Cloud analysis is a critical component of weather and climate science,
impacting various sectors like disaster management. However, achieving
fine-grained cloud analysis, such as cloud segmentation, in remote sensing
remains challenging due to the inherent difficulties in obtaining accurate
labels, leading to significant labeling errors in training data. Existing
methods often assume the availability of reliable segmentation annotations,
limiting their overall performance. To address this inherent limitation, we
introduce an innovative model-agnostic Cloud Adaptive-Labeling (CAL) approach,
which operates iteratively to enhance the quality of training data annotations
and consequently improve the performance of the learned model. Our methodology
commences by training a cloud segmentation model using the original
annotations. Subsequently, it introduces a trainable pixel intensity threshold
for adaptively labeling the cloud training images on the fly. The newly
generated labels are then employed to fine-tune the model. Extensive
experiments conducted on multiple standard cloud segmentation benchmarks
demonstrate the effectiveness of our approach in significantly boosting the
performance of existing segmentation models. Our CAL method establishes new
state-of-the-art results when compared to a wide array of existing
alternatives.

通过创新的模型无关的云自适应标注（CAL）方法来提高云分割模型的性能，该方法使用原始标注进行训练，然后利用可调节的像素强度阈值对云训练图像进行自适应标注，最终通过此方法显著提高现有分割模型的性能。

增强遥感云理解的自适应标记

Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding

Sky-image-based solar forecasting using deep learning has been recognized as
a promising approach in reducing the uncertainty in solar power generation.
However, one of the biggest challenges is the lack of massive and diversified
sky image samples. In this study, we present a comprehensive survey of
open-source ground-based sky image datasets for very short-term solar
forecasting (i.e., forecasting horizon less than 30 minutes), as well as
related research areas which can potentially help improve solar forecasting
methods, including cloud segmentation, cloud classification and cloud motion
prediction. We first identify 72 open-source sky image datasets that satisfy
the needs of machine/deep learning. Then a database of information about
various aspects of the identified datasets is constructed. To evaluate each
surveyed datasets, we further develop a multi-criteria ranking system based on
8 dimensions of the datasets which could have important impacts on usage of the
data. Finally, we provide insights on the usage of these datasets for different
applications. We hope this paper can provide an overview for researchers who
are looking for datasets for very short-term solar forecasting and related
areas.

该研究对短期（预测时间少于 30 分钟）太阳能预测的数据集进行了综合调查，并开发了基于八个维度的多标准排名系统，以评估调查的数据集。同时，介绍了与阳光能预测相关的其他研究领域和使用这些数据集的不同应用。

用于极短期太阳能预测、云分析和建模的开源地基天空图像数据集：综述

Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey

We propose a novel deep learning model named ACLNet, for cloud segmentation
from ground images. ACLNet uses both deep neural network and machine learning
(ML) algorithm to extract complementary features. Specifically, it uses
EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to
learn at multiple receptive fields, and "global attention module" (GAM) to
extract finegrained details from the image. ACLNet also uses k-means clustering
to extract cloud boundaries more precisely. ACLNet is effective for both
daytime and nighttime images. It provides lower error rate, higher recall and
higher F1-score than state-of-art cloud segmentation models. The source-code of
ACLNet is available here: this https URL.

ACLNet 是一种有效的云分割模型，利用深度神经网络和机器学习算法提取互补特征，使用 EfficientNet-B0 作为骨干网络，ASPP 学习多个感受野，GAM 提取图像中的细节，并使用 k-means 聚类更精确地提取云边界。其错误率更低，召回率和 F1 - 分数更高，适用于白天和黑夜图像。