Articles | Volume 11, issue 1
https://doi.org/10.5194/gi-11-195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-11-195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
02 Jun 2022
Review article |
| 02 Jun 2022
| 02 Jun 2022
GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data
Roberto Pierdicca
Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, Ancona, Italy
Invited contribution by Roberto Pierdicca, recipient of the EGU Geosciences Instrumentation and Data Systems Division Outstanding Early Career Scientists Award 2021.
Marina Paolanti
CORRESPONDING AUTHOR
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy
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M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
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C. Morbidoni, R. Pierdicca, R. Quattrini, and E. Frontoni
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S. Chiappini, A. Fini, E. S. Malinverni, E. Frontoni, G. Racioppi, and R. Pierdicca
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F. Matrone, A. Lingua, R. Pierdicca, E. S. Malinverni, M. Paolanti, E. Grilli, F. Remondino, A. Murtiyoso, and T. Landes
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1419–1426, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020, 2020
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W15, 871–878, https://doi.org/10.5194/isprs-archives-XLII-2-W15-871-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W15-871-2019, 2019
E. S. Malinverni, S. Chiappini, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 771–776, https://doi.org/10.5194/isprs-archives-XLII-2-W11-771-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-771-2019, 2019
E. S. Malinverni, R. Pierdicca, F. Di Stefano, M. Sturari, M. Mameli, E. Frontoni, R. Orazi, and F. Colosi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 785–792, https://doi.org/10.5194/isprs-archives-XLII-2-W11-785-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-785-2019, 2019
R. Quattrini, R. Pierdicca, A. Lucidi, F. Di Stefano, and E. S. Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W9, 647–654, https://doi.org/10.5194/isprs-archives-XLII-2-W9-647-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W9-647-2019, 2019
G. Fangi, R. Pierdicca, M. Sturari, and E. S. Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 331–337, https://doi.org/10.5194/isprs-archives-XLII-2-331-2018, https://doi.org/10.5194/isprs-archives-XLII-2-331-2018, 2018
R. Pierdicca, E. S. Malinverni, F. Piccinini, M. Paolanti, A. Felicetti, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 893–900, https://doi.org/10.5194/isprs-archives-XLII-2-893-2018, https://doi.org/10.5194/isprs-archives-XLII-2-893-2018, 2018
E. S. Malinverni, R. Pierdicca, M. Sturari, F. Colosi, and R. Orazi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 203–210, https://doi.org/10.5194/isprs-archives-XLII-5-W1-203-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-203-2017, 2017
R. Quattrini, R. Pierdicca, C. Morbidoni, and E. S. Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 275–281, https://doi.org/10.5194/isprs-archives-XLII-5-W1-275-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-275-2017, 2017
Related subject area
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Short summary
Short summary
Transportation networks, such as road or river systems and electricity grids, are often equipped with various sensors that produce vast amounts of data that reflect real-life situations on these networks. We present an all-in-one data management solution to deal with these data. Our solution addresses both the storage and querying of such data and is based on a theoretical database model and query logic. In addition, this is implemented and tested on real-life datasets.
This article is included in the Encyclopedia of Geosciences
Matthias Zeeman, Andreas Christen, Sue Grimmond, Daniel Fenner, William Morrison, Gregor Feigel, Markus Sulzer, and Nektarios Chrysoulakis
EGUsphere, https://doi.org/10.5194/egusphere-2024-1469, https://doi.org/10.5194/egusphere-2024-1469, 2024
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Overview of a data system for documenting, processing, managing and publishing data streams from research networks of atmospheric/environmental sensors of varying complexity in urban environments. Our solutions aim to deliver resilient, near-time data using freely-available software.
This article is included in the Encyclopedia of Geosciences
Chentao Wang, Ming Deng, Nini Duan, Xiaoxi Ma, and Meng Wang
Geosci. Instrum. Method. Data Syst., 12, 187–200, https://doi.org/10.5194/gi-12-187-2023, https://doi.org/10.5194/gi-12-187-2023, 2023
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This paper proposes a new online data transmission technology for marine controlled-source electromagnetic (MCSEM) transmitters. The technology enables high-precision data acquisition, storage, and ethernet file transmission and offers significant convenience. This technology has the potential to revolutionize the application of MCSEM transmitters in marine explorations and to offer significant convenience.
This article is included in the Encyclopedia of Geosciences
Thomas Wutzler, Oscar Perez-Priego, Kendalynn Morris, Tarek S. El-Madany, and Mirco Migliavacca
Geosci. Instrum. Method. Data Syst., 9, 239–254, https://doi.org/10.5194/gi-9-239-2020, https://doi.org/10.5194/gi-9-239-2020, 2020
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Continuous data of soil CO2 efflux can improve model prediction of climate warming, and automated data are becoming increasingly available. However, aggregating chamber-based data to plot scale pose challenges. Therefore, we showed, using 1 year of half-hourly data, how using the lognormal assumption tackles several challenges. We propose that plot-scale SO2 efflux observations should be reported together with lognormally based uncertainties and enter model constraining frameworks at log scale.
This article is included in the Encyclopedia of Geosciences
David Fuertes, Carlos Toledano, Ramiro González, Alberto Berjón, Benjamín Torres, Victoria E. Cachorro, and Ángel M. de Frutos
Geosci. Instrum. Method. Data Syst., 7, 67–81, https://doi.org/10.5194/gi-7-67-2018, https://doi.org/10.5194/gi-7-67-2018, 2018
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CÆLIS is a software system which aims at simplifying the management of a photometric ground-based network, providing tools by monitoring the instruments, processing the data in real time and offering the scientific community a new tool to work with the data. The present work describes the system architecture of CÆLIS and some examples of applications and data processing.
This article is included in the Encyclopedia of Geosciences
Charles W. Magee Jr., Martin Danišík, and Terry Mernagh
Geosci. Instrum. Method. Data Syst., 6, 523–536, https://doi.org/10.5194/gi-6-523-2017, https://doi.org/10.5194/gi-6-523-2017, 2017
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The paper demonstrates that isotopologue disequilibrium can be created in the SIMS ionization process, and that the specific conditions under which it is created during the oxygen bombardment of geological materials are consistent with known conditions where traditional interrelationships between ion abundances break down. Further study to determine the degree of radiation dosage at which extreme disequilibrium appears involved Raman and helium dating on a variety of well-characterized zircons.
This article is included in the Encyclopedia of Geosciences
Sergey Y. Khomutov, Oksana V. Mandrikova, Ekaterina A. Budilova, Kusumita Arora, and Lingala Manjula
Geosci. Instrum. Method. Data Syst., 6, 329–343, https://doi.org/10.5194/gi-6-329-2017, https://doi.org/10.5194/gi-6-329-2017, 2017
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Noise is a common problem for the experiments or observations. Noise in the raw data of magnetic observatories has features. The article makes an attempt to give a review of this noise, using the data from some Russian and Indian observatories.
This article is included in the Encyclopedia of Geosciences
M. B. Krassovski, J. S. Riggs, L. A. Hook, W. R. Nettles, P. J. Hanson, and T. A. Boden
Geosci. Instrum. Method. Data Syst., 4, 203–213, https://doi.org/10.5194/gi-4-203-2015, https://doi.org/10.5194/gi-4-203-2015, 2015
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Ecosystem-scale manipulation experiments are getting more complicated and require innovative approaches that help manage high volumes of in situ observations. New large-scale, well-designed, and reliable data acquisition and management systems will become common it the future. The presented approach shows an example of such a system that was built in a remote and harsh environmental location. The provided details can be used for the design of similar systems for other experiments in future.
This article is included in the Encyclopedia of Geosciences
T. A. Boden, M. Krassovski, and B. Yang
Geosci. Instrum. Method. Data Syst., 2, 165–176, https://doi.org/10.5194/gi-2-165-2013, https://doi.org/10.5194/gi-2-165-2013, 2013
E. de Lucas, M. J. Miguel, D. Mozos, and L. Vázquez
Geosci. Instrum. Method. Data Syst., 1, 23–31, https://doi.org/10.5194/gi-1-23-2012, https://doi.org/10.5194/gi-1-23-2012, 2012
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Short summary
For the processing of geomatics data, artificial intelligence (AI) offers overwhelming opportunities. The integration of AI approaches in geomatics has developed into the concept of geospatial artificial intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. This contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. How AI approaches have been exploited for the interpretation of geomatic data is explained.
For the processing of geomatics data, artificial intelligence (AI) offers overwhelming...
