@InProceedings{MachadoNoguSant:2021:ScClUs,
               author = "Machado, Gabriel Lucas Silva and Nogueira, Keiller and dos Santos, 
                         Jefersson Alex",
          affiliation = "{Universidade Federal de Minas Gerais} and {University of 
                         Stirling} and {Universidade Federal de Minas Gerais}",
                title = "Scene classification using a combination of aerial and ground 
                         images",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "deep learning, machine learning, remote sensing, image 
                         classification, multi-modal machine learning, metric learning, 
                         cross-view matching.",
             abstract = "lt is undeniable that aerial images can provide useful information 
                         for a large variety of tasks, such as disaster relief, and urban 
                         planning. But, since these images only see the Earth from one 
                         point of view, some applications may benefit from complementary 
                         information provided by other perspective views of the scene, such 
                         as ground-level images. Despite a large number of public image 
                         repositories for both georeferenced photos and aerial images (such 
                         as Google Maps, and Street View), there is a lack of public 
                         datasets that allow studies that exploit the complementarity of 
                         aerial+ground imagery. Given this, we present two new publicly 
                         available datasets named AiRound and CV-BrCT. Using both, we 
                         tackled the scene classification task in 2 different scenarios. 
                         The first one has a fully-paired image set, while the second has 
                         missing samples. In both situations, we used deep learning and 
                         feature fusion algorithms. To handle missing samples, we proposed 
                         a content-based image retrieval framework.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CTA2S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CTA2S",
           targetfile = "WTD_Gabriel.pdf",
        urlaccessdate = "2024, Apr. 28"
}