens. 2024,16, 3957 7 of 29
2.1.3. Other NVIDIA Jetson Boards
•
NVIDIA Jetson TX2i. The Jetson TX2i is another notable component used in space
applications:
–
Performance: it provides up to 1.3 TFLOPS of AI performance, making it suitable
for demanding imaging and data processing tasks.
–
Applications: Aitech’s S-A1760 Venus system incorporates the TX2i, specifically
designed for small satellite constellations operating in low Earth orbit (LEO) and
near-Earth orbit (NEO). This system is characterized by its compact size and
rugged design, making it ideal for harsh space environments.
•
NVIDIA Jetson AGX Xavier Industrial. The Jetson AGX Xavier Industrial module
offers advanced capabilities for more complex satellite missions:
–
High Performance: it delivers server-class performance with enhanced AI capa-
bilities, making it suitable for sophisticated tasks like sensor fusion and real-time
image processing.
–
Radiation Resistance: studies have indicated that the AGX Xavier can withstand
radiation effects when properly enclosed, making it a viable option for satellites
operating in challenging environments.
•
Planet Labs’ Pelican-2 Satellite. The upcoming Pelican-2 satellite, developed by Planet
Labs, will utilize the NVIDIA Jetson edge AI platform:
–
Intelligent Imaging: this integration aims to enhance imaging capabilities and
provide faster insights through real-time data processing onboard.
–
AI Applications: the collaboration with NVIDIA will enable the satellite to lever-
age AI for improved data analytics, supporting rapid decision-making processes
in various applications.
2.1.4. Intel Movidius Myriad X VPU
The Intel Movidius Myriad X VPU is a specialized hardware accelerator known for its
advanced capabilities in neural network (NN) inference, powered by a cluster of processors
referred to as SHAVE cores. One of its key features is the flexibility it offers developers,
allowing the number of SHAVE cores utilized for NN inference to be customized. With a
total of 16 SHAVE processors, the Myriad X provides users with the ability to fine-tune the
performance-to-power-consumption ratio, enabling them to optimize the device’s operation
based on their specific use cases and energy requirements. The Myriad X particularly
excels in accelerating neural networks that incorporate convolutional layers, such as Fully
Convolutional Networks (FCNs) and Convolutional Neural Networks (CNNs). Its ability to
efficiently handle these complex architectures makes it a preferred solution for applications
that demand high-speed, low-power processing of computationally intensive models. In
addition to its use in terrestrial applications, the Myriad VPU family, including both the
Myriad X and its predecessor, the Myriad 2, has established a foothold in the aerospace
sector. Like the Google Edge TPU and Nvidia Jetson platforms, the Myriad VPU has been
recognized as a dependable accelerator for NN inference in space. These devices have
played critical roles in various space missions, having been deployed on satellites and
aboard the International Space Station (ISS) [
42
,
58
,
59
]. Their combination of adaptability,
resilience, and performance under challenging conditions makes them particularly well
suited for use in low Earth orbit missions [
41
,
43
,
60
]. These characteristics underscore
Myriad X’s versatility as a robust solution for AI-driven tasks in both space exploration
and other resource-constrained environments.
2.2. FPGA-Based Designs
FPGAs are the standard devices where hardware designs are prototyped. In some
cases, they can also be used as deployment devices; satellites are one of these cases. FPGAs
play a crucial role in satellite missions due to their versatility, re-programmability, and
ability to handle complex data processing tasks. Space environments expose electronics

Remote Sens. 2024,16, 3957 8 of 29
to radiation, which can cause Single-Event Upsets (SEUs) that disrupt normal operation.
Certain FPGAs are designed with radiation tolerance or hardness, minimizing the risks
associated with space radiation. This makes them suitable for critical applications where
reliability is paramount. Examples of radiation-hardened FPGAs are as follows:
•
Xilinx Virtex-5QV [
61
]: it is designed specifically for space applications and is known
for its high performance and re-programmability. It features enhanced radiation
tolerance, making it suitable for use in satellites and other space systems where
reliability is critical. This FPGA has been used in various missions, including those
conducted by NASA and the European Space Agency (ESA).
•
Actel/Microsemi ProASIC3 [
62
]: these FPGAs are anti-fuse-based devices that provide
a high degree of immunity against radiation-induced faults [
63
]. These FPGAs are
one-time programmable and are often used in applications where re-programmability
is less critical but where robustness against radiation is essential.
•
Microsemi RTG4 [
64
]: it is engineered to withstand radiation-induced Single-Event
Upsets (SEUs) and Total Ionizing Dose (TID) effects [
65
,
66
], making it suitable for use
in high-radiation environments such as space missions.
In this subsection, we will see the most recent and relevant designs aimed to accelerate
inference of NNs onboard satellites.
In [
67
], the authors address the problem of accelerating Deep reinforcement learning
(DRL) models onboard satellites. The application addressed concerns about the onboard
real-time routing for dynamic low Earth orbit (LEO) satellite networks. The authors propose
to reduce the inference time of DRL models by parallelizing and accelerating part of the
convolutional layer’s operation using an onboard FPGA. They propose a co-design method
to improve the onboard inference time of the Dueling-DQN-based routing algorithm and
they tested the proposed solution using an Onboard Computer (OBC) featuring both a CPU
and an FPGA. The parallelization focuses on the ReLU activation function and on the 2D
convolutional layer of the NN exploiting the FPGA, while all the other algorithm’s operation
is executed on the CPU. In particular, they modify the way the sum-pooling operation is
performed inside the convolutional layer to enable parallelization. The implementation is
carried out using Vivado HLS. They tested the proposed solution on a PYNQ-Z2 board,
achieving a 3.1× inference time speedup compared to the CPU-only deployment.
In [
68
], the authors propose an FPGA-based hardware accelerator to perform inference
of NNs onboard satellites. The authors also provide a comparison between their hardware
accelerator and the Intel Myriad-2 VPU. To perform this comparison, they took into consid-
eration the CloudScout case study showing that the proposed solution can achieve a lower
inference time and better customization capability at the expense of a higher time to market
and power consumption. The authors applied a custom quantization method to reduce the
Convolutional Neural Network (CNN)’s size while maintaining a comparable accuracy.
One of the main trade-offs highlighted by the authors is the one between performance and
FPGA resource consumption. The proposed solution was deployed and tested on a Zynq
Ultrascale+ ZCU106 Development Board. The authors noted that despite the CloudScout’s
CNN having been quantized, the CNN’s size exceeded the on-chip memory capability of
most commercially available FPGAs, requiring the integration of the off-chip DDR memory
featured by the board into the accelerator’s design. The proposed solution achieved a 2.4×
lower inference time and 1.8× higher power consumption compared to the Intel Myriad 2
VPU. At the same time, the energy per inference was reduced by 24%. Finally, the proposed
solution was deployed on the rad-hard Xilinx Kintex Ultrascale XQRKU060 to prove the
proposed design could fit space-grade devices to enable longer duration and higher-orbit
missions compared to the Intel Myriad 2 VPU.
In [
69
], the authors present an approach for onboard cloud coverage classification
using a quantized CNN implemented on an FPGA. The study focuses on optimizing
the performance of cloud detection in satellite imagery, addressing the challenges posed
by quantization and resource utilization on FPGA platforms. They provided a specific
CNN design, CloudSatNet-1, to maintain a high accuracy despite the quantization process.

Remote Sens. 2024,16, 3957 9 of 29
To achieve this, they tried different bit widths for the CNN compression and evaluated
accuracy, False Positive Rate (FPR), and other parameters. The Zynq-7020 board was used
to test the proposed solution. The results demonstrate that the proposed CloudSatNet-1
achieves high accuracy in cloud coverage classification while being efficient enough for
real-time processing on satellites. In particular, the proposed solution achieved a higher
accuracy compared to the CloudScout CNN at the expense of a higher FPR. It represents a
promising solution for on-board cloud detection in satellite systems, balancing accuracy
with computational efficiency. The FPGA implementation shows significant advantages
in terms of speed and resource efficiency compared to traditional CPU-based approaches.
Figure 3shows the architecture of CloudSaNet-1.
Figure 3. The architecture of CloudSaNet-1. Image taken from [69].
In [
70
], the authors present the ICU4SAT, a versatile instrument control unit. This de-
sign aims to enhance the capabilities of satellite instruments by providing a re-configurable
control unit that can adapt to various mission requirements. ICU4SAT is designed to
improve the autonomous operation of satellites, in particular facilitating advanced imaging
and data processing. The ICU4SAT key features are as follows:
•
Re-configurability: it can be adapted for different instruments and tasks, allowing it to
support a wide range of satellite missions;
•
Open Source: utilizing open-source components promotes collaboration and innova-
tion, enabling users to modify and improve the system as needed;
•
Integration with AI: the unit is designed to incorporate AI, enhancing its ability to
process data and make decisions autonomously.
The system is built using open-source components, which facilitates accessibility and
customization for different applications.
In [
71
], the authors explore the integration of FPGAs in enhancing the onboard pro-
cessing capabilities for spacecraft pose estimation using CNNs. The authors present three
approaches to landmark localization:
• Direct Regression on Full Image: a straightforward method but less accurate;
• Detection-Based Methods: utilizing heatmaps to improve accuracy;
•
Combination of Detection and Cropping: involves detecting the spacecraft first and
then applying landmark localization algorithms, which significantly enhances accu-
racy.
The paper details the implementation of CNN models on FPGAs, specifically using
the Xilinx DPU IP Core with Xilinx UltraScale+ MPSoC hardware. The study highlights the
network quantization techniques to optimize model performance on FPGAs. The authors
show that the inference on the FPGA using 8-bit quantization has a negligible RMS drop
(around 0.55) when compared to the 32-bit float inference on a PC. Finally, the authors
implemented both the YOLOv3 and ResNet34-U-Net CNNs, finding that encoder–decoder
models achieve better performance in landmark localization, with an inference time in the
order of tens of milliseconds. Figure 4shows the HW and SW implementation flow on the
MPSoC (Xilinx, San Jose, CA, USA).

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Figure 4. HW and SW inference flow on the MPSoC. Image taken from [71].
2.3. Neuromorphic Hardware and Memristors
Neuromorphic hardware represents a significant class of accelerators that offer a
balance between high computational efficiency and low energy consumption. These sys-
tems are designed to mimic the neural architecture of the human brain, enabling efficient
processing of NN models, particularly Spiking Neural Networks (SNNs).
Neuromorphic hardware, such as Intel’s Loihi 2 [
72
,
73
], is specifically designed for
high-efficiency, event-driven computations. Loihi 2 supports the development of SNNs,
which can be more energy-efficient than traditional Artificial Neural Networks (ANNs).
SNNs process information using discrete spikes, similar to biological neurons, which allows
for lower power consumption and faster processing speeds in certain applications.
Memristors are another type of analog-signal-based hardware that demonstrate high
efficiency and low power consumption. Memristors can store and process information
simultaneously, making them highly suitable for implementing NNs. They offer non-
volatile memory, which means they retain information without power, further reducing
energy consumption.
The combination of neuromorphic hardware and memristors presents a promising
approach for space applications, where power efficiency and computational capability
are critical. These technologies can be used to develop advanced AI systems that operate
efficiently in the harsh conditions of space.
Table 1provides a comparison of neuromorphic hardware and memristors with
traditional hardware accelerators.
Table 1. Comparison of neuromorphic hardware and memristors with traditional hardware
accelerators.
Hardware Computational Efficiency (TOPS/W) Power Consumption (W) Suitability for Space
Intel Loihi 2 10 <1 High
Memristors Varies <1 High
Google Edge TPU 4 2 Tested
Nvidia Jetson Orin Nano 6 7–15 Tested
Intel Movidius Myriad X 1.5 1–2 Tested
As shown in Table 1, neuromorphic hardware like Intel’s Loihi 2 and memristors offer
superior computational efficiency and lower power consumption compared to traditional
hardware accelerators. Their suitability for space applications is high due to their energy
efficiency and robustness in harsh environments.
2.4. Commercial Solutions
Nowadays, inside the product portfolio of some companies, we can find solutions
specifically developed for AI acceleration onboard satellites. These solutions provide
radiation-hardened devices to deploy spaceborne applications. In this subsection, we
introduce some of the most interesting solutions provided by companies.
Ingeniars [
74
] provides the GPU@SAT [
75
,
76
] system, which is designed to enable
AI and machine learning (ML) applications directly onboard satellites. This solution fea-
tures a general-purpose GPU-like IP core integrated into a radiation-hardened FPGA to

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ensure the system can operate reliably in the harsh environment of space. By integrating
GPU-like capabilities into satellite systems, GPU@SAT significantly enhances the compu-
tational efficiency and performance of AI applications, making them more feasible for
space missions. This solution features an IP core that can be configured using OpenCL
properties, allowing for the execution of kernels (small programs) on the hardware. This
includes setting up necessary parameters, loading executable binary code, and allocating
memory buffers. The system can schedule and execute multiple kernels, managing their
sequence and dependencies efficiently. GPU@SAT is tailored for AI and machine learning
(ML) applications, including computer vision tasks such as image and video processing.
It leverages the parallel processing capabilities of Graphics Processing Units (GPUs) to
accelerate computational tasks, which is crucial for real-time data analysis onboard satel-
lites. Moreover, this system supports the development of edge computing applications
in the context of Space-IoT, enabling smart processing directly onboard satellites. This
enhances the capability for real-time or near-real-time data processing without constant
communication with ground-based systems.
Mitsubishi Heavy Industries (MHI), Ltd. has developed an onboard AI-based object
detector called Artificial Intelligence Retraining In Space (AIRIS) [
77
]. AIRIS aims to
perform object detection from satellite-acquired images and its operations will be controlled
by the space-grade Microprocessor Unit (MPU) SOISOC4, a product of development by
the Japan Aerospace Exploration Agency (JAXA) and MHI. This MPU provides a high
radiation resistance, allowing it to operate in the harsh radiation environment of deep
space. AIRIS consists of an AI-equipped data processor and an Earth observation camera
developed by the Tokyo University of Science. It will take images using the camera and
it will use its AI to transmit to the ground only the subsection of images containing the
target objects. Moreover, it will allow its onboard AI model to update by receiving a new
version of it from the ground station. AIRIS will execute a demonstration as part of the
“Innovative Satellite Technology Demonstration-4” mission aboard the small demonstration
satellite “RAISE-4”, which is scheduled for launch in 2025. In particular, AIRIS will perform
vessel detection leveraging onboard AI inference and it will transmit to the ground only
the portion of images containing the identified objects.
Blue Marble Communications (BMC) [
78
] provides a radiation-hardened Space Edge
Processor (SEP) that integrates CPU, GPU, and FPGA capability into a high-performance,
secure edge processor for spaceborne applications. This device features the following [
79
]:
• Industry-leading radiation performance and power efficiency;
• The SEP features an integral Ethernet/MPLS switch;
• AMD Ryzen V2748 CPU+GPU;
• AMD Versal coprocessor FPGA.
Both BruhnBruhn Innovation AB (BBI) and AIKO S.r.l. provide software to run AI-
based algorithms on the SEP device [80,81].
Intel has developed the Loihi 2 neuromorphic chip, which is designed for high-
efficiency, event-driven computations. Loihi 2 supports the development of SNNs, which
can be more energy-efficient than traditional ANNs. This chip is particularly suitable for
space applications due to its low power consumption and high computational efficiency.
MemComputing, Inc. offers memristor-based processing units that provide high
efficiency and low power consumption for NN implementations. These units can store and
process information simultaneously, making them highly suitable for space applications
where power efficiency is critical. Memristor technology offers non-volatile memory, which
means it retains information without power, further reducing energy consumption.
Both neuromorphic hardware and memristor-based solutions present promising ap-
proaches for enhancing the computational capabilities of satellites while maintaining low
power consumption and high efficiency. These technologies are particularly valuable
for developing advanced AI systems that can operate efficiently in the harsh conditions
of space.

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3. Software Tools, Methods, and Solutions for AI Integration
3.1. Main Challenges
The integration of AI models on embedded platforms aboard satellites presents a series
of complex challenges, both technological and methodological, as it does for low-power
embedded devices [
82
]. These challenges extend beyond traditional hardware and resource
limitations [
28
,
83
], encompassing issues related to reliability, security, data management,
and operational resilience in extreme environments. In this subsection, we provide an
examination of some of the main challenges and further issues that may arise.
Integrating AI models into satellite systems introduces significant energy constraints,
particularly in low Earth orbit (LEO) satellite missions, which are reliant on limited energy
resources, primarily solar panels. Power must be distributed among various systems,
including communication, thermal control, instrumentation, and computation. AI models
that demand intensive processing can quickly increase energy consumption, necessitating
dynamic and adaptive power management.
The harsh conditions of space pose another significant challenge. Extreme tempera-
ture variations and high-energy radiation can cause hardware malfunctions, impacting the
reliability of embedded components. Radiation can induce soft errors, leading to temporary
data corruption, or hard errors, resulting in permanent hardware failure. AI models and
hardware platforms must be resilient to such failures, incorporating fault-tolerant comput-
ing techniques, such as hardware redundancy, fault recovery through checkpoints, and
algorithms capable of recognizing and avoiding radiation-induced errors. Error detection
and correction mechanisms, such as error-correcting codes (ECCs), should be employed to
prevent performance degradation and data corruption.
In addition, embedded platforms on satellites are constrained in terms of compu-
tational capacity and memory compared to terrestrial systems. Memory resources are
often scarce and must be carefully managed to execute complex AI models without
overloading the system. Techniques like pruning, sparsity, and model distillation are
essential for reducing the size and complexity of AI models, making them executable in
low-memory environments.
Another critical issue is the need for software upgradability and maintenance. Once
launched, satellites have limited capacity for physical upgrades, making remote software
updates crucial. Updating AI models and algorithms onboard is complicated by communi-
cation constraints, latency, and the high costs of data transmissions from space. A reliable
and secure remote update system is necessary to ensure the robustness of updates against
potential interruptions or data corruption during transmission. Additionally, techniques
for edge learning, which enable satellites to adapt AI models to environmental or data
changes without requiring full updates from the ground, should be considered.
Data management and communication bandwidth also present major challenges.
Satellites may generate vast amounts of data, but the available bandwidth for transmitting
data to Earth is usually limited. AI models can reduce the transmission load by processing
data locally, but this introduces challenges in selecting and managing relevant information
for transmission. Intelligent onboard data processing and filtering must be implemented
to transmit only significant information or critical alerts, such as anomalies or key data
points in Earth observation missions. Moreover, advanced data compression algorithms
should be utilized to preserve the quality of critical information without compromising
subsequent analyses.
Cybersecurity is another growing concern in space. Vulnerable satellites may compro-
mise critical missions if subjected to intrusions or tampering. The integration of AI onboard
requires that platforms be protected against unauthorized access and that models and data
be encrypted to prevent manipulation [
84
,
85
]. Encryption and authentication mechanisms
are essential to protect AI models and inferences, ensuring that communications between
satellites and ground stations are authenticated and secure. AI could also be used to
monitor system security in real time, detecting anomalous behaviours that could signal
cyberattacks or system malfunctions.

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Moreover, validating and verifying AI models in the space environment is a significant
methodological challenge. Models must undergo extensive testing before launch and be
monitored throughout the mission to ensure that they operate as expected, despite vary-
ing environmental conditions and limited resources. Testing in simulated environments,
replicating space conditions such as radiation and temperature fluctuations, is crucial.
Additionally, fallback models or simpler versions must be available in case primary models
fail or behave unexpectedly due to unforeseen conditions.
3.2. SW Approaches for AI Integration
The integration of AI models into embedded platforms onboard satellites involves
addressing a range of complex technological and methodological challenges. Among
these, quantization [
86
] plays a key role by reducing the precision of numerical data, such
as converting from 32-bit floating point to 8-bit integer, which significantly decreases
memory usage and enhances inference speed. However, quantization introduces its own
set of challenges. It is essential to balance computational efficiency with the accuracy of
the model, as this process can result in rounding errors and a loss of precision. Testing
and optimizing models becomes crucial to ensure that the quality of inference remains
acceptable. Additionally, hardware compatibility poses a challenge since not all embedded
devices support every type of quantization. Some devices may natively support 8-bit
quantization, while others might require additional optimizations.
In this context, the use of lightweight frameworks and optimized runtimes, such as
TensorFlow Lite, ONNX Runtime, and PyCoral API, is important for efficiently executing
AI models on hardware with limited resources. However, ensuring compatibility between
the selected framework and the satellite’s hardware is critical, as not all frameworks support
the required hardware architectures or model operations. Furthermore, the performance of
these frameworks can vary depending on the device’s specifications and the complexity of
the model, necessitating thorough testing on actual hardware to achieve optimal results.
Another key approach is model compression [
87
,
88
], which includes techniques such
as pruning, sparsity, and model distillation. These methods help reduce model complexity
while maintaining their effectiveness. Nonetheless, pruning can negatively impact per-
formance if not carried out correctly, so it is essential to test and validate the model after
applying this technique to ensure satisfactory performance. Distillation, on the other hand,
requires a training and fine-tuning process, which can be computationally intensive. In an
onboard environment, it is vital to strike a balance between the effectiveness of distillation
and the computational demands it places on the system.
Additionally, improving efficiency through pipelining and parallelization, where tasks
are divided and executed simultaneously, introduces further complexity. Managing concur-
rency and data synchronization becomes a critical issue. It is essential to design the system
carefully to prevent conflicts and ensure that data are processed accurately. Balancing the
workload across resources is also crucial to avoid overloading some processing units while
others remain idle, which demands thorough analysis and optimization.
In this subsection, we introduce some of the most recent works aimed at addressing
the problems related to the integration of AI-based algorithms onboard satellites.
In [
89
], the authors show how to reduce the size of a CNN to reduce the upload
time needed to transmit the CNN’s weights from the ground to the satellite. In particular,
starting from the CNN introduced in [
90
] (Baseline), they reduced the size of this CNN,
creating two smaller versions (Reduced and Logistic) by reducing both the number of layers
and channels per layer. This allowed a reduction of around 98.9% of the number of CNN
parameters. Moreover, to reduce as much as possible the file size containing the CNN’s
weights, the authors converted the weights into 16- and 8-bit values. The conversion to 8
bit was carried as shown in Equation (1).

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f{x}=





127 , if x ·100 >127
−127 , if x ·100 <−127
⌊x·100⌉, otherwise
(1)
The authors considered the CNN inference performed as FP32, so no dedicated on-
board hardware is needed during inference time. To evaluate the performance of the
reduced CNNs, they considered the task of classifying plasma regions in Earth’s magneto-
sphere. Concerning the accuracy, they found that the Baseline and Reduced CNNs showed
quite similar accuracy, while the Logistic CNN showed a slightly higher accuracy. Moving
to the file size, it is clear that reducing the bit widths of the weights from 32 to 8 greatly
reduces the amount of data to be transmitted. Considering both the smaller version of
the proposed CNN (the Logistic one) and the reduced bit width, the authors estimate a
reduction time of 240×, proving the value of developing smaller CNNs for dedicated tasks.
In [
91
], the authors propose a novel approach for detecting changes in satellite imagery
using Auto-Associative Neural Network (AANN). Their goal is to provide efficient change
detection methods designed to operate onboard satellites, allowing for real-time processing
and analysis. Solutions like this are useful to efficiently handle large amounts of data that
are generated by the satellite during sensing applications that produce high-resolution
images; storing and transmitting to the ground only the images that are valuable to the de-
sired application could be a promising solution in these cases. The authors utilize Sentinel-2
imagery, focusing on urban changes while excluding natural variations. The proposed
AANN compresses input images into lower-dimensional representations, which are then
analyzed to determine changes by calculating the Euclidean distance between feature
vectors derived from different time points. The proposed AANN provides a higher True
Positive Rate (TPR), F1 score, and recall compared to Discrete Wavelet Transform (DWT).
In [
92
], the authors introduce a benchmarking pipeline for evaluating the performance
of deep learning algorithms on edge devices for space applications. The proposed approach
is model-agnostic and can be used to assess various deep learning algorithms, with a focus
on computer vision tasks for space missions. The evaluation involves three use cases:
detection, classification, and hyperspectral image segmentation. The key contributions of
the proposed work are as follows:
•
A benchmarking pipeline that utilizes standard Xilinx tools and a deep learning
deployment chain to run deep learning techniques on edge devices;
•
Performance evaluation of three state-of-the-art deep learning algorithms for computer
vision tasks on the Leopard DPU Evalboard, which will be deployed onboard the
Intuition-1 satellite;
•
The analysis focuses on the latency, throughput, and performance of the models.
Quantification of deep learning algorithm performance at every step of the deployment
chain allows practitioners to understand the impact of crucial deployment steps like
quantization or compilation on the model’s operational abilities.
The tests were run on the Leopard DPU Evalboard.
In [
93
], the authors propose an approach for cloud detection using CNNs specifically
designed for remote sensing applications. This study emphasizes the importance of efficient
cloud screening in satellite imagery to enhance the quality of data for various applications,
including climate monitoring and environmental assessments. They propose an encoder–
decoder CNN structure and they built training and test samples using the SPARCS (Spatial
Procedures for Automated Removal of Cloud and Shadow) [
94
] dataset. The main metrics
taken into consideration by the authors are as follows: F1 score, mean Intersection over
Union (mIoU), and overall accuracy. Different parameters were evaluated to understand
their impact on the CNN performance:
•
Input Bands: the network was tested with varying numbers of spectral bands, reveal-
ing that using four specific bands (red, green, blue, and infrared) yielded the best
results;

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•
Input Size: different input sizes were assessed, with a size of 128
×
128 pixels providing
optimal accuracy while reducing memory usage;
•
Precision: experiments with half-precision computations demonstrated significant
memory savings but at the cost of performance degradation in segmentation tasks;
•
Convolutional Filters: adjusting the number of filters affected both memory consump-
tion and accuracy, suggesting a balance is necessary to maintain valuable performance
while optimizing resources;
•
Encoder Depth: utilizing deeper residual networks improved performance metrics
significantly but increased memory usage and inference time.
The authors showed that the proposed CNN outperformed existing state-of-the-art
methods like Deeplab V3+ [
95
] in terms of accuracy and memory efficiency. The findings
indicate that careful tuning of network parameters can lead to substantial improvements in
cloud detection capabilities without excessive resource demands. Thus, the proposed CNN-
based approach is effective for onboard cloud screening of satellite imagery. It highlights
the potential for real-time processing in spaceborne systems, paving the way for enhanced
data quality in remote sensing applications. This research contributes valuable insights into
optimizing neural network architectures for specific tasks in remote sensing, particularly
concerning resource constraints typical in onboard systems. Figure 5shows the neural
network’s architecture for the cloud screening.
Figure 5. Cloud screening neural network architecture. Image taken from [93].
In [
96
], the authors investigate the use of SNNs on neuromorphic processors to im-
prove energy efficiency in AI-based tasks for satellite communication. Their work aims
to compare the performance and power consumption of various satellite communication
applications using traditional AI accelerators versus neuromorphic hardware. They took
into consideration three use cases: payload resource optimization; onboard interference
detection and classification; and dynamic receive beamforming. The authors compare the
performance of conventional CNNs implemented on Xilinx’s VCK5000 Versal development
card with SNNs running on Intel’s Loihi 2 chip. Loihi 2 [
97
,
98
] is Intel’s second-generation
neuromorphic chip, introduced in 2021. It is designed for high-efficiency, event-driven
computations, making it suitable for applications such as machine learning and real-time
data processing, and it is supported by Lava [
99
], an open-source framework for devel-
oping neuro-inspired applications. The findings suggest that neuromorphic hardware
could significantly enhance the efficiency of onboard AI systems in power-constrained
environments like satellites, providing a promising avenue for future advancements in
satellite communication technology.
In [
100
], the authors present a novel approach for onboard ship detection utilizing a
custom hardware-oriented CNN, referred to as HO-ShipNet. This system is specifically
designed for detecting ships in optical satellite images, which is crucial for maritime
monitoring and safety. The HO-ShipNet architecture (shown in Figure 6) is tailored for
embedded systems, enabling efficient processing and deployment on platforms with limited
computational resources, such as satellites or drones. This model achieved high accuracy in
distinguishing between ship and non-ship scenarios, which is critical for effective maritime

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surveillance. The authors highlight the increasing importance of integrating advanced AI
technologies in maritime applications, showcasing how embedded neural networks can
enhance real-time decision-making processes in complex environments. The main tasks
taken into consideration are as follows:
•
Maritime Security: enhancing the capability to monitor illegal fishing, smuggling, and
other illicit activities at sea;
•
Environmental Monitoring: assisting in tracking oil spills and other environmental
hazards;
•
Traffic Management: improving the management of shipping routes and port opera-
tions.
Notably, the authors emphasize explainability, allowing users to understand the
decision-making process of the neural network. This is particularly important in applica-
tions where trust and transparency are essential. Figure 7shows the architecture, including
both PL and PS developed for the HO-ShipNet network.
Figure 6. HO-ShipNet architecture. Image taken from [100].
Figure 7. PL and PS architecture developed for the HO-ShipNet. Image taken from [100].
4. AI Applications in Satellites Tasks and Missions
AI is emerging as a key technology in the space sector, particularly aboard satellites.
Its applications range from enhancing the operational efficiency of satellites to gathering
and analyzing scientific data, as well as boosting the capabilities of exploratory missions. In
this section, we provide an overview of the main AI applications in the space context that
can benefit from AI-based onboard processing capability and provide works specifically
designed to improve each of these applications.
4.1. Earth Observation and Image Analysis
One of the primary applications of AI aboard satellites is in Earth observation, where
AI is used to analyze vast numbers of images and amounts of Geospatial data. Satellites

Remote Sens. 2024,16, 3957 17 of 29
equipped with AI can detect environmental changes, such as deforestation, glacier melting,
pollution, and natural disasters like hurricanes or wildfires. AI helps filter and select the
most relevant images for transmission to Earth, reducing the amount of data sent and
speeding up decision-making processes. Other benefits of adopting AI-powered solutions
onboard satellites for Earth observation applications include enabling direct observation
and recognition of natural disasters from orbit, thus enabling faster disaster response
and mitigation. Nonetheless, there are many challenges in integrating AI-based solutions
onboard satellites, especially small satellites. These challenges mainly concern hardware
resource consumption such as power consumption, and the availability of radiation-tolerant
hardware accelerators while maintaining good performance of the AI models.
In [
58
], the authors propose the CloudScout CNN for onboard cloud coverage de-
tection. The aim is to identify hyperspectral images that are covered by the cloud and
thus that do not have to be downloaded to the ground station, saving time and energy
resources. The dataset was built using hyperspectral images from the Sentinel-2 mission.
The proposed CNN has been tailored to run on the Intel Myriad 2 VPU to achieve a valuable
trade-off between low inference time and low-power consumption. CluodScout achieves
92% accuracy, a 1% False Positive (FP) rate, an inference time of 325 ms, and a power
consumption of 1.8 W. The model footprint, around 2.1 MB, makes it feasible to upload a
new version of the CNN during the mission life cycle.
The
Φ
-Sat-1 mission, launched on 3 September 2020, is a pioneering project by the
European Space Agency (ESA) that demonstrates the use of AI for Earth observation [
101
].
This mission is notable for being the first to implement an onboard deep neural network
(DNN) for processing satellite imagery directly in space. Key features of the
Φ
-Sat-1 mission
include the following:
•
AI Integration: The satellite employs a Convolutional Neural Network to perform
cloud detection, filtering out unusable images before they are transmitted back to
Earth. This enhances data efficiency by significantly reducing the volume of images
sent down, which is crucial given that cloud cover can obscure over 30% of satellite
images.
•
Technological Demonstrator: as part of the Federated Satellite Systems (FSSCat) initia-
tive,
Φ
-Sat-1 serves as a technological demonstrator, showcasing how AI can optimize
satellite operations and data collection.
•
Payload: the satellite is equipped with a hyperspectral camera and an AI processor
(Intel Movidius Myriad 2), which enables it to analyze and process data onboard.
•
Future Developments: following the success of
Φ
-Sat-1, plans are already in motion
for the
Φ
-Sat-2 mission, which aims to expand on these capabilities by integrating
more advanced AI applications for various Earth observation tasks.
Overall,
Φ
-Sat-1 marks a significant step forward in utilizing AI technologies in space,
setting the stage for more sophisticated applications in future missions.
The authors of [
102
] discuss advancements in image processing techniques for high-
resolution Earth observation satellites, specifically focusing on the French space agency
(CNES) experience with satellites like Pléiades. The paper focuses on the following:
•
Image Acquisition: high-resolution satellites capture panchromatic images with fine
spatial resolution and multispectral images with coarser sampling due to downlink
constraints.
•
Processing Chain: the paper outlines a next-generation processing chain that includes
onboard compression, correction of compression artifacts, denoising, deconvolution,
and pan-sharpening techniques.
•
Compression Techniques: a fixed-quality compression method is detailed, which
minimizes the impact of compression on image quality while optimizing bitrate based
on scene complexity.
•
Denoising Performance: the study shows that non-local denoising algorithms signifi-
cantly improve image quality, outperforming previous methods by 15% in terms of
root mean squared error.

Remote Sens. 2024,16, 3957 18 of 29
•
Adaptation for CMOS Sensors: the authors also discuss adapting these processing
techniques for low-cost CMOS Bayer colour matrices, demonstrating the versatility of
the proposed image processing chain.
This research contributes to enhancing the quality and efficiency of satellite im-
agery processing, which is crucial for various applications in remote sensing and Earth
observation.
As discussed in [
103
], the integration of AI-based image recognition in small-satellite
Earth observation missions offers a mix of substantial opportunities and notable challenges.
One of the key advantages is the increased efficiency in handling data. AI can process
images directly onboard the satellite, filtering out unusable data—such as images obscured
by clouds—before transmission. This capability significantly reduces the volume of data
sent back to Earth, making better use of bandwidth and lowering transmission costs. Addi-
tionally, satellites equipped with AI gain enhanced autonomy, enabling them to analyze
data in real time and make decisions about which information to transmit. This results in
faster response times, particularly valuable for applications like disaster monitoring and
environmental assessments. Moreover, AI supports more advanced functionalities, includ-
ing cloud detection, object recognition, and anomaly identification directly in space. This
leads to more actionable insights, increasing the utility of the satellite’s imagery for diverse
stakeholders. The use of onboard AI also contributes to cost reduction by minimizing the
data processing requirements on the ground, thus cutting operational expenses. However,
the integration of AI into space missions presents several challenges. The hardware used in
space is subject to strict limitations in terms of power consumption and processing capacity.
AI models must be both efficient and lightweight, which can restrict their complexity and
effectiveness. In addition, the space environment exposes hardware to radiation, which
can affect the reliability of AI systems, requiring more robust designs to ensure uninter-
rupted operation. Developing AI models for space also involves extensive training with
relevant datasets, which is often complicated by the unique conditions of space missions.
These models must be adaptable to handle a variety of operational scenarios. Furthermore,
the integration of AI into existing satellite systems is a technically demanding process,
involving compatibility issues with software, data formats, and communication protocols.
This adds complexity to mission planning and execution. In conclusion, while AI-based
image recognition holds transformative potential for enhancing small-satellite Earth obser-
vation missions, careful consideration of the associated technical challenges is essential for
successful implementation.
In [
104
], the authors propose a distillation method to reduce the size of deep neural
networks (DNNs) for satellite onboard image segmentation, specifically for ship detection
tasks. The goal is to simplify large, high-performance deep neural networks (DNNs) to
fit within the limited computational resources of CubeSat platforms while minimizing
accuracy loss. The motivation of this work is that Cubesat satellites can benefit from
onboard data reduction and data reduction can be effectively performed using modern
deep neural networks (DNNs). At the same time, modern deep neural networks (DNNs)
are often too big and require too many computation capabilities. With this aim, the authors
propose to use a teacher–student approach to reduce the size of state-of-the-art Deep Neural
Networks (DNNs). In particular, they use a distillation process to train a smaller student
DNN to mimic the outputs of the teacher DNN. The student DNN is optimized to have
around 1 million parameters to fit within Cubesat processing payloads. They use a weighted
MSE loss function to train the student DNN. The authors propose a distillation-based
method to significantly reduce the size of DNNs for onboard satellite image segmentation
while maintaining high accuracy, enabling their use within the constraints of CubeSat
platforms. Moreover, they highlight that combining distillation with other compression
methods like pruning and quantization can achieve even higher reduction rates.
The integration of onboard anomaly detection systems in marine environmental
protection leverages artificial intelligence to identify threats to marine ecosystems, such as
oil spills, harmful algal blooms, and sediment discharges. The European Space Agency’s

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Φ
-sat-2 mission features a marine anomaly detection application that assigns an anomaly
score to maritime images [
105
]. Figure 8shows the architecture of this solution. This score
increases as the image content deviates from normal water conditions. When the score
exceeds a certain threshold, alerts are triggered to facilitate rapid responses from authorities.
The main key features of the proposed solution are as follows:
•
Image Prioritization: the system prioritizes downloading images with the highest
anomaly scores, optimizing data transmission and operator efficiency.
•
Alert Mechanism: immediate alerts are sent for significant incidents detected onboard,
enhancing response times.
•
AI Efficiency: the application uses minimal annotated data for training, making it
suitable for satellites with limited computational power and adaptable to various
environments beyond marine settings.
This innovative approach demonstrates the potential of AI in enhancing maritime
monitoring and environmental protection efforts, establishing a framework that can be
utilized across different ecosystems and satellite missions.
Figure 8. Architecture of the anomaly detection pipeline proposed in [
105
]. Image taken from [
105
].
4.2. Anomaly Detection and Predictive Maintenance
AI is widely used for monitoring and predictive maintenance of satellites. By analyzing
operational data, machine learning algorithms can detect anomalies in onboard systems
and predict failures before they occur. This improves satellite management, reduces
unplanned downtime, extends the operational life of equipment, and enhances mission
reliability [106,107].
In [
108
], the authors provide an in-depth exploration of various machine learning
(ML) methods aimed at improving the detection of anomalies in satellite telemetry data.
They highlight different types of anomalies that can occur in telemetry, emphasizing the
importance of efficient detection strategies to maintain satellite health and performance.
The paper explores several machine learning approaches, such as Gaussian Mixture Models
(GMMs), which model the distribution of telemetry data to identify outliers, and the Local
Outlier Factor (LOF), a technique that detects anomalies by assessing the local density
of data points. It also discusses the use of autoencoders—neural networks that learn to
represent data efficiently—making them particularly useful for identifying deviations from
normal behaviour. The research applies these techniques to actual satellite telemetry data,
evaluating their effectiveness in detecting anomalies. However, the dataset used in the
evaluation presents certain challenges, such as limited time coverage and a scarcity of
critical anomalies, requiring careful performance assessment of each algorithm. Despite
these challenges, the paper concludes that machine learning techniques offer significant
potential for improving anomaly detection in satellite telemetry. These methods can reduce
reliance on human operators and increase the overall operational efficiency of satellite
systems. Nonetheless, the study acknowledges that further research and refinement of
these approaches are necessary to fully adapt them for practical use in space missions.
The authors in [
109
] outline a framework designed to monitor and predict the health
of Control Moment Gyroscopes (CMGs), which play a crucial role in the attitude control
systems of satellites. The study introduces a Prognostic Health Management (PHM) frame-
work, tailored specifically for CMGs, to emphasize the need for early detection of potential

Remote Sens. 2024,16, 3957 20 of 29
failures and to estimate the Remaining Useful Life (RUL) of these critical components. This
proactive approach aims to prevent unexpected failures and enhance satellite reliability.
The framework leverages historical telemetry data from CMGs, using them to train models
that can detect failure patterns and predict the future operational lifespan of the gyroscopes.
Various machine learning techniques are explored in this context, including both regression
models and classification algorithms, which analyze the telemetry data to forecast possible
failures. The models are evaluated based on their performance metrics, specifically their
accuracy in predicting failures and estimating the RUL. The ultimate goal is to ensure that
these models can operate reliably within the challenging conditions of space. In terms
of practical applications, the implementation of this PHM framework can significantly
enhance operational efficiency. By enabling proactive maintenance, the framework can
extend the lifespan of CMGs and help reduce the likelihood of unforeseen failures. More-
over, the approach has broader applications beyond CMGs; it can be adapted for other
satellite components and systems, contributing to a comprehensive strategy for satellite
health management. This research underlines the importance of predictive analytics in the
space sector, offering a pathway to improved reliability and reduced operational costs for
satellite systems.
The authors in [
110
] explore a new cloud detection system using Convolutional Neural
Networks (CNNs) on commercial off-the-shelf (COTS) microcontrollers. This system is
designed for small satellites to autonomously analyze and prioritize cloud-free images for
transmission, thereby optimizing data collection and improving the quality of Earth obser-
vation data. By deploying the CNN on COTS hardware, the research demonstrates that
commercial components can effectively perform machine learning tasks in space, making
the technology both accessible and cost-effective. The study also examines how quantiza-
tion affects CNN performance by comparing results from the embedded system with those
from a more powerful PC setup. Despite the constraints, the embedded system achieves
performance comparable to more advanced platforms. Overall, the findings suggest that
integrating AI with commerical off-the-shelf (COTS) components can significantly enhance
satellite operations, paving the way for more efficient real-time data processing in future
space missions.
In [
111
], the authors explore the integration of machine learning (ML) technologies in
satellite systems, specifically focusing on Failure Detection, Isolation, and Recovery (FDIR)
functions. As satellite technology becomes increasingly complex, traditional FDIR methods
are deemed inadequate for handling multiple simultaneous failures and the prognosis
of future issues, leading to potentially catastrophic outcomes. The authors highlight the
potential of machine learning (ML) algorithms for improving error detection and prognosis
through advanced onboard systems that can operate under strict power, mass, and radiation
constraints. Machine learning (ML) algorithms can enhance the autonomy and reliability
of satellite operations by learning from in-flight data to detect and respond to failures
more effectively. This work also provides a comparison of various commercial off-the-shelf
(COTS) edge AI boards, focusing on their power draw, processing capabilities (TOPS), and
suitability for space applications. The boards taken into consideration in this paper are
Nvidia Jetson Xavier NX, Huawei Atlas 200, Google Coral, and Intel Movidius Myriad 2.
The results highlight how it is important to select an appropriate hardware device based on
mission requirements.
4.3. Autonomous Navigation and Control
AI plays a crucial role in implementing autonomous navigation systems in satellites.
These systems allow satellites to make real-time decisions regarding routes and manoeuvres,
reducing their dependence on ground-based control. In space exploration missions, AI
enables probes and satellites to autonomously adjust their trajectories to avoid obstacles or
modify their orbits in response to environmental changes. This capability is particularly
valuable in complex environments, such as missions to asteroids or distant planets [
112
,
113
].

Remote Sens. 2024,16, 3957 21 of 29
In [
114
], the authors present a novel Guidance, Navigation, and Control (GNC) system
designed to enhance the autonomy and efficiency of spacecraft during on-orbit manip-
ulation tasks. This research is driven by the increasing demand for advanced robotic
manipulation in space, particularly for servicing missions involving non-cooperative tar-
gets like malfunctioning satellites. The proposed solution includes two main systems:
•
AI Modules. The proposed GNC system incorporates two state-of-the-art AI compo-
nents:
–
Deep Learning (DL)-based Pose Estimation: this algorithm estimates the pose of
a target from 2D images using a pre-trained neural network, eliminating the need
for prior knowledge about the target’s dynamics;
–
Trajectory Modeling and Control: this technique utilizes probabilistic model-
ing to manage the trajectories of robotic manipulators, allowing the system
to adapt to new situations without complex on-board trajectory optimizations.
This minimizes disturbances to the spacecraft’s attitude caused by manipulator
movements.
•
Centralized Camera Network. The system employs a centralized camera network
as its primary sensor, integrating a 7 Degrees of Freedom (DoF) robotic arm into the
GNC architecture.
The intelligent GNC system was tested through simulations of a conceptual mission
named AISAT, which involves a micro-satellite performing manipulations around a non-
cooperative CubeSat. The simulations were conducted in Matlab/Simulink, utilizing a
physics rendering engine to visualize the operations realistically.
Intelligent GNC architectures could serve as a foundation for developing fully au-
tonomous orbital robotic systems.
In [
115
], the authors present the DeepNav initiative, which is a research project funded
by the Italian Space Agency (ASI). DeepNav aims to enhance autonomous navigation
techniques for small satellites operating in deep space, particularly around asteroids.
This is crucial for missions that require precise maneuvering and data collection from
these celestial bodies. This project is set to last for 18 months, during which various
methodologies will be explored and tested. DeepNav’s key Features are as follows:
•
Autonomous Navigation. DeepNav focuses on creating systems that allow satellites to
navigate without constant human intervention, which is vital for missions that operate
far from Earth;
•
Deep Learning Techniques. The project leverages deep learning algorithms to process
and analyze data collected from asteroid surfaces, enabling better decision-making
and navigation strategies.
The project represents a significant step forward in the field of aerospace engineering,
particularly in the context of small satellite missions.
In [
116
], the authors present the FUTURE mission. This mission aims at developing
an innovative approach to enhancing spacecraft autonomy, specifically focusing on orbit
determination using AI. The FUTURE mission aims to reduce reliance on ground operators
by improving the onboard autonomy of a 6
U
CubeSat. This will be achieved through ad-
vanced optical sensors and AI-based algorithms that process data for positional knowledge.
Its key components are as follows:
•
Optical sensors. The CubeSat will be equipped with optical sensors to gather data
about Earth features such as lakes and coastlines;
•
AI processing. The data collected will be processed onboard to generate positional
inputs for navigation filters, enhancing the accuracy of orbit determination.
The authors introduce the navigation filter architecture, a preliminary design of the
navigation filter focusing on how it will process data from the optical sensors, and then
they assesses the potential accuracy of orbit determination achievable through onboard
processing. Finally, they discuss opportunistic observations of celestial objects, such as the

Remote Sens. 2024,16, 3957 22 of 29
Moon, to validate autonomous navigation methods during specific flight conditions. The
future phases of the mission will include further enhancements to the navigation filter. This
could lead to improved autonomous operations not just in low Earth orbit (LEO) but also
in missions targeting other celestial bodies, thereby expanding the operational capabilities
of CubeSats in deep space exploration.
4.4. Data Management, Data Compression, and Communication Optimization
AI facilitates the intelligent management of data collected onboard satellites by op-
timizing data compression and selecting the most relevant information for transmission.
This is critical since satellite communication resources are limited, and transmitting large
volumes of data can be costly and slow. Using AI, satellites can process data onboard,
identify the most valuable scientific or operational information, and transmit only this
to Earth.
Moreover, AI can be used to optimize communication networks, improving resource
management and load distribution among various satellites. AI algorithms can dynamically
adjust the network to better handle data traffic, minimize interference, and ensure optimal
coverage [117].
In [
118
], the authors addresses the significant challenge of data transmission from
satellites to Earth, particularly in Earth observation applications. The authors highlight
that satellite downlink bandwidth is often a limiting factor for transmitting high-resolution
images, which can hinder timely data delivery and analysis.To address this issue, they
introduce a novel approach utilizing federated learning for onboard image compression.
This method allows satellites to collaboratively learn compression algorithms without
needing to send raw data back to Earth, thus conserving bandwidth. The proposed
framework enables multiple satellites to train a shared model while keeping their data
localized. This decentralized approach not only enhances privacy but also optimizes the
learning process by leveraging diverse datasets from various satellites.
The authors conduct experiments demonstrating that their federated learning strategy
significantly improves compression efficiency compared to traditional methods, thereby
alleviating the downlink bottleneck. Improving image transmission capabilities could
impact various applications in Earth observation, including environmental monitoring and
disaster response.
In [
119
], the authors explore the development of an advanced algorithm for com-
pressing multispectral images directly on satellites. This approach is crucial for efficient
data transmission and storage in Earth observation missions. The primary goal of this
paper is to enhance the compression of multispectral images using AI techniques, which
can significantly reduce the amount of data sent back to Earth. The proposed AI-based
algorithm leverages machine learning (ML) to improve compression rates while main-
taining image quality. This involves training models on existing datasets to optimize the
compression process.
The implementation of the algorithm demonstrates improved performance compared
to traditional methods, achieving higher compression ratios without substantial loss of
critical image information. This technology is particularly beneficial for low Earth orbit
(LEO) satellites, where bandwidth is limited and efficient data handling is essential for
timely analysis and response.
In [
120
], the authors focus on enhancing the efficiency of data processing in small
satellites, particularly in the context of hyperspectral imaging. The authors leverage deep
learning inference techniques to optimize onboard data processing to address the significant
challenge posed by the large volumes of data generated by hyperspectral sensors, which
complicates the downlinking process to ground stations. The study proposes an onboard
processing framework that utilizes deep learning (DL) algorithms to analyze and compress
hyperspectral data before transmission, thus reducing the amount of data that needs to
be downlinked and minimizing bandwidth usage. The findings indicate that employing

Remote Sens. 2024,16, 3957 23 of 29
deep learning (DL) models can improve data processing times and significantly enhance
the accuracy and speed of reflectance retrievals from satellite data.
The increasing volume of SAR data necessitates efficient processing methods to over-
come limitations in downlink capacity and reduce latency in data products. In [
11
], the
authors discuss how the employment of deep learning (DL) and machine learning (ML)
algorithms can help in reducing the bandwidth requirement and enhance the real-time
capabilities, which is crucial for time-sensitive applications like disaster response and
environmental monitoring. The authors also highlight the challenges related to onboard
processing, including both the onboard computing power and the power consumption. To
overcome this challenges it is mandatory to develop efficient algorithms tailored to the
onboard processing capabilities.
In [
121
], the authors explore the transformative role of artificial intelligence (AI)
in enhancing satellite communication systems. It emphasizes how AI technologies can
optimize various aspects of satellite operations, addressing the increasing demand for
connectivity in a rapidly evolving digital landscape. They introduce a traffic demand
prediction method utilizing deep learning (DL)-based algorithms. This approach aims
to effectively manage the dynamic traffic demands faced by satellite networks, ensuring
efficient resource allocation and service delivery under varying conditions. Moreover, the
authors highlighted AI as a crucial tool for improving operational efficiency in satellite
communications. The integration of AI can automate and streamline processes such as data
processing, signal analysis, and anomaly detection, thereby reducing operational costs and
enhancing service reliability.
5. Conclusions and Future Works
The integration of AI models, particularly NNs, into embedded platforms onboard
satellites represents a significant advancement in satellite technology. This review has high-
lighted the various hardware solutions, including ASICs, FPGAs, VPUs, and neuromorphic
hardware, that enable efficient NN inference in space. Each hardware type offers unique
advantages in terms of computational efficiency, power consumption, and suitability for
the harsh conditions of space.
ASICs like the Google Edge TPU and Nvidia Jetson boards have demonstrated high
computational efficiency and low power consumption, making them suitable for short-
duration satellite missions. FPGAs, while offering flexibility and re-programmability, pro-
vide higher radiation tolerance, which is essential for long-duration missions. Neuromor-
phic hardware and memristors present promising approaches for developing advanced AI
systems that operate efficiently in space, offering superior computational efficiency and
lower power consumption.
The commercial solutions discussed, such as Ingeniars’ GPU@SAT, MHI’s AIRIS, and
Blue Marble Communications’ SEP, showcase the industry’s efforts to develop radiation-
hardened devices for AI acceleration onboard satellites. These solutions enhance the
computational capabilities of satellites, enabling real-time data processing and reducing
the reliance on ground-based systems.
Despite these advancements, several challenges remain. The limited computational
and memory resources onboard satellites, the need for fault-tolerant systems, and the harsh
space environment pose significant obstacles to the deployment of complex AI models.
Additionally, the integration of AI models into satellite systems introduces cybersecurity
risks that must be addressed to ensure the integrity and reliability of satellite operations.
Future research should focus on the following areas to further enhance the integration
of AI onboard satellites:
•
Optimization of AI Models: Developing lightweight and energy-efficient AI models
that can operate within the constraints of satellite hardware. Techniques such as
model compression, pruning, and quantization should be explored to reduce the
computational requirements of AI models.

Remote Sens. 2024,16, 3957 24 of 29
•
Radiation Tolerance: Enhancing the radiation tolerance of AI hardware through the
development of radiation-hardened components and fault-tolerant systems. This
includes testing and validating AI hardware in simulated space environments to
ensure reliability.
•
Neuromorphic and Memristor-Based Systems: Further research into neuromorphic
hardware and memristor-based systems for space applications. These technologies
offer promising solutions for developing energy-efficient AI systems that can operate
in the harsh conditions of space.
•
Cybersecurity: Implementing robust cybersecurity measures to protect AI models
and data from unauthorized access and manipulation. This includes encryption,
authentication, and continuous monitoring for potential threats.
•
Real-Time Data Processing: Developing advanced AI algorithms for real-time data
processing onboard satellites. This includes applications such as Earth observation,
anomaly detection, and autonomous navigation, which require low-latency responses.
•
Collaboration and Standardization: Promoting collaboration between industry,
academia, and government agencies to standardize AI hardware and software so-
lutions for space applications. This will facilitate the development of interoperable
systems and accelerate the adoption of AI technologies in the space sector.
In conclusion, the integration of AI onboard satellites holds great promise for enhanc-
ing the capabilities of satellite missions. By addressing the current challenges and focusing
on future research directions, we can unlock the full potential of AI in space, enabling more
efficient, autonomous, and resilient satellite operations.
Author Contributions: All authors contributed equally to this research work. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
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