AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest

[EN] 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.[ES] En este artículo es abordado el desafío de detectar áreas vinculadas con crímenes ambientales trasnacionales en la selva amazónica usando datos de Inteligencia Geoespacial, imágenes de libre acceso Sentinel-2 proporcionadas por el programa Copernicus, así como también las capacidades de procesamiento en la nube de la plataforma Google Earth Engine. Para esto, se generó un conjunto de datos que consta de 6 clases con un total de 30.000 imágenes multiespectrales de 13 bandas, etiquetadas y georreferenciadas que es usado para alimentar modelos avanzados de Inteligencia Artificial Geoespacial (redes neuronales convolucionales profundas) especializados en las tareas de clasificación de imágenes. Con el conjunto de datos presentado en este artículo es posible obtener una exactitud global (overall accuracy) de clasificación de 96.56%. Es también demostrado cómo los resultados obtenidos se pueden utilizar en aplicaciones reales para apoyar la toma de decisiones destinadas a prevenir los Crímenes Ambientales Transnacionales en la selva Amazónica. El Conjunto de datos AmazonCRIME se coloca a disposición del público en el repositorio: https://github.com/jp-geoAI/AmazonCRIME.git.Agradecemos al Programa de Posgraduación en Ciencias Geodésicas de la Universidad Federal de Paraná y el apoyo financiero al Consejo Nacional de Desarrollo Científico y Tecnológico de Brasil (CNPq) (190032/2017-0).Pinto-Hidalgo, JJ.; Silva-Centeno, JA. (2022). AmazonCRIME: un conjunto de datos y punto de referencia de Inteligencia Artificial Geoespacial para la clasificación de áreas potenciales vinculadas a Crímenes Ambientales Transnacionales en la Selva Amazónica. Revista de Teledetección. 0(59):1-21. https://doi.org/10.4995/raet.2022.15710121059Abdani, S.R., & Zulkifley, M.A. 2019. 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