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
Related authors
Mattia Balestra, Federico Fattorini, Renato Angeloni, Laura Nanni, Adriano Mancini, Roberto Papa, and Roberto Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 17–24, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-17-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-17-2024, 2024
Stefano Chiappini, Mattia Balestra, Federico Giulioni, Ernesto Marcheggiani, Eva Savina Malinverni, and Roberto Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 61–68, https://doi.org/10.5194/isprs-annals-X-3-2024-61-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-61-2024, 2024
Marsia Sanità, Jonathan Fratini, Nikhil Muralikrishna, Roberto Pierdicca, and Eva Savina Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-2024, 389–395, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-389-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-389-2024, 2024
M. Balestra, R. Pierdicca, L. Cesaretti, G. Quattrini, A. Mancini, A. Galli, E. S. Malinverni, S. Casavecchia, and S. Pesaresi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 33–40, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-33-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-33-2023, 2023
R. Pierdicca, L. Nepi, A. Mancini, E. S. Malinverni, and M. Balestra
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 1089–1096, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1089-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1089-2023, 2023
R. Pierdicca, L. Gorgoglione, M. Martuscelli, and S. Usmanov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 1225–1232, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1225-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1225-2023, 2023
D. Abate, A. Agapiou, K. Toumbas, A. Lampropoulos, K. Petrides, R. Pierdicca, M. Paolanti, F. Di Stefano, A. Felicetti, E. S. Malinverni, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 3–10, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, 2023
E. Balloni, L. Gorgoglione, M. Paolanti, A. Mancini, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 155–162, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, 2023
F. Matrone, A. Felicetti, M. Paolanti, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-M-1-2023, 207–214, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, 2023
F. Di Stefano, R. Pierdicca, E. S. Malinverni, A. Corneli, and B. Naticchia
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 131–138, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, 2023
D. Abate, M. Paolanti, R. Pierdicca, A. Lampropoulos, K. Toumbas, A. Agapiou, S. Vergis, E. Malinverni, K. Petrides, A. Felicetti, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 729–736, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, 2022
M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 833–839, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, 2022
A. Murtiyoso, F. Matrone, M. Martini, A. Lingua, P. Grussenmeyer, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 317–324, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, 2022
C. Morbidoni, R. Pierdicca, R. Quattrini, and E. Frontoni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4-W1-2020, 95–102, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020, 2020
S. Chiappini, A. Fini, E. S. Malinverni, E. Frontoni, G. Racioppi, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 441–448, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-441-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-441-2020, 2020
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
E. S. Malinverni, R. Pierdicca, M. Paolanti, M. Martini, C. Morbidoni, F. Matrone, and A. Lingua
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W15, 735–742, https://doi.org/10.5194/isprs-archives-XLII-2-W15-735-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W15-735-2019, 2019
M. Paolanti, R. Pierdicca, M. Martini, A. Felicetti, E. S. Malinverni, E. Frontoni, and P. Zingaretti
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
Mattia Balestra, Federico Fattorini, Renato Angeloni, Laura Nanni, Adriano Mancini, Roberto Papa, and Roberto Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 17–24, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-17-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-17-2024, 2024
Stefano Chiappini, Mattia Balestra, Federico Giulioni, Ernesto Marcheggiani, Eva Savina Malinverni, and Roberto Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 61–68, https://doi.org/10.5194/isprs-annals-X-3-2024-61-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-61-2024, 2024
Marsia Sanità, Jonathan Fratini, Nikhil Muralikrishna, Roberto Pierdicca, and Eva Savina Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-2024, 389–395, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-389-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-389-2024, 2024
M. Balestra, R. Pierdicca, L. Cesaretti, G. Quattrini, A. Mancini, A. Galli, E. S. Malinverni, S. Casavecchia, and S. Pesaresi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 33–40, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-33-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-33-2023, 2023
R. Pierdicca, L. Nepi, A. Mancini, E. S. Malinverni, and M. Balestra
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 1089–1096, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1089-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1089-2023, 2023
S. Pansoni, S. Tiribelli, M. Paolanti, F. Di Stefano, E. Frontoni, E. S. Malinverni, and B. Giovanola
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 1149–1155, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1149-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1149-2023, 2023
R. Pierdicca, L. Gorgoglione, M. Martuscelli, and S. Usmanov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 1225–1232, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1225-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1225-2023, 2023
D. Abate, A. Agapiou, K. Toumbas, A. Lampropoulos, K. Petrides, R. Pierdicca, M. Paolanti, F. Di Stefano, A. Felicetti, E. S. Malinverni, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 3–10, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-3-2023, 2023
E. Balloni, L. Gorgoglione, M. Paolanti, A. Mancini, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 155–162, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-155-2023, 2023
F. Matrone, A. Felicetti, M. Paolanti, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-M-1-2023, 207–214, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, https://doi.org/10.5194/isprs-annals-X-M-1-2023-207-2023, 2023
F. Di Stefano, R. Pierdicca, E. S. Malinverni, A. Corneli, and B. Naticchia
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 131–138, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023, 2023
D. Abate, M. Paolanti, R. Pierdicca, A. Lampropoulos, K. Toumbas, A. Agapiou, S. Vergis, E. Malinverni, K. Petrides, A. Felicetti, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 729–736, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, 2022
M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 833–839, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, 2022
A. Murtiyoso, F. Matrone, M. Martini, A. Lingua, P. Grussenmeyer, and R. Pierdicca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 317–324, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, 2022
C. Morbidoni, R. Pierdicca, R. Quattrini, and E. Frontoni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4-W1-2020, 95–102, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020, 2020
S. Chiappini, A. Fini, E. S. Malinverni, E. Frontoni, G. Racioppi, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 441–448, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-441-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-441-2020, 2020
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
E. S. Malinverni, R. Pierdicca, M. Paolanti, M. Martini, C. Morbidoni, F. Matrone, and A. Lingua
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W15, 735–742, https://doi.org/10.5194/isprs-archives-XLII-2-W15-735-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W15-735-2019, 2019
M. Paolanti, R. Pierdicca, M. Martini, A. Felicetti, E. S. Malinverni, E. Frontoni, and P. Zingaretti
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
Data management
Managing Data of Sensor-Equipped Transportation Networks using Graph Databases
Modular approach to near-time data management for multi-city atmospheric environmental observation campaigns
Research on online data transmission technology in a marine controlled-source electromagnetic transmitter
Soil CO2 efflux errors are lognormally distributed – implications and guidance
CÆLIS: software for assimilation, management and processing data of an atmospheric measurement network
Extreme isotopologue disequilibrium in molecular SIMS species during SHRIMP geochronology
Noise in raw data from magnetic observatories
A comprehensive data acquisition and management system for an ecosystem-scale peatland warming and elevated CO2 experiment
The AmeriFlux data activity and data system: an evolving collection of data management techniques, tools, products and services
Martian dust devils detector over FPGA
Erik Bollen, Rik Hendrix, and Bart Kuijpers
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2024-3, https://doi.org/10.5194/gi-2024-3, 2024
Revised manuscript accepted for GI
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Cited articles
Adegun, A., Akande, N., Ogundokun, R., and Asani, E.: Image segmentation and
classification of large scale satellite imagery for land use: a review of the
state of the arts, Int. J. Civ. Eng. Technol, 9, 1534–1541, 2018. a
Ali, M. U., Khan, H. F., Masud, M., Kallu, K. D., and Zafar, A.: A machine
learning framework to identify the hotspot in photovoltaic module using
infrared thermography, Sol. Energy, 208, 643–651, 2020. a
Audebert, N., Le Saux, B., and Lefèvre, S.: Beyond RGB: Very high
resolution urban remote sensing with multimodal deep networks, ISPRS J. Photogramm., 140, 20–32, 2018. a
Audebert, N., Le Saux, B., and Lefèvre, S.: Deep learning for
classification of hyperspectral data: A comparative review, IEEE Geosci. Remote S., 7, 159–173, 2019. a
Balado, J., Sousa, R., Díaz-Vilariño, L., and Arias, P.: Transfer
Learning in urban object classification: Online images to recognize point
clouds, Automation in Construction, 111, 103058, https://doi.org/10.1016/j.autcon.2019.103058, 2020. a, b, c
Ball, J. E., Anderson, D. T., and Chan Sr, C. S.: Comprehensive survey of deep
learning in remote sensing: theories, tools, and challenges for the
community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609, 2017. a
Bang, H.-T., Park, S., and Jeon, H.: Defect identification of composites via
thermography and deep learning techniques, Compos. Struct., 246, 112405 pp., https://doi.org/10.1016/j.compstruct.2020.112405,
2020. a
Baqersad, J., Poozesh, P., Niezrecki, C., and Avitabile, P.: Photogrammetry and
optical methods in structural dynamics–A review, Mech. Syst.
Signal Pr., 86, 17–34, 2017. a
Bian, J., Tian, D., Tang, Y., and Tao, D.: A survey on trajectory clustering
analysis, arXiv [preprint] arXiv:1802.06971, 2018. a
Bian, J., Tian, D., Tang, Y., and Tao, D.: Trajectory data classification: A
review, ACM T. Intel. Syst. Tec., 10,
1–34, 2019. a
Blais, J. R. and Esche, H.: Geomatics and the new cyber-infrastructure,
Geomatica, 62, 11–22, 2008. a
Böhler, W. and Heinz, G.: Documentation, surveying, photogrammetry, in:
XVII CIPA Symposium, Recife, Olinda, Brazil, vol. 1, 3–6 October 1999, https://www.cipaheritagedocumentation.org/wp-content/uploads/2018/11/Boehler-Heinz-Documentation-surveying-photogrammetry.pdf (last access: 25 May 2022), 1999. a
Boongoen, T. and Iam-On, N.: Cluster ensembles: A survey of approaches with
recent extensions and applications, Computer Science Review, 28, 1–25, 2018. a
Can, G., Mantegazza, D., Abbate, G., Chappuis, S., and Giusti, A.: Semantic
segmentation on Swiss3DCities: A benchmark study on aerial photogrammetric 3D
pointcloud dataset, Pattern Recogn. Lett., 150, 108–114, 2021. a
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.:
Deeplab: Semantic image segmentation with deep convolutional nets, atrous
convolution, and fully connected crfs, IEEE T. Pattern Anal., 40, 834–848, 2017. a
Chen, Y., Xiong, Y., Zhang, B., Zhou, J., and Zhang, Q.: 3D point cloud
semantic segmentation toward large-scale unstructured agricultural scene
classification, Comput. Electron. Agr., 190, 106445, https://doi.org/10.1016/j.compag.2021.106445, 2021. a
Chu, H., Ma, W.-C., Kundu, K., Urtasun, R., and Fidler, S.: Surfconv: Bridging
3d and 2d convolution for rgbd images, in: Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018, 3002–3011, https://doi.org/10.1109/cvpr.2018.00317, 2018. a
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R.,
Franke, U., Roth, S., and Schiele, B.: The cityscapes dataset for semantic
urban scene understanding, in: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016, 3213–3223, https://doi.org/10.1109/cvpr.2016.350, 2016. a
Czerniawski, T. and Leite, F.: Automated segmentation of RGB-D images into a
comprehensive set of building components using deep learning, Adv.
Eng. Inform., 45, 101131, https://doi.org/10.1016/j.aei.2020.101131, 2020. a, b
Dabiri, S., Marković, N., Heaslip, K., and Reddy, C. K.: A deep
convolutional neural network based approach for vehicle classification using
large-scale GPS trajectory data, Transport. Res. C-Emer., 116, 102644, https://doi.org/10.1016/j.trc.2020.102644, 2020. a, b
Di Stefano, F., Chiappini, S., Gorreja, A., Balestra, M., and Pierdicca, R.:
Mobile 3D scan LiDAR: a literature review, Geomatics, Natural Hazards and
Risk, 12, 2387–2429, 2021. a
Duan, Y., Liu, S., Hu, C., Hu, J., Zhang, H., Yan, Y., Tao, N., Zhang, C.,
Maldague, X., Fang, Q., Ibarra-Castanedo, C., Chen, D., Li, X., and Meng, J.: Automated defect classification in infrared
thermography based on a neural network, NDT & E Int., 107,
102147, https://doi.org/10.1016/j.ndteint.2019.102147, 2019. a, b, c
Dunderdale, C., Brettenny, W., Clohessy, C., and van Dyk, E. E.: Photovoltaic
defect classification through thermal infrared imaging using a machine
learning approach, Progress in Photovoltaics: Research and Applications, 28, 177–188, 2020. a
Elhamdadi, H., Canavan, S., and Rosen, P.: AffectiveTDA: Using Topological Data
Analysis to Improve Analysis and Explainability in Affective Computing, IEEE T. Vis. Comput. Gr., 28, 769–779, https://doi.org/10.1109/TVCG.2021.3114784, 2021. a
Felicetti, A., Paolanti, M., Zingaretti, P., Pierdicca, R., and Malinverni,
E. S.: Mo. Se.: Mosaic image segmentation based on deep cascading learning,
Virtual Archaeology Review, 12, 25–38, https://doi.org/10.4995/var.2021.14179, 2021. a
Fuhrman, J. D., Gorre, N., Hu, Q., Li, H., El Naqa, I., and Giger, M. L.: A
review of explainable and interpretable AI with applications in COVID-19
imaging, Med. Phys., 49, 1–14, https://doi.org/10.1002/mp.15359, 2021. a
Fu, K.-S. and Mui, J.: A survey on image segmentation, Pattern recognition, 13,
3–16, 1981. a
Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., and Zhang, Q.: Application of
consumer RGB-D cameras for fruit detection and localization in field: A
critical review, Comput. Electron. Agr., 177, 105687, https://doi.org/10.1016/j.compag.2020.105687,
2020. a
Gade, R. and Moeslund, T. B.: Thermal cameras and applications: a survey,
Mach. Vision Appl., 25, 245–262, 2014. a
Geng, X., Ji, S., Lu, M., and Zhao, L.: Multi-Scale Attentive Aggregation for
LiDAR Point Cloud Segmentation, Remote Sensing, 13, 691, https://doi.org/10.3390/rs13040691, 2021. a
Ghamisi, P., Plaza, J., Chen, Y., Li, J., and Plaza, A. J.: Advanced spectral
classifiers for hyperspectral images: A review, IEEE Geosci. Remote S., 5, 8–32, 2017. a
Gomarasca, M. A.: Basics of geomatics, Applied Geomatics, 2, 137–146, 2010. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,
S., Courville, A., and Bengio, Y.: Generative adversarial nets, Adv. Neur. Inf., 27, 2672–2680, 2014. a
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y.: Deep learning,
vol. 1, MIT press Cambridge, ISBN: 9780262035613, 2016. a
Greco, A., Pironti, C., Saggese, A., Vento, M., and Vigilante, V.: A deep
learning based approach for detecting panels in photovoltaic plants, in:
Proceedings of the 3rd International Conference on Applications of
Intelligent Systems, Las Palmas de Gran Canaria, Spain, 7–9 January 2020, 1–7, https://doi.org/10.1145/3378184.3378185, 2020. a, b
Grilli, E., Menna, F., and Remondino, F.: A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 339–344, https://doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017, 2017. a
Groves, P. D.: Principles of GNSS, inertial, and multisensor integrated
navigation systems, [Book review], IEEE Aero. El. Syst.
Mag., 30, 26–27, 2015. a
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S.: Deep learning
for visual understanding: A review, Neurocomputing, 187, 27–48, 2016. a
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., and Bennamoun, M.: Deep learning
for 3d point clouds: A survey, IEEE T. Pattern Anal., 43, 4338–4364, https://doi.org/10.1109/tpami.2020.3005434, 2020. a
Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., and Chanussot, J.: Graph
convolutional networks for hyperspectral image classification, IEEE
T. Geosci. Remote, 59, 5966–5978, https://doi.org/10.1109/tgrs.2020.3015157, 2020a. a, b
James, J.: Travel Mode Identification With GPS Trajectories Using Wavelet
Transform and Deep Learning, IEEE T. Intell. Transp., 22, 1093–1103, https://doi.org/10.1109/tits.2019.2962741, 2020. a, b
Jamiruddin, R., Sari, A. O., Shabbir, J., and Anwer, T.: RGB-depth SLAM review,
arXiv [preprint], 1805.07696, 2018. a
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B.: GeoAI: spatially
explicit artificial intelligence techniques for geographic knowledge
discovery and beyond, 34, 625–636, https://doi.org/10.1080/13658816.2019.1684500, 2020. a
Jiang, X., de Souza, E. N., Pesaranghader, A., Hu, B., Silver, D. L., and
Matwin, S.: TrajectoryNet: an embedded GPS trajectory representation for
point-based classification using recurrent neural networks, in: Proceedings
of the 27th Annual International Conference on Computer Science and Software
Engineering, Markham, Ontario, Canada, 6–8 November 2017, 192–200, https://dl.acm.org/doi/10.5555/3172795.3172817 (last access: 25 May 2022), 2017. a, b
Jiang, Z.: A survey on spatial prediction methods, IEEE T. Knowl. Data En., 31, 1645–1664, 2018. a
Kandeal, A., Elkadeem, M., Thakur, A. K., Abdelaziz, G. B., Sathyamurthy, R.,
Kabeel, A., Yang, N., and Sharshir, S. W.: Infrared thermography-based
condition monitoring of solar photovoltaic systems: A mini review of recent
advances, Sol. Energy, 223, 33–43, 2021. a
Kattenborn, T., Leitloff, J., Schiefer, F., and Hinz, S.: Review on
Convolutional Neural Networks (CNN) in vegetation remote sensing, ISPRS J. Photogramm., 173, 24–49, 2021. a
Kirimtat, A. and Krejcar, O.: A review of infrared thermography for the
investigation of building envelopes: Advances and prospects, Energ. Buildings, 176, 390–406, 2018. a
Konecny, G.: Recent global changes in geomatics education, Int. Arch. Photogramm.,
34, 9–14, 2002. a
Kothari, P., Kreiss, S., and Alahi, A.: Human trajectory forecasting in crowds:
A deep learning perspective, IEEE T. Intell. Transp., April 2021, 1–15, https://doi.org/10.1109/tits.2021.3069362, 2021. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with
deep convolutional neural networks, Adv. Neur. In., 25, 1097–1105, 2012. a
Laska, M. and Blankenbach, J.: Multi-Task Neural Network for Position
Estimation in Large-Scale Indoor Environments, IEEE Access, 10,
26024–26032, 2022. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436, https://doi.org/10.1038/nature14539, 2015. a, b
Lee, J. S., Park, J., and Ryu, Y.-M.: Semantic segmentation of bridge
components based on hierarchical point cloud model, Automat. Constr., 130, 103847, https://doi.org/10.1016/j.autcon.2021.103847, 2021. a
Lemmens, M.: Terrestrial laser scanning, Geo-information, 5, 101–121, 2011. a
Liciotti, D., Paolanti, M., Frontoni, E., and Zingaretti, P.: People detection
and tracking from an RGB-D camera in top-view configuration: review of
challenges and applications, in: International Conference on Image Analysis
and Processing, Catania, Italy, 11–15 September 2017, 207–218, https://doi.org/10.1007/978-3-319-70742-6_20, 2017. a
Liscio, E., Guryn, H., and Stoewner, D.: Accuracy and repeatability of
trajectory rod measurement using laser scanners, J. Forensic Sci., 63, 1506–1515, 2018. a
Li, S., Kang, X., Fang, L., Hu, J., and Yin, H.: Pixel-level image fusion: A
survey of the state of the art, information Fusion, 33, 100–112,
2017. a
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., and Benediktsson, J. A.:
Deep learning for hyperspectral image classification: An overview, IEEE
T. Geosci. Remote, 57, 6690–6709,
2019. a
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., and
Pietikäinen, M.: Deep learning for generic object detection: A survey,
Int. J. Comput. Vision, 128, 261–318, 2020. a
Li, Y., Zhang, H., Xue, X., Jiang, Y., and Shen, Q.: Deep learning for remote
sensing image classification: A survey, Wires Data
Min. Knowl., 8, e1264, https://doi.org/10.1002/widm.1264, 2018. a
Li, Y., Ma, L., Zhong, Z., Liu, F., Chapman, M. A., Cao, D., and Li, J.: Deep
learning for LiDAR point clouds in autonomous driving: a review, IEEE T. Neur. Net. Lear., 32, 3412–3432, https://doi.org/10.1109/tnnls.2020.3015992, 2020. a
Luo, Q., Gao, B., Woo, W. L., and Yang, Y.: Temporal and spatial deep learning
network for infrared thermal defect detection, NDT & E Int., 108,
102164, https://doi.org/10.1016/j.ndteint.2019.102164, 2019. a, b
Malinverni, E. S., Pierdicca, R., Paolanti, M., Martini, M., Morbidoni, C., Matrone, F., and Lingua, A.: DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 735–742, https://doi.org/10.5194/isprs-archives-XLII-2-W15-735-2019, 2019. a
Martín-Jiménez, J. A., Zazo, S., Justel, J. J. A.,
Rodríguez-Gonzálvez, P., and González-Aguilera, D.: Road safety
evaluation through automatic extraction of road horizontal alignments from
Mobile LiDAR System and inductive reasoning based on a decision tree, ISPRS J. Photogramm., 146, 334–346, 2018. a
Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., and
Remondino, F.: Comparing machine and deep learning methods for large 3D
heritage semantic segmentation, ISPRS Int. Geo-Inf., 9, 535, https://doi.org/10.3390/ijgi9090535, 2020a. a
Matrone, F., Lingua, A., Pierdicca, R., Malinverni, E. S., Paolanti, M., Grilli, E., Remondino, F., Murtiyoso, A., and Landes, T.: A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION, 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, 2020b. a
Mehonic, A. and Kenyon, A. J.: Brain-inspired computing needs a master plan,
Nature, 604, 255–260, 2022. a
Mendili, L. E., Puissant, A., Chougrad, M., and Sebari, I.: Towards a
multi-temporal deep learning approach for mapping urban fabric using sentinel
2 images, Remote Sensing, 12, 423, 2020. a
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., and
Terzopoulos, D.: Image segmentation using deep learning: A survey, IEEE
T. Pattern Anal., February 2021, p. 1, https://doi.org/10.1109/TPAMI.2021.3059968, 2021. a
Mitchell, T: Machine Learning, New York, McGraw-hill, ISBN: 978-0-07-042807-2, 1997. a
Morbidoni, C., Pierdicca, R., Paolanti, M., Quattrini, R., and Mammoli, R.:
Learning from Synthetic Point Cloud Data for Historical Buildings Semantic
Segmentation, Journal on Computing and Cultural Heritage (JOCCH), 13, 1–16,
2020. a
Naha, S., Xiao, Q., Banik, P., Alimoor Reza, M., and Crandall, D. J.:
Pose-Guided Knowledge Transfer for Object Part Segmentation, in: Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Workshops, Seattle, WA, USA, 14–19 June 2020, 906–907, https://doi.org/10.1109/cvprw50498.2020.00461, 2020. a
Nasiri, A., Taheri-Garavand, A., Omid, M., and Carlomagno, G. M.: Intelligent
fault diagnosis of cooling radiator based on deep learning analysis of
infrared thermal images, Appl. Therm. Eng., 163, 114410, https://doi.org/10.1016/j.applthermaleng.2019.114410, 2019. a, b
Ongsulee, P.: Artificial intelligence, machine learning and deep learning, in:
2017 15th International Conference on ICT and Knowledge Engineering
(ICT&KE), Bangkok, Thailand, November 2017, 22–24, 1–6, https://doi.org/10.1109/ICTKE.2017.8259629, 2017. a
Özdemir, E. and Remondino, F.: CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 103–110, https://doi.org/10.5194/isprs-archives-XLII-2-W13-103-2019, 2019. a
Pang, Y., Wang, W., Du, L., Zhang, Z., Liang, X., Li, Y., and Wang, Z.:
Nyström-based spectral clustering using airborne LiDAR point cloud data
for individual tree segmentation, Int. J. Digit. Earth, 14,
1452–1476, 2021. a
Paolanti, M. and Frontoni, E.: Multidisciplinary Pattern Recognition
applications: A review, Computer Science Review, 37, 100276, https://doi.org/10.1016/j.cosrev.2020.100276, 2020. a
Paolanti, M., Pierdicca, R., Martini, M., Di Stefano, F., Morbidoni, C.,
Mancini, A., Malinverni, E. S., Frontoni, E., and Zingaretti, P.: Semantic 3D
Object Maps for Everyday Robotic Retail Inspection, in: International
Conference on Image Analysis and Processing, Trento, Italy, 9–13 September 2019, 263–274, https://doi.org/10.1007/978-3-030-30754-7_27, 2019. a
Pierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., and Zingaretti, P.: DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 893–900, https://doi.org/10.5194/isprs-archives-XLII-2-893-2018, 2018. a
Pierdicca, R., Mameli, M., Malinverni, E. S., Paolanti, M., and Frontoni, E.:
Automatic Generation of Point Cloud Synthetic Dataset for Historical Building
Representation, in: International Conference on Augmented Reality, Virtual
Reality and Computer Graphics, Lecce, Italy, 7–10 September 2019, 203–219, https://doi.org/10.1007/978-3-030-25965-5_16, 2019a. a
Pierdicca, R., Paolanti, M., Felicetti, A., Piccinini, F., and Zingaretti, P.:
Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep
Learning-Based System for Thermal Images, Energies, 13, 6496, https://doi.org/10.3390/en13246496,
2020a. a
Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C.,
Malinverni, E. S., Frontoni, E., and Lingua, A. M.: Point Cloud Semantic
Segmentation Using a Deep Learning Framework for Cultural Heritage, Remote
Sensing, 12, 1005, https://doi.org/10.3390/rs12061005, 2020b. a, b
Qi, C. R., Su, H., Mo, K., and Guibas, L. J.: Pointnet: Deep learning on point
sets for 3d classification and segmentation, in: Proceedings of the IEEE
conference on computer vision and pattern recognition, Honolulu, HI, USA, 21–26 July 2017, 652–660,
https://doi.org/10.1109/cvpr.2017.16, 2017a. a, b, c, d
Qi, C. R., Yi, L., Su, H., and Guibas, L. J.: Pointnet
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.: You only look once:
Unified, real-time object detection, in: Proceedings of the IEEE conference
on computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016, 779–788, https://doi.org/10.1109/cvpr.2016.91, 2016. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., Prabhat, P.: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204, 2019. a
Rossi, L., Ajmar, A., Paolanti, M., and Pierdicca, R.: Vehicle trajectory
prediction and generation using LSTM models and GANs, Plos one, 16,
e0253868, https://doi.org/10.1371/journal.pone.0253868, 2021. a
Sharma, R. C., Hara, K., and Hirayama, H.: A machine learning and
cross-validation approach for the discrimination of vegetation physiognomic
types using satellite based multispectral and multitemporal data,
Scientifica, 2017, 9806479, https://doi.org/10.1155/2017/9806479, 2017. a, b
Shen, Z., Liang, H., Lin, L., Wang, Z., Huang, W., and Yu, J.: Fast Ground
Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process,
Remote Sensing, 13, 3239, https://doi.org/10.3390/rs13163239, 2021. a
Shi, Z. and Pun-Cheng, L. S.: Spatiotemporal data clustering: a survey of
methods, ISPRS Int. Geo-Inf., 8, 112, https://doi.org/10.3390/ijgi8030112, 2019. a
Siddique, A. and Afanasyev, I.: Deep Learning-based Trajectory Estimation of
Vehicles in Crowded and Crossroad Scenarios, in: 2021 28th Conference of Open
Innovations Association (FRUCT), Moskva, Russia, 25–29 January 2021, 413–423, https://doi.org/10.23919/fruct50888.2021.9347580, 2021. a
Signoroni, A., Savardi, M., Baronio, A., and Benini, S.: Deep learning meets
hyperspectral image analysis: a multidisciplinary review, Journal of Imaging,
5, 52, https://doi.org/10.3390/jimaging5050052, 2019. a
Sultana, F., Sufian, A., and Dutta, P.: Evolution of image segmentation using
deep convolutional neural network: A survey, Knowl.-Based Syst., 201–202, p.
106062, https://doi.org/10.1016/j.knosys.2020.106062, 2020. a
Sun, Y., Wang, Y., Liu, Z., Siegel, J., and Sarma, S.: Pointgrow:
Autoregressively learned point cloud generation with self-attention, in: The
IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA, 1–5 March 2020, 61–70, https://doi.org/10.1109/wacv45572.2020.9093430, 2020. a
Tajwar, T., Hossain, S. F., Mobin, O. H., Islam, M., Khan, F. R., and Rahman,
M. M.: Infrared Thermography Based Hotspot Detection Of Photovoltaic Module
using YOLO, in: 2021 IEEE 12th Energy Conversion Congress & Exposition-Asia
(ECCE-Asia), Singapore, 24–27 May 2021, 1542–1547, https://doi.org/10.1109/ecce-asia49820.2021.9478998, 2021. a
Tan, P.-N., Steinbach, M., and Kumar, V.: Introduction to data mining, Pearson Education India, 2nd edn., Addison-Wesley, ISBN-13: 978-0133128901, 2016. a
Toth, C. and Jóźków, G.: Remote sensing platforms and sensors: A
survey, ISPRS J. Photogramm., 115, 22–36,
2016. a
Ullah, I., Yang, F., Khan, R., Liu, L., Yang, H., Gao, B., and Sun, K.:
Predictive maintenance of power substation equipment by infrared thermography
using a machine-learning approach, Energies, 10, 1987, https://doi.org/10.3390/en10121987, 2017. a, b
Ullah, I., Khan, R. U., Yang, F., and Wuttisittikulkij, L.: Deep learning
image-based defect detection in high voltage electrical equipment, Energies,
13, 392, https://doi.org/10.3390/en13020392, 2020. a, b
Vicnesh, J., Oh, S. L., Wei, J. K. E., Ciaccio, E. J., Chua, K. C., Tong, L.,
and Acharya, U. R.: Thoughts concerning the application of thermogram images
for automated diagnosis of dry eye-A review, Infrared Phys. Techn., 106,
p. 103271, https://doi.org/10.1016/j.infrared.2020.103271, 2020. a
Vondrick, C., Pirsiavash, H., and Torralba, A.: Generating videos with scene
dynamics, in: Adv. Neur. In., 29, 613–621, 2016. a
Wang, H., Wang, Y., Zhang, Q., Xiang, S., and Pan, C.: Gated convolutional
neural network for semantic segmentation in high-resolution images, Remote
Sensing, 9, 446, https://doi.org/10.3390/rs9050446, 2017. a
Wang, Z., Xu, Y., Yu, J., Xu, G., Fu, J., and Gu, T.: Instance segmentation of
point cloud captured by RGB-D sensor based on deep learning, Int. J. Comp. Integ. M., 34, 950–963, 2021. a
Weinmann, M., Schmidt, A., Mallet, C., Hinz, S., Rottensteiner, F., and Jutzi, B.: CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 271–278, https://doi.org/10.5194/isprsannals-II-3-W4-271-2015, 2015. a
Xiao, A., Yang, X., Lu, S., Guan, D., and Huang, J.: FPS-Net: A convolutional
fusion network for large-scale LiDAR point cloud segmentation, ISPRS J. Photogramm., 176, 237–249, 2021. a
Xiao, Y., Wu, J., Lin, Z., and Zhao, X.: A deep learning-based multi-model
ensemble method for cancer prediction, Computer Meth. Prog. Bio., 153, 1–9, 2018. a
Xiao, Z., Wang, Y., Fu, K., and Wu, F.: Identifying different transportation
modes from trajectory data using tree-based ensemble classifiers, ISPRS
Int. Geo-Inf., 6, 57, https://doi.org/10.3390/ijgi6020057, 2017. a, b
Xie, Y., Jiaojiao, T., and Zhu, X.: Linking Points With Labels in 3D: A Review
of Point Cloud Semantic Segmentation, IEEE Geosci. Remote S., 8, 38–59, https://doi.org/10.1109/mgrs.2019.2937630 2020. a
Xu, X., Chen, Y., Zhang, J., Chen, Y., Anandhan, P., and Manickam, A.: A novel
approach for scene classification from remote sensing images using deep
learning methods, Eur. J. Remote Sens., 54, 383–395, 2021. a
Yang, M.-D., Tseng, H.-H., Hsu, Y.-C., and Tsai, H. P.: Semantic segmentation
using deep learning with vegetation indices for rice lodging identification
in multi-date UAV visible images, Remote Sensing, 12, 633, https://doi.org/10.3390/rs12040633, 2020. a
Yang, X., Stewart, K., Tang, L., Xie, Z., and Li, Q.: A review of GPS
trajectories classification based on transportation mode, Sensors, 18, 3741, https://doi.org/10.3390/s18113741,
2018a. a
Yang, Y., Yan, J., Guo, J., Kuang, Y., Yin, M., Wang, S., and Ma, C.: Driving
behavior analysis of city buses based on real-time GNSS traces and road
information, Sensors, 21, 687, https://doi.org/10.3390/s21030687, 2021. a
Yang, Z., Jiang, W., Xu, B., Zhu, Q., Jiang, S., and Huang, W.: A convolutional
neural network-based 3D semantic labeling method for ALS point clouds, Remote
Sensing, 9, 936, https://doi.org/10.3390/rs9090936, 2017. a, b
Yan, L., Yoshua, B., and Geoffrey, H.: Deep learning, nature, 521, 436–444,
2015. a
Yuan, X., Shi, J., and Gu, L.: A review of deep learning methods for semantic
segmentation of remote sensing imagery, Expert Syst. Appl.,
169, 114417, https://doi.org/10.1016/j.eswa.2020.114417, 2021. a, b, c
Zang, N., Cao, Y., Wang, Y., Huang, B., Zhang, L., and Mathiopoulos, P. T.:
Land-use Mapping for High Spatial Resolution Remote Sensing Image via Deep
Learning: A Review, IEEE J. Sel. Top. Appl., 14, 5372–5391, https://doi.org/10.1109/jstars.2021.3078631, 2021. a, b
Zhang, C., Atkinson, P. M., George, C., Wen, Z., Diazgranados, M., and Gerard,
F.: Identifying and mapping individual plants in a highly diverse
high-elevation ecosystem using UAV imagery and deep learning, ISPRS J. Photogramm., 169, 280–291, 2020. a
Zhang, L. and Zhang, L.: Deep learning-based classification and reconstruction
of residential scenes from large-scale point clouds, IEEE T. Geosci. Remote, 56, 1887–1897, 2017. a
Zhang, L., Zhang, L., and Du, B.: Deep learning for remote sensing data: A
technical tutorial on the state of the art, IEEE Geoscience and Remote
Sensing Magazine, 4, 22–40, 2016. a
Zhang, L., Xia, H., Liu, Q., Wei, C., Fu, D., and Qiao, Y.: Visual Positioning
in Indoor Environments Using RGB-D Images and Improved Vector of Local
Aggregated Descriptors, ISPRS Int. J. Geo-Inf., 10,
195, https://doi.org/10.3390/ijgi10040195, 2021. a
Zhang, R., Xie, P., Wang, C., Liu, G., and Wan, S.: Classifying transportation
mode and speed from trajectory data via deep multi-scale learning, Comput.
Netw., 162, 106861, https://doi.org/10.1016/j.comnet.2019.106861, 2019. a, b
Zhao, Z.-Q., Zheng, P., Xu, S.-T., and Wu, X.: Object detection with deep
learning: A review, IEEE T. Neur. Net. Lear., 30, 3212–3232, 2019. a
Zheng, H., Zhou, X., He, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., and Tian,
Y.: Early season detection of rice plants using RGB, NIR-GB and multispectral
images from unmanned aerial vehicle (UAV), Comput. Electron. Agr., 169, 105223, https://doi.org/10.1016/j.compag.2020.105223, 2020. a, b
Zhuang, C., Wang, Z., Zhao, H., and Ding, H.: Semantic part segmentation method
based 3D object pose estimation with RGB-D images for bin-picking, Robotics
and Computer-Integrated Manufacturing, 68, 102086, https://doi.org/10.1016/j.rcim.2020.102086, 2021. a
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...
