  This work proposes a multi-task fully convolutional architecture for tree
species mapping in dense forests from sparse and scarce polygon-level
annotations using hyperspectral UAV-borne data. Our model implements a partial
loss function that enables dense tree semantic labeling outcomes from non-dense
training samples, and a distance regression complementary task that enforces
tree crown boundary constraints and substantially improves the model
performance. Our multi-task architecture uses a shared backbone network that
learns common representations for both tasks and two task-specific decoders,
one for the semantic segmentation output and one for the distance map
regression. We report that introducing the complementary task boosts the
semantic segmentation performance compared to the single-task counterpart in up
to 11% reaching an average user's accuracy of 88.63% and an average producer's
accuracy of 88.59%, achieving state-of-art performance for tree species
classification in tropical forests.
