Center Pivot Classification With Deep Residual U-Net

Center pivots are a modem irrigation technique mainly applied in precision agriculture, once it has high efficiency in water consumption and low labor workers when compared to traditional irrigation methods. Knowing their location is valuable since monitoring, evaluating, and estimating essential features in the lands becomes easier, remote sensing is a robust tool to act upon this kind of problem. To identify center pivots, we used a deep residual U-Net with a pixel comparison at image reconstruction to enhance results. We obtained a validation loss of 0.19, which adds up with pixel comparison. Results were satisfactory, with 2070 correct identifications from a total of 2109 center pivots (98.15%). Future studies to improve these results would require more data in different places and seasons.