Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/su15064725 http://hdl.handle.net/11449/245606 |
Resumo: | The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth's surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping. |
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Repositório Institucional da UNESP |
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spelling |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Seriesanomaly detectiontime serieslandscape dynamicsframeworkThe synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth's surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.Funda��o de Amparo � Pesquisa do Estado de S�o Paulo (FAPESP)Conselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)Sao Paulo State University (UNESP)Sao Paulo State Univ UNESP, Sci & Technol Inst ICT, BR-12245000 Sao Jose Dos Campos, BrazilSao Paulo State Univ UNESP, Fac Sci & Technol FCT, BR-19060080 Presidente Prudente, BrazilSao Paulo State Univ UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, BrazilSao Paulo State Univ UNESP, Sci & Technol Inst ICT, BR-12245000 Sao Jose Dos Campos, BrazilSao Paulo State Univ UNESP, Fac Sci & Technol FCT, BR-19060080 Presidente Prudente, BrazilSao Paulo State Univ UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, BrazilFAPESP: 2021/01305-6FAPESP: 2021/03328-3CNPq: 316228/2021-4MdpiUniversidade Estadual Paulista (UNESP)Gino, Vinicius L. S. [UNESP]Negri, Rogerio G. [UNESP]Souza, Felipe N. [UNESP]Silva, Erivaldo A. [UNESP]Bressane, Adriano [UNESP]Mendes, Tatiana S. G. [UNESP]Casaca, Wallace [UNESP]2023-07-29T11:59:52Z2023-07-29T11:59:52Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19http://dx.doi.org/10.3390/su15064725Sustainability. Basel: Mdpi, v. 15, n. 6, 19 p., 2023.http://hdl.handle.net/11449/24560610.3390/su15064725WOS:000959505400001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSustainabilityinfo:eu-repo/semantics/openAccess2024-06-18T18:18:17Zoai:repositorio.unesp.br:11449/245606Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:42:53.021981Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
title |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
spellingShingle |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series Gino, Vinicius L. S. [UNESP] anomaly detection time series landscape dynamics framework |
title_short |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
title_full |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
title_fullStr |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
title_full_unstemmed |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
title_sort |
Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series |
author |
Gino, Vinicius L. S. [UNESP] |
author_facet |
Gino, Vinicius L. S. [UNESP] Negri, Rogerio G. [UNESP] Souza, Felipe N. [UNESP] Silva, Erivaldo A. [UNESP] Bressane, Adriano [UNESP] Mendes, Tatiana S. G. [UNESP] Casaca, Wallace [UNESP] |
author_role |
author |
author2 |
Negri, Rogerio G. [UNESP] Souza, Felipe N. [UNESP] Silva, Erivaldo A. [UNESP] Bressane, Adriano [UNESP] Mendes, Tatiana S. G. [UNESP] Casaca, Wallace [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Gino, Vinicius L. S. [UNESP] Negri, Rogerio G. [UNESP] Souza, Felipe N. [UNESP] Silva, Erivaldo A. [UNESP] Bressane, Adriano [UNESP] Mendes, Tatiana S. G. [UNESP] Casaca, Wallace [UNESP] |
dc.subject.por.fl_str_mv |
anomaly detection time series landscape dynamics framework |
topic |
anomaly detection time series landscape dynamics framework |
description |
The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth's surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T11:59:52Z 2023-07-29T11:59:52Z 2023-03-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/su15064725 Sustainability. Basel: Mdpi, v. 15, n. 6, 19 p., 2023. http://hdl.handle.net/11449/245606 10.3390/su15064725 WOS:000959505400001 |
url |
http://dx.doi.org/10.3390/su15064725 http://hdl.handle.net/11449/245606 |
identifier_str_mv |
Sustainability. Basel: Mdpi, v. 15, n. 6, 19 p., 2023. 10.3390/su15064725 WOS:000959505400001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sustainability |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
19 |
dc.publisher.none.fl_str_mv |
Mdpi |
publisher.none.fl_str_mv |
Mdpi |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129453789806592 |