论文标题
使用半监督的学习技术评估卫星图像中的污水检查后损害
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
论文作者
论文摘要
为了应对地震，野火和武装冲突等灾难，人道主义组织需要以损害评估的形式进行准确，及时的数据，这表明建筑物和人口中心受到最大影响。最近的研究将机器学习与遥感结合在一起，以自动从卫星图像中提取此类信息，减少体力劳动和周转时间。在实际灾难响应方案中使用机器学习方法的主要障碍是难以获得足够数量的标记数据来训练模型进行展开的灾难。本文显示了半监督学习（SSL）在训练模型中的新应用，以损害评估，并以最少的标记数据和大量未标记的数据来评估。我们将最先进的SSL方法（包括MixMatch和FixMatch）的性能与2010年海地地震，2017年圣罗莎·野火（Santa Rosa Wildfire）以及2016年叙利亚武装冲突的监督基线进行了比较。我们展示了接受SSL方法培训的模型如何达到完全监督的性能，尽管仅使用了一小部分标记数据并确定领域以进一步改进。
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from satellite imagery, reducing manual labor and turn-around time. A major impediment to using machine learning methods in real disaster response scenarios is the difficulty of obtaining a sufficient amount of labeled data to train a model for an unfolding disaster. This paper shows a novel application of semi-supervised learning (SSL) to train models for damage assessment with a minimal amount of labeled data and large amount of unlabeled data. We compare the performance of state-of-the-art SSL methods, including MixMatch and FixMatch, to a supervised baseline for the 2010 Haiti earthquake, 2017 Santa Rosa wildfire, and 2016 armed conflict in Syria. We show how models trained with SSL methods can reach fully supervised performance despite using only a fraction of labeled data and identify areas for further improvements.
