Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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<p>Monitoring and protection of natural resources have grown increasingly important in recent years, since the effect of emerging illnesses has caused serious concerns among environmentalists and communities. In this regard, tree roots are one of the most crucial and fragile plant organs, as well as one of the most difficult to assess [1].</p> <p>Within this context, ground penetrating radar (GPR) applications have shown to be precise and effective for investigating and mapping tree roots [2]. Furthermore, in order to overcome limitations arising from natural soil heterogeneity, a recent study has proven the feasibility of deep learning image-based detection and classification methods applied to the GPR investigation of tree roots [3].</p> <p>The present research proposes an analysis of the effect of root orientation on the GPR detection of tree root systems. To this end, a dedicated survey methodology was developed for compilation of a database of isolated roots. A set of GPR data was collected with different incidence angles with respect to each investigated root. The GPR signal is then processed in both temporal and frequency domains to filter out existing noise-related information and obtain spectrograms (i.e. a visual representation of a signal's frequency spectrum relative to time). Subsequently, an image-based deep learning framework is implemented, and its performance in recognising outputs with different incidence angles is compared to traditional machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the use of novel ways to enhance the interpretation of tree root systems.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust. The Authors would also like to thank the Ealing Council and the Walpole Park for facilitating this research.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Innes, J. L., 1993. <em>Forest health: its assessment and status</em>. CAB International.</p> <p>[2] Lantini, L., Tosti, F., Giannakis, I., Zou, L., Benedetto, A. and Alani, A. M., 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing 12(20), 3417.</p> <p>[3] Lantini, L., Massimi, F., Tosti, F., Alani, A. M. and Benedetto, F. "A Deep Learning Approach for Tree Root Detection using GPR Spectrogram Imagery," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 391-394.</p>

Monitoring and conservation of natural resources such as trees have become necessary as the impact of new diseases attacking the integrity of trees has created major concerns for environmentalists and communities in recent years. Within this context, tree roots are one of the plants' most important and vulnerable organs as well as one of the most challenging ones to inspect. Tree roots naturally are developed under the ground, hence difficult to be seen and access. To that effect, the non-destructive testing (NDT) methods have become one of the preferred methods of tree roots assessment and monitoring as opposed to other conventional and destructive techniques. The applications of the ground penetrating radar (GPR) have proven to be an accurate approach and methodology for the investigation and mapping of tree roots. However, a major challenge for GPR detection of tree roots architecture and pattern accurately is the soil inhomogeneity, including the presence of various natural and artificial features within the soil. This study aims to mitigate the uncertainty in root detection by proposing a deep learning method based on the analysis of GPR spectrograms (i.e., a graphic representation of a signal's frequency spectrum with respect to time). In this study, the GPR signal is first processed in both the time and frequency domains to filter the existing noise-related information and hence, to produce spectrograms. Subsequently, an image-based deep learning framework is implemented, and the effectiveness in detecting tree roots is analysed in comparison with conventional feature-based machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the implementation of new methodologies in assessing tree root systems.

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