[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123518-en":3,"doc-seo-123518-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123518,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Wetland Classification Using Machine Learning Models in the Brazilian Pantanal - Abstract","The Pantanal is the largest tropical wetland region, spanning Bolivia, Brazil, and Paraguay, with several areas protected under Ramsar as well as UNESCO Biosphere Reserves and World Heritage Sites. The study aims to classify surface water bodies as wetlands using machine learning via clustering. Sentinel-2A mosaics processed in Google Earth Engine provide a Normalized Difference Vegetation Index from which an empirical threshold segments water bodies. Clustering is then performed on morphological characteristics of each segmented object. Results indicate unsupervised learning can categorize water bodies effectively.","Wetland Classification Using Machine Learning Models in  \nthe Brazilian Pantanal  \nClassificação de áreas úmidas usando modelos de Aprendizado de Máquina no Pantanal Brasileiro  \nNatalia Verónica Revollo*, Edinéia Aparecida dos Santos Galvanin**, Carlos Enrique Berger***, Verónica Gil****, Sandra  \nMara Alves da Silva Neves*****  \n* Departamento de Ingeniería Eléctrica y de Computadoras, Universidad Nacional del Sur, Argentina  \n** Departamento de Geografia e Planejamento/Ourinhos, Universidade Estadual Paulista, [edineia.galvanin@unesp.br](edineia.galvanin@unesp.br)  \n*** Universidad Provincial del Sudoeste, Buenos Aires, [carlos.berger@upso.edu.ar](carlos.berger@upso.edu.ar)  \n**** Departamento de Geografía y Turismo, Universidad Nacional del Sur-CONICET, [verogil@uns.edu.ar](verogil@uns.edu.ar)  \n***** Faculdade de Ciências Humanas/Geografia, Universidade do Estado de Mato Grosso, [ssneves@unemat.br](ssneves@unemat.br)  \n[https://doi.org/10.5380/raega.v63i2.100193](https://doi.org/10.5380/raega.v63i2.100193)  \nAbstract  \nThe Pantanal is the largest tropical wetland in the world and covers the countries of Bolivia, Brazil, and Paraguay. Some regions within the Pantanal are part of the Convention on Wetlands or are recognized as Biosphere Reserves and World Heritage Sites by UNESCO. This work's objective is to classify surface water bodies into wetlands using machine learning techniques, through clustering techniques. Initially, the (Normalized Difference Vegetation Index) was used from a mosaic of images covering the study area from the Sentinel 2-A satellite processed on the Google Earth Engine platform. From this index, a threshold is established empirically, and the water bodies are segmented. The clustering technique is then applied to the morphological characteristics of each segmented object. The results obtained show that it is possible to categorize water bodies with unsupervised learning techniques.  \nKeywords:  \nRemote sensing, Artificial intelligence, Image processing, Water bodies.  \nResumo  \nO Pantanal é a maior área úmida tropical do mundo e abrange os países da Bolívia, Brasil e Paraguai. Existem regiões dentro do Pantanal que estão aderidas ao tratado da Convenção de áreas úmidas ou que são reconhecidas como Reserva da Biosfera e Patrimônio da Humanidade pela UNESCO. O objetivo deste trabalho é a classificação de corpos d’água superficiais em áreas úmidas por meio do uso de técnicas de aprendizado de máquina, em particular técnicas de agrupamento. Inicialmente, utilizou-se o índice Índice de Vegetação por Diferença Normalizada a partir de um mosaico de imagens que cobre a área de estudo provenientes do satélite Sentinel 2-A processadas na plataforma Google Earth Engine. A partir deste índice, estabeleceu-se um limiar de maneira empírica para segmentar os corpos d’água. Em seguida, aplicou-se a técnica de agrupamento às  \ncaracterísticas morfológicas de cada objeto segmentado. Os resultados obtidos mostram que é possível a categorização de corpos d’água com técnicas de aprendizado não supervisionado.  \nPalavras-chave:  \nSensoriamento remoto, Inteligência artificial, Processamento de imagens, Corpos d’água.  \nI. INTRODUCTION  \nWetlands constitute approximately 6% of our planet surface that are permanently or intermittently flooded. They are vital for the human survival and a source of water and primary productivity, the habitat of several plant and animal species. Despite its’ global importance, half of the Earth’s wetlands have disappeared. Currently, there is a Wetlands Convention Treaty for the conservation and rational use of these areas and its’resources. This treaty covers 2,513 areas of international importance, covering an area of 257,254,185 ha (Ramsar, 2024) . The Pantanal is the world’s largest tropical wetland region, and encompasses parts of Bolivia, Brazil and Paraguay. Some sections within the Pantanal joined the Wetlands Convention Treaty or are considered as a Biosphere Reserve and World Herita","cbCaipdYziqGdwiV","https://ap.wps.com/l/cbCaipdYziqGdwiV","pdf",1252922,1,16,"English","en",105,"# I. Introduction\n## Wetlands importance and conservation context\n## Role of satellite imagery and AI/ML\n## Need for unsupervised learning and clustering\n## Study objective for Pantanal water bodies","[{\"question\":\"What is the main objective of the study on the Brazilian Pantanal?\",\"answer\":\"To classify surface water bodies into wetlands using machine learning techniques, specifically clustering-based unsupervised methods.\"},{\"question\":\"How are water bodies segmented before applying clustering?\",\"answer\":\"Sentinel-2A mosaics are used in Google Earth Engine to compute the Normalized Difference Vegetation Index, then an empirically chosen threshold segments the water bodies.\"},{\"question\":\"Why does the study emphasize unsupervised machine learning?\",\"answer\":\"Because analyzing large areas makes expert manual labeling impractical, so unsupervised clustering helps identify spatial patterns without labels.\"}]","Wetland Classification Using Machine Learning Models in the Brazilian Pantanal - Abstract | PDF",1785817050,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"wetland-classification-using-machine-learning-models-in-the-brazilian-pantanal-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/wetland-classification-using-machine-learning-models-in-the-brazilian-pantanal-abstract/123518/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study on the Brazilian Pantanal?","Question",{"text":75,"@type":76},"To classify surface water bodies into wetlands using machine learning techniques, specifically clustering-based unsupervised methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are water bodies segmented before applying clustering?",{"text":80,"@type":76},"Sentinel-2A mosaics are used in Google Earth Engine to compute the Normalized Difference Vegetation Index, then an empirically chosen threshold segments the water bodies.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the study emphasize unsupervised machine learning?",{"text":84,"@type":76},"Because analyzing large areas makes expert manual labeling impractical, so unsupervised clustering helps identify spatial patterns without labels.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]