Land cover information is indispensable for advancing the United Nations'
sustainable development goals, and land cover mapping under a more detailed
category system would significantly contribute to economic livelihood tracking
and environmental degradation measurement. However, the substantial difficulty
in acquiring fine-grained training data makes the implementation of this task
particularly challenging. Here, we propose to combine fully labeled source
domain and weakly labeled target domain for weakly supervised domain adaptation
(WSDA). This is beneficial as the utilization of sparse and coarse weak labels
can considerably alleviate the labor required for precise and detailed land
cover annotation. Specifically, we introduce the Prototype-based pseudo-label
Rectification and Expansion (PRE) approach, which leverages the prototypes
(i.e., the class-wise feature centroids) as the bridge to connect sparse labels
and global feature distributions. According to the feature distances to the
prototypes, the confidence of pseudo-labels predicted in the unlabeled regions
of the target domain is assessed. This confidence is then utilized to guide the
dynamic expansion and rectification of pseudo-labels. Based on PRE, we carry
out high categorical resolution land cover mapping for 10 cities in different
regions around the world, severally using PlanetScope, Gaofen-1, and Sentinel-2
satellite images. In the study areas, we achieve cross-sensor, cross-category,
and cross-continent WSDA, with the overall accuracy exceeding 80%. The
promising results indicate that PRE is capable of reducing the dependency of
land cover classification on high-quality annotations, thereby improving label
efficiency. We expect our work to enable global fine-grained land cover
mapping, which in turn promote Earth observation to provide more precise and
thorough information for environmental monitoring.

利用弱监督领域自适应方法，基于原型伪标签修正和扩展，实现高分类分辨率土地覆盖图，并在不同地区的 10 个城市中实现跨传感器、跨类别和跨大陆的地形适应，总体精度超过 80%。

全球高分辨率土地覆盖图的弱监督方法

Global High Categorical Resolution Land Cover Mapping via Weak  Supervision

This paper studies weakly supervised domain adaptation(WSDA) problem, where
we only have access to the source domain with noisy labels, from which we need
to transfer useful information to the unlabeled target domain. Although there
have been a few studies on this problem, most of them only exploit
unidirectional relationships from the source domain to the target domain. In
this paper, we propose a universal paradigm called GearNet to exploit bilateral
relationships between the two domains. Specifically, we take the two domains as
different inputs to train two models alternately, and asymmetrical
Kullback-Leibler loss is used for selectively matching the predictions of the
two models in the same domain. This interactive learning schema enables
implicit label noise canceling and exploits correlations between the source and
target domains. Therefore, our GearNet has the great potential to boost the
performance of a wide range of existing WSDL methods. Comprehensive
experimental results show that the performance of existing methods can be
significantly improved by equipping with our GearNet.

本文提出了 GearNet，一个利用双向关系的通用范例，用于解决仅具有有噪标签源域数据的弱监督域自适应问题，并通过不对称 Kullback-Leibler 损失来选择性地匹配两个模型在同一领域中的预测，从而实现了隐式标签噪声消除，并利用源域和目标域之间的相关性，实现了现有 WSDL 方法性能的大幅提升。