In this paper, we introduce a novel method designed to enhance label
efficiency in satellite imagery analysis by integrating semi-supervised
learning (SSL) with active learning strategies. Our approach utilizes
contrastive learning together with uncertainty estimations via Monte Carlo
Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed
using the Eurosat dataset. We explore the effectiveness of our method in
scenarios featuring both balanced and unbalanced class distributions. Our
results show that for unbalanced classes, our method is superior to the random
approach, enabling significant savings in labeling effort while maintaining
high classification accuracy. These findings highlight the potential of our
approach to facilitate scalable and cost-effective satellite image analysis,
particularly advantageous for extensive environmental monitoring and land use
classification tasks. Note on preliminary results: This paper presents a new
method for active learning and includes results from an initial experiment
comparing random selection with our proposed method. We acknowledge that these
results are preliminary. We are currently conducting further experiments and
will update this paper with additional findings, including comparisons with
other methods, in the coming weeks.

介绍了一种新颖方法，通过将半监督学习与主动学习策略相结合，提高了卫星图像分析的标签效率；采用对比学习和蒙特卡洛失活来估计不确定性，在 Sentinel-2 卫星图像上使用 Eurosat 数据集进行了分析，结果显示在不平衡类别情况下，该方法优于随机选择方法，在保持高分类准确性的同时显著减少了标注工作量，此方法有潜力促进可扩展和具有成本效益的卫星图像分析，对环境监测和土地利用分类任务尤为有优势。

通过对比学习和不确定性估计增强 Sentinel 2 影像的主动学习

Enhancing Active Learning for Sentinel 2 Imagery through Contrastive  Learning and Uncertainty Estimation

Environmental disasters such as floods, hurricanes, and wildfires have
increasingly threatened communities worldwide, prompting various mitigation
strategies. Among these, property buyouts have emerged as a prominent approach
to reducing vulnerability to future disasters. This strategy involves
governments purchasing at-risk properties from willing sellers and converting
the land into open space, ostensibly reducing future disaster risk and impact.
However, the aftermath of these buyouts, particularly concerning land-use
patterns and community impacts, remains under-explored. This research aims to
fill this gap by employing innovative techniques like satellite imagery
analysis and deep learning to study these patterns. To achieve this goal, we
employed FEMA's Hazard Mitigation Grant Program (HMGP) buyout dataset,
encompassing over 41,004 addresses of these buyout properties from 1989 to
2017. Leveraging Google's Maps Static API, we gathered 40,053 satellite images
corresponding to these buyout lands. Subsequently, we implemented five
cutting-edge machine learning models to evaluate their performance in
classifying land cover types. Notably, this task involved multi-class
classification, and our model achieved an outstanding ROC-AUC score of 98.86%

利用卫星图像分析和深度学习技术，研究了环境灾害、房产买断、土地利用模式及社区影响，通过 FEMA 的买断数据集和谷歌地图静态 API 获取卫星图像，应用了五种先进机器学习模型，在多类别分类任务中取得了优秀的 ROC-AUC 得分（98.86%）