Detection of Opencast Mining Sites from Multispectral Satellite Images

Jorge Mario Londoño-Peláez

doi:10.1007/978-981-16-4884-7_13

Detection of Opencast Mining Sites from Multispectral Satellite Images

Producción científica: Capítulo del libro/informe/acta de congreso › Ponencia publicada en las memorias del evento con ISBN › revisión exhaustiva

Resumen

In this work, we consider the problem of detecting unauthorized opencast mining activities from satellite imagery, which is important for the adverse effect they cause in the environment and the regions where they occur. Detection by traditional methods is cumbersome and costly. For this reason, we propose a solution leveraging machine learning techniques using multispectral satellite imagery. We implemented these techniques and evaluated their performance using real-world examples and considering various important performance metrics. The results show that these techniques provide an accurate detection system and do not require costly computational resources.

Idioma original	Inglés
Título de la publicación alojada	Developments and Advances in Defense and Security - Proceedings of MICRADS 2021
Editores	Álvaro Rocha, Carlos Hernan Fajardo-Toro, José María Riola Rodríguez
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	161-171
Número de páginas	11
ISBN (versión impresa)	9789811648830
DOI	https://doi.org/10.1007/978-981-16-4884-7_13
Estado	Publicada - 2022
Evento	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 - Cartagena, Colombia Duración: 18 ago. 2021 → 20 ago. 2021

Serie de la publicación

Nombre	Smart Innovation, Systems and Technologies
Volumen	255
ISSN (versión impresa)	2190-3018
ISSN (versión digital)	2190-3026

Conferencia

Conferencia	Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021
País/Territorio	Colombia
Ciudad	Cartagena
Período	18/08/21 → 20/08/21

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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10.1007/978-981-16-4884-7_13

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Londoño-Peláez, J. M. (2022). Detection of Opencast Mining Sites from Multispectral Satellite Images. En Á. Rocha, C. H. Fajardo-Toro, & J. M. Riola Rodríguez (Eds.), Developments and Advances in Defense and Security - Proceedings of MICRADS 2021 (pp. 161-171). (Smart Innovation, Systems and Technologies; Vol. 255). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-4884-7_13

Londoño-Peláez, Jorge Mario. / Detection of Opencast Mining Sites from Multispectral Satellite Images. Developments and Advances in Defense and Security - Proceedings of MICRADS 2021. editor / Álvaro Rocha ; Carlos Hernan Fajardo-Toro ; José María Riola Rodríguez. Springer Science and Business Media Deutschland GmbH, 2022. pp. 161-171 (Smart Innovation, Systems and Technologies).

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title = "Detection of Opencast Mining Sites from Multispectral Satellite Images",

abstract = "In this work, we consider the problem of detecting unauthorized opencast mining activities from satellite imagery, which is important for the adverse effect they cause in the environment and the regions where they occur. Detection by traditional methods is cumbersome and costly. For this reason, we propose a solution leveraging machine learning techniques using multispectral satellite imagery. We implemented these techniques and evaluated their performance using real-world examples and considering various important performance metrics. The results show that these techniques provide an accurate detection system and do not require costly computational resources.",

keywords = "Full convolutional neural network, Opencast mining, Semantic segmentation, Support vector machines",

author = "Londo{\~n}o-Pel{\'a}ez, {Jorge Mario}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference date: 18-08-2021 Through 20-08-2021",

year = "2022",

doi = "10.1007/978-981-16-4884-7_13",

language = "Ingl{\'e}s",

isbn = "9789811648830",

series = "Smart Innovation, Systems and Technologies",

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

pages = "161--171",

editor = "{\'A}lvaro Rocha and Fajardo-Toro, {Carlos Hernan} and {Riola Rodr{\'i}guez}, {Jos{\'e} Mar{\'i}a}",

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Londoño-Peláez, JM 2022, Detection of Opencast Mining Sites from Multispectral Satellite Images. En Á Rocha, CH Fajardo-Toro & JM Riola Rodríguez (eds.), Developments and Advances in Defense and Security - Proceedings of MICRADS 2021. Smart Innovation, Systems and Technologies, vol. 255, Springer Science and Business Media Deutschland GmbH, pp. 161-171, Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021, Cartagena, Colombia, 18/08/21. https://doi.org/10.1007/978-981-16-4884-7_13

Detection of Opencast Mining Sites from Multispectral Satellite Images. / Londoño-Peláez, Jorge Mario.
Developments and Advances in Defense and Security - Proceedings of MICRADS 2021. ed. / Álvaro Rocha; Carlos Hernan Fajardo-Toro; José María Riola Rodríguez. Springer Science and Business Media Deutschland GmbH, 2022. p. 161-171 (Smart Innovation, Systems and Technologies; Vol. 255).

Producción científica: Capítulo del libro/informe/acta de congreso › Ponencia publicada en las memorias del evento con ISBN › revisión exhaustiva

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T1 - Detection of Opencast Mining Sites from Multispectral Satellite Images

AU - Londoño-Peláez, Jorge Mario

PY - 2022

Y1 - 2022

N2 - In this work, we consider the problem of detecting unauthorized opencast mining activities from satellite imagery, which is important for the adverse effect they cause in the environment and the regions where they occur. Detection by traditional methods is cumbersome and costly. For this reason, we propose a solution leveraging machine learning techniques using multispectral satellite imagery. We implemented these techniques and evaluated their performance using real-world examples and considering various important performance metrics. The results show that these techniques provide an accurate detection system and do not require costly computational resources.

AB - In this work, we consider the problem of detecting unauthorized opencast mining activities from satellite imagery, which is important for the adverse effect they cause in the environment and the regions where they occur. Detection by traditional methods is cumbersome and costly. For this reason, we propose a solution leveraging machine learning techniques using multispectral satellite imagery. We implemented these techniques and evaluated their performance using real-world examples and considering various important performance metrics. The results show that these techniques provide an accurate detection system and do not require costly computational resources.

KW - Full convolutional neural network

KW - Opencast mining

KW - Semantic segmentation

KW - Support vector machines

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DO - 10.1007/978-981-16-4884-7_13

M3 - Ponencia publicada en las memorias del evento con ISBN

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BT - Developments and Advances in Defense and Security - Proceedings of MICRADS 2021

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Londoño-Peláez JM. Detection of Opencast Mining Sites from Multispectral Satellite Images. En Rocha Á, Fajardo-Toro CH, Riola Rodríguez JM, editores, Developments and Advances in Defense and Security - Proceedings of MICRADS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 161-171. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-16-4884-7_13

Detection of Opencast Mining Sites from Multispectral Satellite Images

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