Using Artificial Intelligence for Space Challenges: A Survey
Abstract
:1. Introduction
2. Background on AI
3. Challenges in Space Applications
3.1. Mission Design and Planning
3.2. Space Exploration
3.3. Earth Observation
4. State of the Art
4.1. Works on Mission Design and Planning
4.2. Works on Space Exploration
4.3. Works on Earth Observation
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ES | Expert System |
SVM | Support Vector Machine |
ANN | Artificial Neural Network |
CNN | Convolutional Neural Network |
RNN | Recurrent Neural Network |
DCNN | Deep Convolutional Neural Network |
GAN | Generative Adversarial Network |
NPL | Natural Language Processing |
LSTM | Long-Short Term Memory |
EO | Earth Observation |
SSA | Space Situational Awareness |
SST | Space Surveillance and Tracking |
References
- Gao, Y.; Chien, S. Review on space robotics: Toward top-level science through space exploration. Sci. Robot. 2017, 2, eaan5074. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fourati, F.; Alouini, M.S. Artificial intelligence for satellite communication: A review. Intell. Converg. Netw. 2021, 2, 213–243. [Google Scholar] [CrossRef]
- Meß, J.G.; Dannemann, F.; Greif, F. Techniques of artificial intelligence for space applications—A survey. In European Workshop on On-Board Data Processing (OBDP2019); European Space Agency: Paris, France, 2019. [Google Scholar]
- Saravanan, R.; Sujatha, P. A state of art techniques on machine learning algorithms: A perspective of supervised learning approaches in data classification. In Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14–15 June 2018; pp. 945–949. [Google Scholar]
- Cervantes, J.; Garcia-Lamont, F.; Rodríguez-Mazahua, L.; Lopez, A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 2020, 408, 189–215. [Google Scholar] [CrossRef]
- Speiser, J.L.; Miller, M.E.; Tooze, J.; Ip, E. A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst. Appl. 2019, 134, 93–101. [Google Scholar] [CrossRef] [PubMed]
- Sheykhmousa, M.; Mahdianpari, M.; Ghanbari, H.; Mohammadimanesh, F.; Ghamisi, P.; Homayouni, S. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 6308–6325. [Google Scholar] [CrossRef]
- Berry, M.W.; Mohamed, A.; Yap, B.W. Supervised and Unsupervised Learning for Data Science; Springer: Cham, Switzerland, 2019. [Google Scholar]
- Sinaga, K.P.; Yang, M.S. Unsupervised K-means clustering algorithm. IEEE Access 2020, 8, 80716–80727. [Google Scholar] [CrossRef]
- Van Engelen, J.E.; Hoos, H.H. A survey on semi-supervised learning. Mach. Learn. 2020, 109, 373–440. [Google Scholar] [CrossRef] [Green Version]
- Botvinick, M.; Ritter, S.; Wang, J.X.; Kurth-Nelson, Z.; Blundell, C.; Hassabis, D. Reinforcement learning, fast and slow. Trends Cogn. Sci. 2019, 23, 408–422. [Google Scholar] [CrossRef] [Green Version]
- Kelleher, J.D. Deep Learning; MIT Press: Cambridge, MA, USA, 2019. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef]
- Mikolov, T.; Karafiát, M.; Burget, L.; Cernockỳ, J.; Khudanpur, S. Recurrent neural network based language model. In Proceedings of the Interspeech, Makuhari, Japan, 26–30 September 2010; Volume 2, pp. 1045–1048. [Google Scholar]
- Chowdhary, K. Natural language processing. In Fundamentals of Artificial Intelligence; Springer: New Delhi, India, 2020; pp. 603–649. [Google Scholar]
- Zadeh, L.A.; Klir, G.J.; Yuan, B. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers; World Scientific: Singapore, 1996; Volume 6. [Google Scholar]
- Liebowitz, J. The Handbook of Applied Expert Systems; CRC Press: Boca Raton, FL, USA, 2019. [Google Scholar]
- Kua, J.; Loke, S.W.; Arora, C.; Fernando, N.; Ranaweera, C. Internet of Things in Space: A Review of Opportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living. Sensors 2021, 21, 8117. [Google Scholar] [CrossRef]
- Dumitru, C.O.; Schwarz, G.; Castel, F.; Lorenzo, J.; Datcu, M. Artificial intelligence data science methodology for Earth Observation. In Advanced Analytics and Artificial Intelligence Applications; InTech Publishing: London, UK, 2019; pp. 1–20. [Google Scholar]
- Bang, H.; Virós Martin, A.; Prat, A.; Selva, D. Daphne: An intelligent assistant for architecting earth observing satellite systems. In Proceedings of the 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, Kissimmee, FL, USA, 8–12 January 2018; p. 1366. [Google Scholar]
- Viros Martin, A.; Selva, D. Explanation Approaches for the Daphne Virtual Assistant. In Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA, 6–10 January 2020; p. 2254. [Google Scholar]
- Joey Roulette. OneWeb, SpaceX Satellites Dodged a Potential Collision in Orbit. 2021. Available online: https://www.theverge.com/2021/4/9/22374262/oneweb-spacex-satellites-dodged-potential-collision-orbit-space-force (accessed on 15 September 2021).
- Weiss, T.R. AIKO: Autonomous Satellite Operations Thanks to Artificial Intelligence. 2019. Available online: https://www.esa.int/Applications/Telecommunications_Integrated_Applications/Technology_Transfer/AIKO_Autonomous_satellite_operations_thanks_to_Artificial_Intelligence (accessed on 15 September 2021).
- Gaudet, B.; Linares, R.; Furfaro, R. Deep reinforcement learning for six degree-of-freedom planetary landing. Adv. Space Res. 2020, 65, 1723–1741. [Google Scholar] [CrossRef]
- Weiss, T.R. The AI Inside NASA’s Latest Mars Rover, Perseverance. 2021. Available online: https://www.datanami.com/2021/02/18/the-ai-inside-nasas-latest-mars-rover-perseverance (accessed on 15 September 2021).
- Airbus. “Hello, I am CIMON*!”. 2018. Available online: https://www.airbus.com/newsroom/press-releases/en/2018/02/hello--i-am-cimon-.html (accessed on 15 September 2021).
- Larry Hardesty. A Method to Image Black Holes. 2016. Available online: https://news.mit.edu/2016/method-image-black-holes-0606 (accessed on 15 September 2021).
- Linares, R.; Furfaro, R.; Reddy, V. Space objects classification via light-curve measurements using deep convolutional neural networks. J. Astronaut. Sci. 2020, 67, 1063–1091. [Google Scholar] [CrossRef]
- Dattilo, A.; Vanderburg, A.; Shallue, C.J.; Mayo, A.W.; Berlind, P.; Bieryla, A.; Calkins, M.L.; Esquerdo, G.A.; Everett, M.E.; Howell, S.B.; et al. Identifying exoplanets with deep learning. ii. two new super-earths uncovered by a neural network in k2 data. Astron. J. 2019, 157, 169. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; Huang, J.; Feng, Y.; Wang, F.; Sang, J. A machine learning-based approach for improved orbit predictions of LEO space debris with sparse tracking data from a single station. IEEE Trans. Aerosp. Electron. Syst. 2020, 56, 4253–4268. [Google Scholar] [CrossRef]
- Linares, R.; Furfaro, R. Space object classification using deep convolutional neural networks. In Proceedings of the 2016 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, 5–8 July 2016; pp. 1140–1146. [Google Scholar]
- Oltrogge, D.L.; Alfano, S. The technical challenges of better space situational awareness and space traffic management. J. Space Saf. Eng. 2019, 6, 72–79. [Google Scholar] [CrossRef]
- Hilton, S.; Cairola, F.; Gardi, A.; Sabatini, R.; Pongsakornsathien, N.; Ezer, N. Uncertainty Quantification for Space Situational Awareness and Traffic Management. Sensors 2019, 19, 4361. [Google Scholar] [CrossRef] [Green Version]
- Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R. Resident space object characterization and behavior understanding via machine learning and ontology-based Bayesian networks. In Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, USA, 20–23 September 2016. [Google Scholar]
- Lonnie Shekhtman. NASA Takes a Cue From Silicon Valley to Hatch Artificial Intelligence Technologies. 2019. Available online: https://www.nasa.gov/feature/goddard/2019/nasa-takes-a-cue-from-silicon-valley-to-hatch-artificial-intelligence-technologies (accessed on 15 September 2021).
- Huang, Y.; Wu, S.; Mu, Z.; Long, X.; Chu, S.; Zhao, G. A Multi-agent Reinforcement Learning Method for Swarm Robots in Space Collaborative Exploration. In Proceedings of the 2020 6th International Conference on Control, Automation and Robotics (ICCAR), Singapore, 20–23 April 2020; pp. 139–144. [Google Scholar]
- Furfaro, R.; Bloise, I.; Orlandelli, M.; Di Lizia, P.; Topputo, F.; Linares, R. Deep learning for autonomous lunar landing. In Proceedings of the 2018 AAS/AIAA Astrodynamics Specialist Conference, Snowbird, UT, USA, 19–23 August 2018; Volume 167, pp. 3285–3306. [Google Scholar]
- Gaudet, B.; Furfaro, R.; Linares, R. Reinforcement learning for angle-only intercept guidance of maneuvering targets. Aerosp. Sci. Technol. 2020, 99, 105746. [Google Scholar] [CrossRef] [Green Version]
- Furfaro, R.; Scorsoglio, A.; Linares, R.; Massari, M. Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronaut. 2020, 171, 156–171. [Google Scholar] [CrossRef] [Green Version]
- Scorsoglio, A.; D’Ambrosio, A.; Ghilardi, L.; Gaudet, B.; Curti, F.; Furfaro, R. Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing. J. Spacecr. Rocket. 2022, 59, 153–165. [Google Scholar] [CrossRef]
- Cinelli, I. The Role of Artificial Intelligence (AI) in Space Healthcare. Aerosp. Med. Hum. Perform. 2020, 91, 537–539. [Google Scholar] [CrossRef]
- Trofin, R.S.; Chiru, C.; Vizitiu, C.; Dinculescu, A.; Vizitiu, R.; Nistorescu, A. Detection of Astronauts’ Speech and Language Disorder Signs during Space Missions using Natural Language Processing Techniques. In Proceedings of the 2019 E-Health and Bioengineering Conference (EHB), Iasi, Romania, 21–23 November 2019; pp. 1–4. [Google Scholar]
- Yan, F.; Shiqi, L.; Kan, Q.; Xue, L.; Li, C.; Jie, T. Language-facilitated human–robot cooperation within a human cognitive modeling infrastructure: A case in space exploration task. In Proceedings of the 2020 IEEE International Conference on Human-Machine Systems (ICHMS), Rome, Italy, 7–9 September 2020; pp. 1–3. [Google Scholar]
- Durbha, S.S.; King, R.L. Semantics-enabled framework for knowledge discovery from Earth observation data archives. IEEE Trans. Geosci. Remote Sens. 2005, 43, 2563–2572. [Google Scholar] [CrossRef]
- Denis, G.; de Boissezon, H.; Hosford, S.; Pasco, X.; Montfort, B.; Ranera, F. The evolution of Earth Observation satellites in Europe and its impact on the performance of emergency response services. Acta Astronaut. 2016, 127, 619–633. [Google Scholar] [CrossRef]
- The European Commission’s Science and Knowledge Service. Earth Observation. 2021. Available online: https://ec.europa.eu/jrc/en/research-topic/earth-observation (accessed on 15 September 2021).
- OECD. Earth Observation for Decision-Making. 2017. Available online: https://www.oecd.org/env/indicators-modelling-outlooks/Earth_Observation_for_Decision_Making.pdf (accessed on 15 September 2021).
- Berquand, A.; McDonald, I.; Riccardi, A.; Moshfeghi, Y. The automatic categorisation of space mission requirements for the Design Engineering Assistant. In Proceedings of the 70th International Astronautical Congress, Washington, DC, USA, 21–25 October 2019. [Google Scholar]
- Murdaca, F.; Berquand, A.; Riccardi, A.; Soares, T.; Gerené, S.; Brauer, N.; Kumar, K. Artificial intelligence for early design of space missions in support of concurrent engineering sessions. In Proceedings of the 8th International Systems & Concurrent Engineering for Space Applications Conference, Glasgow, UK, 26–28 September 2018. [Google Scholar]
- Simpson, B.C.; Selva, D.; Richardson, D. Extracting Science Traceability Graphs from Mission Concept Documentation using Natural Language Processing. In Proceedings of the AIAA SCITECH 2022 Forum, San Diego, CA, USA, 3–7 January 2022; p. 1182. [Google Scholar]
- Berquand, A.; Murdaca, F.; Riccardi, A.; Soares, T.; Generé, S.; Brauer, N.; Kumar, K. Artificial intelligence for the early design phases of space missions. In Proceedings of the 2019 IEEE Aerospace Conference, Big Sky, MT, USA, 2–9 March 2019; pp. 1–20. [Google Scholar]
- Ferreirab, P.M.G.V.; Ambrosioc, P.A.M. A proposal an innovative Framework for the Conception of the Ground Segment of Space Systems. In Proceedings of the 71st International Astronautical Congress (IAC)—The CyberSpace Edition, IAC 2020, Online, 12–14 October 2020. [Google Scholar]
- Ren, X.; Chen, Y. How Can Artificial Intelligence Help With Space Missions—A Case Study: Computational Intelligence-Assisted Design of Space Tether for Payload Orbital Transfer Under Uncertainties. IEEE Access 2019, 7, 161449–161458. [Google Scholar] [CrossRef]
- Akyildiz, I.F.; Kak, A. The internet of space things/cubesats. IEEE Netw. 2019, 33, 212–218. [Google Scholar] [CrossRef]
- Jagannath, A.; Jagannath, J.; Drozd, A. Artificial intelligence-based cognitive cross-layer decision engine for next-generation space mission. In Proceedings of the 2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW), Cleveland, OH, USA, 25–26 June 2019; pp. 1–6. [Google Scholar]
- Yairi, T.; Fukushima, Y.; Liew, C.F.; Sakai, Y.; Yamaguchi, Y. A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites. In Advances in Condition Monitoring and Structural Health Monitoring; Springer: Singapore, 2021; pp. 129–141. [Google Scholar]
- Hassanien, A.E.; Darwish, A.; Abdelghafar, S. Machine learning in telemetry data mining of space mission: Basics, challenging and future directions. Artif. Intell. Rev. 2020, 53, 3201–3230. [Google Scholar] [CrossRef]
- Abdelghafar, S.; Darwish, A.; Hassanien, A.E. Intelligent health monitoring systems for space missions based on data mining techniques. In Machine Learning and Data Mining in Aerospace Technology; Springer: Cham, Switzerland, 2020; pp. 65–78. [Google Scholar]
- Yairi, T.; Takeishi, N.; Oda, T.; Nakajima, Y.; Nishimura, N.; Takata, N. A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Trans. Aerosp. Electron. Syst. 2017, 53, 1384–1401. [Google Scholar] [CrossRef]
- Geng, F.; Li, S.; Huang, X.; Yang, B.; Chang, J.; Lin, B. Fault diagnosis and fault tolerant control of spacecraft attitude control system via deep neural network. Chin. Space Sci. Technol. 2020, 40, 1. [Google Scholar]
- Ibrahim, S.K.; Ahmed, A.; Zeidan, M.A.E.; Ziedan, I.E. Machine learning techniques for satellite fault diagnosis. Ain Shams Eng. J. 2020, 11, 45–56. [Google Scholar] [CrossRef]
- OMeara, C.; Schlag, L.; Wickler, M. Applications of deep learning neural networks to satellite telemetry monitoring. In Proceedings of the 2018 SpaceOps Conference, Marseille, France, 28 May–1 June 2018; p. 2558. [Google Scholar]
- Feruglio, L. Artificial Intelligence for Small Satellites Mission Autonomy; Politecnico di Torino: Torino, Italy, 2017; p. 165. [Google Scholar]
- Amoruso, L.; Abbattista, C.; Antonetti, S.; Drimaco, D.; Feruglio, L.; Fortunato, V.; Iacobellis, M. AI-express In-orbit Smart Services for Small Satellites. In Proceedings of the 2020 International Astronautical Congress (IAC), Online, 12–14 October 2020. [Google Scholar]
- Asrar, M.F.; Saint-Jacques, D.; Williams, D.; Clark, J. Assessing current medical care in space, and updating medical training & machine based learning to adapt to the needs of Deep Space Human Missions. In Proceedings of the 2020 International Astronautical Congress (IAC), Online, 12–14 October 2020. [Google Scholar]
- Alcibiade, A.; Schlacht, I.L.; Finazzi, F.; Di Capua, M.; Ferrario, G.; Musso, G.; Foing, B. Reliability in extreme isolation: A natural language processing tool for stress self-assessment. In International Conference on Applied Human Factors and Ergonomics; Springer: Cham, Switzerland, 2020; pp. 350–357. [Google Scholar]
- Zhang, R.; Wang, Z.; Zhang, Y. Astronaut visual tracking of flying assistant robot in space station based on deep learning and probabilistic model. Int. J. Aerosp. Eng. 2018, 2018, 6357185. [Google Scholar] [CrossRef]
- Rui, Z.; Zhaokui, W.; Yulin, Z. A person-following nanosatellite for in-cabin astronaut assistance: System design and deep-learning-based astronaut visual tracking implementation. Acta Astronaut. 2019, 162, 121–134. [Google Scholar] [CrossRef]
- Zhang, R.; Zhang, Y.; Zhang, X. Tracking In-Cabin Astronauts Using Deep Learning and Head Motion Clues. IEEE Access 2020, 9, 2680–2693. [Google Scholar] [CrossRef]
- Kumar, S.; Tomar, R. The role of artificial intelligence in space exploration. In Proceedings of the 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 15–17 February 2018; pp. 499–503. [Google Scholar]
- Acquatella, P. Development of automation & robotics in space exploration. In Proceedings of the AIAA SPACE 2009 Conference & Exposition, Pasadena, CA, USA, 14–17 September 2009; pp. 1–7. [Google Scholar]
- Vasile, M.; Rodríguez-Fernández, V.; Serra, R.; Camacho, D.; Riccardi, A. Artificial intelligence in support to space traffic management. In Proceedings of the 68th International Astronautical Congress: Unlocking Imagination, Fostering Innovation and Strengthening Security, IAC 2017, Adelaide, Australia, 25–29 September 2007; pp. 3822–3831. [Google Scholar]
- Izzo, D.; Märtens, M.; Pan, B. A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodynamics 2019, 3, 287–299. [Google Scholar] [CrossRef]
- Huang, X.; Li, S.; Yang, B.; Sun, P.; Liu, X.; Liu, X. Spacecraft guidance and control based on artificial intelligence: Review. Acta Aeronaut. Astronaut. Sin 2021, 42, 524201. [Google Scholar]
- Colby, M.; Yliniemi, L.; Tumer, K. Autonomous multiagent space exploration with high-level human feedback. J. Aerosp. Inf. Syst. 2016, 13, 301–315. [Google Scholar] [CrossRef] [Green Version]
- Semenov, A. Elastic computing self-organizing for artificial intelligence space exploration. J. Phys. Conf. Ser. 2021, 1925, 012071. [Google Scholar] [CrossRef]
- Carpentiero, M.; Sabatini, M.; Palmerini, G.B. Swarm of autonomous rovers for cooperative planetary exploration. In Proceedings of the 2017 International Astronautical Congress (IAC), Adelaide, Australia, 25–29 September 2017. [Google Scholar]
- Choi, D.; Kim, D. Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains. Electronics 2021, 10, 1499. [Google Scholar] [CrossRef]
- Fluke, C.J.; Jacobs, C. Surveying the reach and maturity of machine learning and artificial intelligence in astronomy. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, e1349. [Google Scholar] [CrossRef] [Green Version]
- Bird, J.; Colburn, K.; Petzold, L.; Lubin, P. Model Optimization for Deep Space Exploration via Simulators and Deep Learning. arXiv 2020, arXiv:2012.14092. [Google Scholar]
- Bird, J.; Petzold, L.; Lubin, P.; Deacon, J. Advances in deep space exploration via simulators & deep learning. New Astron. 2021, 84, 101517. [Google Scholar]
- Wang, W.; Lin, L.; Fan, Z.; Liu, J. Semi-Supervised Learning for Mars Imagery Classification. In Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19–22 September 2021; pp. 499–503. [Google Scholar]
- Yang, B.; Liu, P.; Feng, J.; Li, S. Two-stage pursuit strategy for incomplete-information impulsive space pursuit-evasion mission using reinforcement learning. Aerospace 2021, 8, 299. [Google Scholar] [CrossRef]
- Yang, B.; Li, S.; Feng, J.; Vasile, M. Fast solver for J2-perturbed Lambert problem using deep neural network. J. Guid. Control Dyn. 2021, 45, 1–10. [Google Scholar] [CrossRef]
- Yang, H.; Yan, J.; Li, S. Fast computation of the Jovian-moon three-body flyby map based on artificial neural networks. Acta Astronaut. 2022, 193, 710–720. [Google Scholar] [CrossRef]
- Yang, B.; Feng, J.; Huang, X.; Li, S. Hybrid method for accurate multi-gravity-assist trajectory design using pseudostate theory and deep neural networks. Sci. China Technol. Sci. 2022, 65, 595–610. [Google Scholar] [CrossRef]
- Yan, J.; Yang, H.; Li, S. ANN-based method for fast optimization of Jovian-moon gravity-assisted trajectories in CR3BP. Adv. Space Res. 2022, 69, 2865–2882. [Google Scholar] [CrossRef]
- Silvestrini, S.; Lunghi, P.; Piccinin, M.; Zanotti, G.; Lavagna, M. Artificial Intelligence Techniques in Autonomous Vision-Based Navigation System for Lunar Landing. In Proceedings of the 71st International Astronautical Congress (IAC 2020), Online, 12–14 October 2020; pp. 1–11. [Google Scholar]
- Salcedo-Sanz, S.; Ghamisi, P.; Piles, M.; Werner, M.; Cuadra, L.; Moreno-Martínez, A.; Izquierdo-Verdiguier, E.; Muñoz-Marí, J.; Mosavi, A.; Camps-Valls, G. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources. Inf. Fusion 2020, 63, 256–272. [Google Scholar] [CrossRef]
- Ferreira, B.; Iten, M.; Silva, R.G. Monitoring sustainable development by means of earth observation data and machine learning: A review. Environ. Sci. Eur. 2020, 32, 1–17. [Google Scholar] [CrossRef]
- Furano, G.; Meoni, G.; Dunne, A.; Moloney, D.; Ferlet-Cavrois, V.; Tavoularis, A.; Byrne, J.; Buckley, L.; Psarakis, M.; Voss, K.O.; et al. Towards the use of artificial intelligence on the edge in space systems: Challenges and opportunities. IEEE Aerosp. Electron. Syst. Mag. 2020, 35, 44–56. [Google Scholar] [CrossRef]
- Meng, Q.; Huang, M.; Xu, Y.; Liu, N.; Xiang, X. Decentralized Distributed Deep Learning with Low-Bandwidth Consumption for Smart Constellations. Space Sci. Technol. 2021, 2021, 9879246. [Google Scholar] [CrossRef]
- Pastena, M.C.; Mathieu, B.; Regan, P.; Esposito, A.; Conticello, M.; Van Dijk, S.; Vercruyssen, C.; Foglia, N.; Koelemann, P.; Hefele, R.J. ESA Earth Observation Directorate NewSpace initiatives. In Proceedings of the USU Conference on Small Satellites, Logan, UT, USA, 3–8 August 2019. [Google Scholar]
- Camps-Valls, G.; Sejdinovic, D.; Runge, J.; Reichstein, M. A perspective on Gaussian processes for Earth observation. Natl. Sci. Rev. 2019, 6, 616–618. [Google Scholar] [CrossRef] [Green Version]
- Stromann, O.; Nascetti, A.; Yousif, O.; Ban, Y. Dimensionality reduction and feature selection for object-based land cover classification based on Sentinel-1 and Sentinel-2 time series using Google Earth Engine. Remote Sens. 2019, 12, 76. [Google Scholar] [CrossRef] [Green Version]
- Luo, F.; Zhang, L.; Du, B.; Zhang, L. Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2020, 58, 5336–5353. [Google Scholar] [CrossRef]
- Zhu, L.; Chen, Y.; Ghamisi, P.; Benediktsson, J.A. Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5046–5063. [Google Scholar] [CrossRef]
- Zhang, H.; Song, Y.; Han, C.; Zhang, L. Remote sensing image spatiotemporal fusion using a generative adversarial network. IEEE Trans. Geosci. Remote Sens. 2020, 59, 4273–4286. [Google Scholar] [CrossRef]
- Jiang, K.; Xie, W.; Li, Y.; Lei, J.; He, G.; Du, Q. Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection. IEEE Trans. Geosci. Remote Sens. 2020, 58, 5224–5236. [Google Scholar] [CrossRef]
- Helber, P.; Bischke, B.; Dengel, A.; Borth, D. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2217–2226. [Google Scholar] [CrossRef] [Green Version]
- Storie, C.D.; Henry, C.J. Deep learning neural networks for land use land cover mapping. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 3445–3448. [Google Scholar]
- Mou, L.; Lu, X.; Li, X.; Zhu, X.X. Nonlocal graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2020, 58, 8246–8257. [Google Scholar] [CrossRef]
- Hong, D.; Yokoya, N.; Chanussot, J.; Zhu, X.X. An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Trans. Image Process. 2018, 28, 1923–1938. [Google Scholar] [CrossRef] [Green Version]
- Yao, J.; Meng, D.; Zhao, Q.; Cao, W.; Xu, Z. Nonconvex-sparsity and nonlocal-smoothness-based blind hyperspectral unmixing. IEEE Trans. Image Process. 2019, 28, 2991–3006. [Google Scholar] [CrossRef]
- Castillo-Navarro, J.; Le Saux, B.; Boulch, A.; Lefèvre, S. Energy-based models in earth observation: From generation to semi-supervised learning. IEEE Trans. Geosci. Remote Sens. 2021. [Google Scholar] [CrossRef]
- Sun, X.; Shi, A.; Huang, H.; Mayer, H. BAS4Net: Boundary-aware semi-supervised semantic segmentation network for very high resolution remote sensing images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 5398–5413. [Google Scholar] [CrossRef]
- Castillo-Navarro, J.; Le Saux, B.; Boulch, A.; Audebert, N.; Lefèvre, S. Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance suite, dataset analysis and multi-task network study. Mach. Learn. 2021, 1–36. [Google Scholar] [CrossRef]
- Dalsasso, E.; Denis, L.; Tupin, F. SAR2SAR: A semi-supervised despeckling algorithm for SAR images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 4321–4329. [Google Scholar] [CrossRef]
- Santangeli, A.; Chen, Y.; Kluen, E.; Chirumamilla, R.; Tiainen, J.; Loehr, J. Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land. Sci. Rep. 2020, 10, 10993. [Google Scholar] [CrossRef] [PubMed]
- Linaza, M.T.; Posada, J.; Bund, J.; Eisert, P.; Quartulli, M.; Döllner, J.; Pagani, A.; G Olaizola, I.; Barriguinha, A.; Moysiadis, T.; et al. Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy 2021, 11, 1227. [Google Scholar] [CrossRef]
- Ruiz-Real, J.L.; Uribe-Toril, J.; Torres Arriaza, J.A.; de Pablo Valenciano, J. A Look at the past, present and future research trends of artificial intelligence in agriculture. Agronomy 2020, 10, 1839. [Google Scholar] [CrossRef]
- Bannerjee, G.; Sarkar, U.; Das, S.; Ghosh, I. Artificial intelligence in agriculture: A literature survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 2018, 7, 1–6. [Google Scholar]
- Sazib, N.; Mladenova, l.E.; Bolten, J.D. Assessing the impact of ENSO on agriculture over Africa using earth observation data. Front. Sustain. Food Syst. 2020, 4, 509914. [Google Scholar] [CrossRef]
- Bestelmeyer, B.T.; Marcillo, G.; McCord, S.E.; Mirsky, S.; Moglen, G.; Neven, L.G.; Peters, D.; Sohoulande, C.; Wakie, T. Scaling up agricultural research with artificial intelligence. IT Prof. 2020, 22, 33–38. [Google Scholar] [CrossRef]
- Ben Ayed, R.; Hanana, M. Artificial intelligence to improve the food and agriculture sector. J. Food Qual. 2021, 2021, 5584754. [Google Scholar] [CrossRef]
- Jung, J.; Maeda, M.; Chang, A.; Bhandari, M.; Ashapure, A.; Landivar-Bowles, J. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr. Opin. Biotechnol. 2021, 70, 15–22. [Google Scholar] [CrossRef]
- Jha, K.; Doshi, A.; Patel, P.; Shah, M. A comprehensive review on automation in agriculture using artificial intelligence. Artif. Intell. Agric. 2019, 2, 510–513. [Google Scholar] [CrossRef]
- Zhang, P.; Guo, Z.; Ullah, S.; Melagraki, G.; Afantitis, A.; Lynch, I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nat. Plants 2021, 7, 864–876. [Google Scholar] [CrossRef] [PubMed]
- Camaréna, S. Artificial intelligence in the design of the transitions to sustainable food systems. J. Clean. Prod. 2020, 271, 122574. [Google Scholar] [CrossRef]
- Li, C. Biodiversity assessment based on artificial intelligence and neural network algorithms. Microprocess. Microsyst. 2020, 79, 103321. [Google Scholar] [CrossRef]
- Antonelli, A.; Goria, S.; Sterner, T.; Silvestro, D. Optimising biodiversity protection through artificial intelligence. bioRxiv 2021. [Google Scholar] [CrossRef]
- Sun, W.; Bocchini, P.; Davison, B.D. Applications of artificial intelligence for disaster management. Nat. Hazards 2020, 103, 2631–2689. [Google Scholar] [CrossRef]
- Tan, L.; Guo, J.; Mohanarajah, S.; Zhou, K. Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Nat. Hazards 2021, 107, 2389–2417. [Google Scholar] [CrossRef]
- Schofield, M. An Artificial Intelligence (AI) Approach to Controlling Disaster Scenarios. In Future Role of Sustainable Innovative Technologies in Crisis Management; IGI Global: Hershey, PA, USA, 2022; pp. 28–46. [Google Scholar]
- Kankanamge, N.; Yigitcanlar, T.; Goonetilleke, A. Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane. Telemat. Inform. 2021, 65, 101729. [Google Scholar] [CrossRef]
- Alam, F.; Ofli, F.; Imran, M. Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behav. Inf. Technol. 2020, 39, 288–318. [Google Scholar] [CrossRef]
- Raza, M.; Awais, M.; Ali, K.; Aslam, N.; Paranthaman, V.V.; Imran, M.; Ali, F. Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform. Future Gener. Comput. Syst. 2020, 112, 1057–1069. [Google Scholar] [CrossRef]
- Stein, A.L. Artificial intelligence and climate change. Yale J. Reg. 2020, 37, 890. [Google Scholar]
- Huntingford, C.; Jeffers, E.S.; Bonsall, M.B.; Christensen, H.M.; Lees, T.; Yang, H. Machine learning and artificial intelligence to aid climate change research and preparedness. Environ. Res. Lett. 2019, 14, 124007. [Google Scholar] [CrossRef] [Green Version]
- Kaack, L.; Donti, P.; Strubell, E.; Kamiya, G.; Creutzig, F.; Rolnick, D. Aligning Artificial Intelligence with Climate Change Mitigation. 2021. Available online: https://hal.archives-ouvertes.fr/hal-03368037/file/Kaack_2021_Aligning.pdf (accessed on 15 September 2021).
- Malik, R.; Pande, S. Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring. J. Artif. Intell. Syst. 2020, 2, 168–190. [Google Scholar] [CrossRef]
- Walsh, T.; Evatt, A.; de Witt, C.S. Artificial Intelligence & Climate Change: Supplementary Impact Report; Technical Report; University of Oxford: Oxford, UK, 2020. [Google Scholar]
- Nordgren, A. Artificial intelligence and climate change: Ethical issues. J. Inform. Commun. Ethics Soc. 2022. ahead-of-print. [Google Scholar] [CrossRef]
- Taddeo, M.; Tsamados, A.; Cowls, J.; Floridi, L. Artificial intelligence and the climate emergency: Opportunities, challenges, and recommendations. One Earth 2021, 4, 776–779. [Google Scholar] [CrossRef]
- Scola, L. Artificial Intelligence Against Climate Change. In Intelligent Computing; Springer: Cham, Switzerland, 2021; pp. 378–397. [Google Scholar]
- Cowls, J.; Tsamados, A.; Taddeo, M.; Floridi, L. The AI gambit: Leveraging artificial intelligence to combat climate change—Opportunities, challenges, and recommendations. AI Soc. 2021, 1–25. [Google Scholar] [CrossRef]
- Luccioni, A.; Schmidt, V.; Vardanyan, V.; Bengio, Y. Using artificial intelligence to visualize the impacts of climate change. IEEE Comput. Graph. Appl. 2021, 41, 8–14. [Google Scholar] [CrossRef]
- Chakraborty, D.; Alam, A.; Chaudhuri, S.; Başağaoğlu, H.; Sulbaran, T.; Langar, S. Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence. Appl. Energy 2021, 291, 116807. [Google Scholar] [CrossRef]
- Zendehboudi, A.; Baseer, M.A.; Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review. J. Clean. Prod. 2018, 199, 272–285. [Google Scholar] [CrossRef]
- Elkiran, G.; Nourani, V.; Elvis, O.; Abdullahi, J. Impact of climate change on hydro-climatological parameters in North Cyprus: Application of artificial intelligence-based statistical downscaling models. J. Hydroinform. 2021, 23, 1395–1415. [Google Scholar] [CrossRef]
- Xiang, X.; Li, Q.; Khan, S.; Khalaf, O.I. Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environ. Impact Assess. Rev. 2021, 86, 106515. [Google Scholar] [CrossRef]
- Ighalo, J.O.; Adeniyi, A.G.; Marques, G. Artificial intelligence for surface water quality monitoring and assessment: A systematic literature analysis. Model. Earth Syst. Environ. 2021, 7, 669–681. [Google Scholar] [CrossRef]
- Sanchez-Pi, N.; Marti, L.; Abreu, A.; Bernard, O.; de Vargas, C.; Eveillard, D.; Maass, A.; Marquet, P.A.; Sainte-Marie, J.; Salomon, J.; et al. Artificial intelligence, machine learning and modeling for understanding the oceans and climate change. In Proceedings of the NeurIPS 2020 Workshop-Tackling Climate Change with Machine Learning, Online, 11–12 December 2020. [Google Scholar]
- Doorn, N. Artificial intelligence in the water domain: Opportunities for responsible use. Sci. Total Environ. 2021, 755, 142561. [Google Scholar] [CrossRef] [PubMed]
- Sharifi, E.; Saghafian, B.; Steinacker, R. Downscaling satellite precipitation estimates with multiple linear regression, artificial neural networks, and spline interpolation techniques. J. Geophys. Res. Atmos. 2019, 124, 789–805. [Google Scholar] [CrossRef] [Green Version]
- Aldhyani, T.H.; Al-Yaari, M.; Alkahtani, H.; Maashi, M. Water quality prediction using artificial intelligence algorithms. Appl. Bionics Biomech. 2020, 2020, 6659314. [Google Scholar] [CrossRef]
- Hmoud Al-Adhaileh, M.; Waselallah Alsaade, F. Modelling and prediction of water quality by using artificial intelligence. Sustainability 2021, 13, 4259. [Google Scholar] [CrossRef]
- Gunda, N.S.K.; Gautam, S.H.; Mitra, S.K. Artificial intelligence based mobile application for water quality monitoring. J. Electrochem. Soc. 2019, 166, B3031. [Google Scholar] [CrossRef]
- Ye, Z.; Yang, J.; Zhong, N.; Tu, X.; Jia, J.; Wang, J. Tackling environmental challenges in pollution controls using artificial intelligence: A review. Sci. Total Environ. 2020, 699, 134279. [Google Scholar] [CrossRef]
- Cihan, P.; Ozel, H.; Ozcan, H.K. Modeling of atmospheric particulate matters via artificial intelligence methods. Environ. Monit. Assess. 2021, 193, 1–15. [Google Scholar] [CrossRef]
- AlOmar, M.K.; Hameed, M.M.; AlSaadi, M.A. Multi hours ahead prediction of surface ozone gas concentration: Robust artificial intelligence approach. Atmos. Pollut. Res. 2020, 11, 1572–1587. [Google Scholar] [CrossRef]
- Yang, B.; Li, S.; Liu, X.; Huang, X.; Huang, X. Fast optimization method for Mars high-fidelity aerobraking trajectory using a neural network. Sci. Sin. Technol. 2020, 50, 1185–1199. [Google Scholar] [CrossRef]
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Russo, A.; Lax, G. Using Artificial Intelligence for Space Challenges: A Survey. Appl. Sci. 2022, 12, 5106. https://doi.org/10.3390/app12105106
Russo A, Lax G. Using Artificial Intelligence for Space Challenges: A Survey. Applied Sciences. 2022; 12(10):5106. https://doi.org/10.3390/app12105106
Chicago/Turabian StyleRusso, Antonia, and Gianluca Lax. 2022. "Using Artificial Intelligence for Space Challenges: A Survey" Applied Sciences 12, no. 10: 5106. https://doi.org/10.3390/app12105106