期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 17, 期 8, 页码 1391-1395出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2947783
关键词
Semantics; Task analysis; Training; Estimation; Predictive models; Decoding; Land surface; Aerial imagery; deep learning; multitask learning; neural networks; semantic segmentation; single view depth estimation
Aerial or satellite imagery is a great source for land surface analysis, which might yield land-use maps or elevation models. In this letter, we present a neural network framework for learning semantics and local height together. We show how this joint multitask learning benefits to each task on the large data set of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map that allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images
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