AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest
In this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the cloud processing capabilities of the Google Eart...
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| Format: | Article |
| Language: | English |
| Published: |
Universitat Politécnica de Valencia
2022-01-01
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| Series: | Revista de Teledetección |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/raet/article/view/15710 |
| _version_ | 1818971777505689600 |
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| author | Jairo J. Pinto-Hidalgo Jorge A. Silva-Centeno |
| author_facet | Jairo J. Pinto-Hidalgo Jorge A. Silva-Centeno |
| author_sort | Jairo J. Pinto-Hidalgo |
| collection | DOAJ |
| description | In this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the cloud processing capabilities of the Google Earth Engine platform. For this, a dataset consisting of 6 classes with a total of 30,000 labelled and geo-referenced 13-band multispectral images was generated, which is used to feed advanced Geospatial Artificial Intelligence models (deep convolutional neural networks) specialised in image classification tasks. With the dataset presented in this paper it is possible to obtain a classification overall accuracy of 96.56%. It is also demonstrated how the results obtained can be used in real applications to support decision making aimed at preventing Transnational Environmental Crimes in the Amazon rainforest. The AmazonCRIME Dataset is made publicly available in the repository: https://github.com/jp-geoAI/AmazonCRIME.git. |
| first_indexed | 2024-12-20T14:57:46Z |
| format | Article |
| id | doaj.art-47397fdc89d34383a41832ddd9ed2f9c |
| institution | Directory Open Access Journal |
| issn | 1133-0953 1988-8740 |
| language | English |
| last_indexed | 2024-12-20T14:57:46Z |
| publishDate | 2022-01-01 |
| publisher | Universitat Politécnica de Valencia |
| record_format | Article |
| series | Revista de Teledetección |
| spelling | doaj.art-47397fdc89d34383a41832ddd9ed2f9c2022-12-21T19:36:45ZengUniversitat Politécnica de ValenciaRevista de Teledetección1133-09531988-87402022-01-0105912110.4995/raet.2022.157109241AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon RainforestJairo J. Pinto-Hidalgo0Jorge A. Silva-Centeno1Universidade Federal do ParanáUniversidade Federal do ParanáIn this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the cloud processing capabilities of the Google Earth Engine platform. For this, a dataset consisting of 6 classes with a total of 30,000 labelled and geo-referenced 13-band multispectral images was generated, which is used to feed advanced Geospatial Artificial Intelligence models (deep convolutional neural networks) specialised in image classification tasks. With the dataset presented in this paper it is possible to obtain a classification overall accuracy of 96.56%. It is also demonstrated how the results obtained can be used in real applications to support decision making aimed at preventing Transnational Environmental Crimes in the Amazon rainforest. The AmazonCRIME Dataset is made publicly available in the repository: https://github.com/jp-geoAI/AmazonCRIME.git.https://polipapers.upv.es/index.php/raet/article/view/15710crímenes ambientales trasnacionalesselva amazónicasentinel-2inteligencia geoespacialinteligencia artificial geoespacial |
| spellingShingle | Jairo J. Pinto-Hidalgo Jorge A. Silva-Centeno AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest Revista de Teledetección crímenes ambientales trasnacionales selva amazónica sentinel-2 inteligencia geoespacial inteligencia artificial geoespacial |
| title | AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest |
| title_full | AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest |
| title_fullStr | AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest |
| title_full_unstemmed | AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest |
| title_short | AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest |
| title_sort | amazoncrime a geospatial artificial intelligence dataset and benchmark for the classification of potential areas linked to transnational environmental crimes in the amazon rainforest |
| topic | crímenes ambientales trasnacionales selva amazónica sentinel-2 inteligencia geoespacial inteligencia artificial geoespacial |
| url | https://polipapers.upv.es/index.php/raet/article/view/15710 |
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