Multitask Learning of Height and Semantics From Aerial Images

被引：35

作者：

Carvalho, Marcela ^{[1
]}

Le Saux, Bertrand ^{[1
]}

Trouve-Peloux, Pauline ^{[1
]}

Champagnat, Frederic ^{[1
]}

Almansa, Andres ^{[2
]}

机构：

[1] Univ Paris Saclay, ONERA, DTIS, F-91123 Palaiseau, France

[2] Univ Paris 05, MAP5, F-75006 Paris, France

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2020年 / 17卷 / 08期

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; DEEP; CLASSIFICATION;

D O I：

10.1109/LGRS.2019.2947783

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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

引用

页码：1391 / 1395

页数：5

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