MAL11

I will expose some possibilities regarding the use of metallic probes of different lengths in GPR and TDR prospecting. With regard to GPR, multi-length probes are dipole-like antennas whose length can be changed by means of switches. The switches can be implemented with PIN diodes, and can act as electronic “knifes”. Therefore, they allow to cut (switched off) or prolong (switched on) the branches of a couple of antennas, and this allows to have more couples of equivalent antennas making use of a unique physical couple of antennas. This allows to contain the size of the system. In particular, a reconfigurable prototypal stepped frequency GPR system was developed within the project AITECH (http://www.aitechnet.com/ibam.html) and was tested in several cases histories  [1-3]. Within this reconfigurable GPR, it is also possible to reconfigure vs. the frequency the integration times of the harmonic tones constituting the radiated signal. This feature allows to reject external electromagnetic interferences without filtering the spectrum of the received signal [4] and without increasing the radiated power.

With regard to TDR measurements, a multi-length probe consists of a TDR device where the rods (in multi-wire version) or the length of internal and external conductor (in coaxial version) can be changed. This can be useful for the measurements of electromagnetic characteristics of a material under test (MUT), in particular its dielectric permittivity and magnetic permeability, both meant in general as complex quantities. Multi-length TDR measurements allow to acquire independent information on the MUT even at single frequency, and this can be of interest in the case of dispersive materials [5-6].

Acknowledgements

I collaborated with several colleagues about the above issues. To list of them would be long, so I will just mention their affiliations: Florence Engineering srl, University of Florence, IDSGeoradar srl, 3d-radar Ltd, Institute for Archaeological and Monumental Heritage IBAM-CNR, University of Bari, University of Malta. Finally, a particular mention is deserved for the Cost Action TU1208.

References

[1] R. Persico, M. Ciminale, L. Matera, A new reconfigurable stepped frequency GPR system, possibilities and issues; applications to two different Cultural Heritage Resources, Near Surface Geophysics, 12, 793-801, 2014.

[2] L. Matera, M. Noviello, M. Ciminale, R. Persico, Integration of multisensor data: an experiment in the archaeological park of Egnazia (Apulia, Southern Italy), Near Surface Geophysics, 13, 613-621, 2015.

[3] R. Persico, S. D'Amico, L. Matera, E. Colica, C. De, Giorgio, A. Alescio, C. Sammut and P. Galea, P. (2019), GPR Investigations at St John's Co‐Cathedral in Valletta, Near Surface Geophysics, 17, 213-229, 2019.

[4] R. Persico, D. Dei, F. Parrini, L. Matera, Mitigation of narrow band interferences by means of a reconfigurable stepped frequency GPR system, Radio Science, 51, 2016.

[5] R. Persico, M. Pieraccini, Measurement of dielectric and magnetic properties of Materials by means of a TDR probe, Near Surface Geophysics, 16,1-9, 2018.

[6] R. Persico, I. Farhat, L. Farrugia, S. d’Amico, C. Sammut, An innovative use of TDR probes: First numerical validations with a coaxial cable, Journal of Environmental & Engineering Geophysics, 23, 437-442, 2018.

How to cite: Persico, R.: Multi-length probes in GPR and TDR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16499, https://doi.org/10.5194/egusphere-egu21-16499, 2021.

Nowadays, the exploitation artificial intelligence approached is overwhelming in several domains. As well, geomatic data are becoming more and more complex and heterogeneous, as they are collected with multi-source data collection techniques. Remotely sensed data, point cloud, thermal images and more are just few examples of complex data which requires growing computational capabilities, but, foremost, powerful tools of processing and interpretation.

The applications of modern AI-based algorithms for the processing of geomatics data offer opportunities that wouldn't be affordable up to few years ago. For geospatial domains, fundamental questions include how AI can be specifically applied to or has to be specifically created for geomatics data. This change is also having a significant impact on geospatial data. Machine Learning (ML) has been a core component of spatial analysis for classification, clustering, and prediction. In addition, Deep Learning (DL) is being integrated to automatically extract useful information with the task of classification, object detection, semantic and instance segmentation, etc. The integration of AI, ML, and DL in geomatics has developed into the concept of Geospatial Artificial Intelligence (GeoAI) that is a new paradigm for geographic knowledge discovery and beyond. 

This talk aims at giving a sight on the emergent discipline called GeoAI, a novel research field in which cutting edge learning based methods are applied to enhance the knowledge and improve the ability of humans to manage complex information. Beside providing a picture of the latest achievements in the filed (outlining AI-based techniques for the analysis and the interpretation of complex geomatics data), this lecture will provide several examples of researches and applications, demonstrating opportunities, challenges and limitations with practical examples. 

Bearing in mind that, for the upcoming future, the "man on the loop" will always assess unpredictable outcomes from the automatization process, it will be demonstrated, at different scales of representation and facing research challenges in different domains (e.g. environmental challenges, forestry, cultural heritage, tourism just to mention some), AI outperforms manual operations in terms of both cost effectiveness and reliability. 

How to cite: Pierdicca, R.: GeoAI:artificial intelligence for interpretation and processing of complex geomatic data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1482, https://doi.org/10.5194/egusphere-egu21-1482, 2021.