With the rapid development of earth observation technology, we have entered
an era of massively available satellite remote-sensing data. However, a large
amount of satellite remote sensing data lacks a label or the label cost is too
high to hinder the potential of AI technology mining satellite data. Especially
in such an emergency response scenario that uses satellite data to evaluate the
degree of disaster damage. Disaster damage assessment encountered bottlenecks
due to excessive focus on the damage of a certain building in a specific
geographical space or a certain area on a larger scale. In fact, in the early
days of disaster emergency response, government departments were more concerned
about the overall damage rate of the disaster area instead of single-building
damage, because this helps the government decide the level of emergency
response. We present an innovative algorithm that constructs Neyman stratified
random sampling trees for binary classification and extends this approach to
multiclass problems. Through extensive experimentation on various datasets and
model structures, our findings demonstrate that our method surpasses both
passive and conventional active learning techniques in terms of class rate
estimation and model enhancement with only 30\%-60\% of the annotation cost of
simple sampling. It effectively addresses the 'sampling bias' challenge in
traditional active learning strategies and mitigates the 'cold start' dilemma.
The efficacy of our approach is further substantiated through application to
disaster evaluation tasks using Xview2 Satellite imagery, showcasing its
practical utility in real-world contexts.

利用构建奈曼分层随机抽样树的创新算法以及对多类问题的推广，通过广泛的实验验证，本研究发现其在类别比率估计和模型增强方面能够超越传统主动学习方法，仅需传统简单抽样的 30％-60％的注释成本。该方法有效解决了传统主动学习策略中的 “采样偏倚” 问题，并缓解了 “冷启动” 困境。同时，在使用 Xview2 卫星图像进行灾害评估任务时，本方法展现出其在实际场景中的实用性。