@FilmorBroadcast{Rodrigues:2021:AuDeCe,
                 cast = "Instituto Nacional de Pesquisas Espaciais (INPE)",
         datereleased = "13-17 set. 2021",
             director = "Rodrigues, Marcos Lima",
                  ibi = "8JMKD3MGPDW34P/4627CFL",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/4627CFL",
       seriesdirector = "Santos, Rafael Duarte Coelho dos and Queiroz, Gilberto Ribeiro de 
                         and Shiguemori, Elcio Hideiti",
          seriestitle = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE, 21 
                         (WORCAP)",
             synopsis = "Water management is a key field to support life and economic 
                         activity nowadays. The greatly increased mechanization of 
                         agriculture, mainly through center pivot irrigation systems, 
                         represents a big challenge to control this resource. Irrigated 
                         agriculture makes up the large majority of consumptive water use, 
                         therefore it is important to identify and quantify these systems. 
                         Currently, with 8.2×10^6 ha, Brazil is among the 10 largest 
                         countries in irrigation areas in the world. In this study, a 
                         combined Computer Vision and Machine Learning approach is proposed 
                         for the identification of center pivots in remote sensing images. 
                         The methodology is based on Circular Hough Transform (CHT) and 
                         Balanced Random Forest (BRF) classifier using vegetation indices 
                         NDVI/SAVI generated from Landsat 8 images and Land Use and Land 
                         Cover (LULC) data provided by project MapBiomas. The candidate's 
                         circles of pivots identified on images are filtered based on 
                         vegetation behavior and shape characteristics of these areas. Our 
                         approach was able to detect 7358 pivots, reaching 83.86% of Recall 
                         for 52 tiles analyzed overall Brazil compared with mapping done by 
                         the Brazilian National Water and Sanitation Agency (ANA). In some 
                         tiles, the Recall reaches up to 100%. The BRF model trained over 
                         spectral and geometric features allowed identify pivots, where 
                         regions with great amplitude of vegetation indices highlight areas 
                         with agricultural activity to the detriment of areas of native 
                         vegetation, and also characteristics of the shapes from targets 
                         based on their delimitation through the High Pass Filter Sharr. 
                         The good accuracy achieved shows the robustness of the method to 
                         detect pivots on a large spatial and temporal scale.",
           targetfile = "Rodrigues Automatic-1.mp4",
                title = "Automatic detection of center pivots using circular hough 
                         transform, balanced random forest and land use and land cover 
                         data",
         yearreleased = "2021",
        urlaccessdate = "12 maio 2024"
}