[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119919-en":3,"doc-seo-119919-105":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},119919,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Automatic Generation of a Portuguese Land Cover Map with Machine Learning","Automatic land cover mapping is addressed through machine learning on satellite imagery, leveraging the growing volume and quality of Earth observation data from programs such as Copernicus. The work develops and evaluates models for segmenting and classifying Sentinel-2 spectral bands using vegetation indices and different land-cover class sets. Two classification strategies are compared: an object-oriented approach with U-Net and pixel-oriented models using random forest and support vector machine. Accuracies range from 68.6% to 94.75%, increasing with fewer classes and reaching 92.37% for eight classes, improving over related bibliography.","Automatic Generation of a Portuguese Land Cover Map with Machine Learning  \nAntonio Esteves 1 and Nuno Valente2  \n1 ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal,  \n[esteves@di.uminho.pt](esteves@di.uminho.pt),  \nWeb: [https://www4.di.uminho.pt/](https://www4.di.uminho.pt/~jno/sitedi/nm   1728.html)[~](https://www4.di.uminho.pt/~jno/sitedi/nm   1728.html)[jno/sitedi/nm ](https://www4.di.uminho.pt/~jno/sitedi/nm   1728.html)[ ](https://www4.di.uminho.pt/~jno/sitedi/nm   1728.html)[ 1728.html](https://www4.di.uminho.pt/~jno/sitedi/nm   1728.html)  \n2 University of Minho, Braga, Portugal  \n[a81986@alunos.uminho.pt](a81986@alunos.uminho.pt)  \nAbstract. The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segment and classify satellite images, to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68 .6% to 94 .75%, depending strongly on the number of classes into which the land cover is classified. The result of 94 .75% was obtained when classifying the land cover only into 5 classes. However, a very interesting accuracy of 92 .37% was achieved by the model when trained to classify 8 classes. These results are superior to those reported in the related bibliography.  \nKeywords: machine learning, deep learning, remote sensing, land cover map  \n1 Introduction  \nRecent scientific advances in remote sensing (RS) have resulted in easy access to satellite imagery. Among the countless applications of satellite imagery, the present work highlights the creation of land use land cover (LULC) maps. LULC refers to human constructions and natural features of the earth’s surface. LULCare used in various fields of study, such as urban planning, natural resource  \n2 Antonio Esteves et al.  \nmanagement, carbon circulation, epidemiology, and climate change. Using the Portuguese territory as a case study, this work intends to apply machine learning (ML) techniques to reproduce the results of the Corine Land Cover (CLC) European Union project.  \nOne of the expected outcomes for this project was to train a model capable of successfully classifying satellite imagery into a LULC map. In a LULC classification task, the term of comparison is an overall accuracy of 85% and where none of the classes have an accuracy of less than 70%[1] . If a trained model performs better than this threshold, it will be considered successful.  \nRS tasks, such as LULC classification, exhibit some unique specificities. Although there are huge amounts of satellite imagery, most of this data is not classified or it is outdated, therefore not being useful for training deep learning (DL) models [2] . The seasons introduce variability, and hence complexity, especially due to changes in phenology [3] . However this variability can be captured by DL methods, provided it is reproduced in the training data [4] .  \nSeveral techniques can be implemented for LULC classification, however, these can be divided into two categories: ","cbCaidHhMLAAOGut","https://ap.wps.com/l/cbCaidHhMLAAOGut","pdf",1595508,1,23,"English","en",105,"# Introduction\n## Land use land cover mapping and success criteria\n## Remote sensing task characteristics\n## Pixel-oriented vs object-oriented classification\n# Related Work\n## Machine learning models for land cover classification\n## Datasets, land cover classes, and spectral indices","[{\"question\":\"What is the main goal of the study on Portuguese land cover maps?\",\"answer\":\"The study aims to create machine learning models that accurately segment and classify satellite images to automatically generate a land cover map for Portugal.\"},{\"question\":\"Which Sentinel-2 data sources and features are used in the experiments?\",\"answer\":\"Experiments use Sentinel-2 spectral bands, vegetation indices, and multiple sets of land cover classes.\"},{\"question\":\"How do the evaluated models differ in their image classification approach?\",\"answer\":\"One approach is object-oriented using a U-Net architecture, while the other is pixel-oriented using random forest and support vector machine.\"}]","Automatic Generation of a Portuguese Land Cover Map with Machine Learning | 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is the main goal of the study on Portuguese land cover maps?","Question",{"text":75,"@type":76},"The study aims to create machine learning models that accurately segment and classify satellite images to automatically generate a land cover map for Portugal.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which Sentinel-2 data sources and features are used in the experiments?",{"text":80,"@type":76},"Experiments use Sentinel-2 spectral bands, vegetation indices, and multiple sets of land cover classes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the evaluated models differ in their image classification approach?",{"text":84,"@type":76},"One approach is object-oriented using a U-Net architecture, while the other is pixel-oriented using random forest and support vector 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