  Information on cultivated crops is relevant for a large number of food
security studies. Different scientific efforts are dedicated to generate this
information from remote sensing images by means of machine learning methods.
Unfortunately, these methods do not account for the spatial-temporal
relationships inherent in remote sensing images. In our paper, we explore the
capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types
from multi-temporal images. In addition, we propose the Intersection Over Union
(IOU) loss function for increasing the overlap between the predicted classes
and ground truth data. The proposed method was applied to identify soybean and
corn from a study area situated in the US corn belt using multi-temporal
Landsat images. The study shows that our method outperforms related methods,
obtaining a Kappa coefficient of 90.8%. We conclude that using the IOU Loss
function provides a superior choice to learn individual crop types.
