# Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Source: [Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:30:26.199Z Authors: Laura Elena Cu\\'e La Rosa, Camile Sothe, Raul Queiroz Feitosa, Cl\\'audia Maria de Almeida, Marcos Benedito Schimalski, Dario Augusto Borges Oliveira Published: 2021-06-01T21:10:10.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()