Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine

Florencia Hastings; Ignacio Fuentes; Mario Perez-Bidegain; Rafael Navas; Angela Gorgoglione

doi:10.1007/978-3-030-58811-3_52

Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine

Florencia Hastings, Ignacio Fuentes, Mario Perez-Bidegain, Rafael Navas, Angela Gorgoglione

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990–2018.

Idioma original	Inglés
Título de la publicación alojada	Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
Editores	Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	721-736
Número de páginas	16
ISBN (versión impresa)	9783030588106
DOI	https://doi.org/10.1007/978-3-030-58811-3_52
Estado	Publicada - 2020
Publicado de forma externa	Sí
Evento	20th International Conference on Computational Science and Its Applications, ICCSA 2020 - Cagliari, Italia Duración: 1 jul. 2020 → 4 jul. 2020

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12252 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	20th International Conference on Computational Science and Its Applications, ICCSA 2020
País/Territorio	Italia
Ciudad	Cagliari
Período	1/07/20 → 4/07/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-58811-3_52

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Hastings, F., Fuentes, I., Perez-Bidegain, M., Navas, R., & Gorgoglione, A. (2020). Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine. En O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (Eds.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings (pp. 721-736). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12252 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58811-3_52

Hastings, Florencia ; Fuentes, Ignacio ; Perez-Bidegain, Mario et al. / Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine. Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. editor / Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Chiara Garau ; Ivan Blecic ; David Taniar ; Bernady O. Apduhan ; Ana Maria A.C. Rocha ; Eufemia Tarantino ; Carmelo Maria Torre ; Yeliz Karaca. Springer Science and Business Media Deutschland GmbH, 2020. pp. 721-736 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0ff9ee26b94a4d78ab4eb15cbb239199,

title = "Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine",

abstract = "Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990–2018.",

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author = "Florencia Hastings and Ignacio Fuentes and Mario Perez-Bidegain and Rafael Navas and Angela Gorgoglione",

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year = "2020",

doi = "10.1007/978-3-030-58811-3_52",

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isbn = "9783030588106",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Blecic and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria A.C.} and Eufemia Tarantino and Torre, {Carmelo Maria} and Yeliz Karaca",

booktitle = "Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings",

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Hastings, F, Fuentes, I, Perez-Bidegain, M, Navas, R & Gorgoglione, A 2020, Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine. En O Gervasi, B Murgante, S Misra, C Garau, I Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino, CM Torre & Y Karaca (eds.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12252 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 721-736, 20th International Conference on Computational Science and Its Applications, ICCSA 2020, Cagliari, Italia, 1/07/20. https://doi.org/10.1007/978-3-030-58811-3_52

Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine. / Hastings, Florencia; Fuentes, Ignacio; Perez-Bidegain, Mario et al.
Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. ed. / Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Chiara Garau; Ivan Blecic; David Taniar; Bernady O. Apduhan; Ana Maria A.C. Rocha; Eufemia Tarantino; Carmelo Maria Torre; Yeliz Karaca. Springer Science and Business Media Deutschland GmbH, 2020. p. 721-736 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12252 LNCS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine

AU - Hastings, Florencia

AU - Fuentes, Ignacio

AU - Perez-Bidegain, Mario

AU - Navas, Rafael

AU - Gorgoglione, Angela

PY - 2020

Y1 - 2020

N2 - Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990–2018.

AB - Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990–2018.

KW - Agricultural region

KW - Google earth engine

KW - Land-cover map

KW - Supervised classification

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U2 - 10.1007/978-3-030-58811-3_52

DO - 10.1007/978-3-030-58811-3_52

M3 - Conference contribution

AN - SCOPUS:85092219123

SN - 9783030588106

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 721

EP - 736

BT - Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Misra, Sanjay

A2 - Garau, Chiara

A2 - Blecic, Ivan

A2 - Taniar, David

A2 - Apduhan, Bernady O.

A2 - Rocha, Ana Maria A.C.

A2 - Tarantino, Eufemia

A2 - Torre, Carmelo Maria

A2 - Karaca, Yeliz

PB - Springer Science and Business Media Deutschland GmbH

T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020

Y2 - 1 July 2020 through 4 July 2020

ER -

Hastings F, Fuentes I, Perez-Bidegain M, Navas R, Gorgoglione A. Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine. En Gervasi O, Murgante B, Misra S, Garau C, Blecic I, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, Karaca Y, editores, Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 721-736. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-58811-3_52

Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine

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