Climate change has led to an increased frequency of natural disasters such as
floods and cyclones. This emphasizes the importance of effective disaster
monitoring. In response, the remote sensing community has explored change
detection methods. These methods are primarily categorized into supervised
techniques, which yield precise results but come with high labeling costs, and
unsupervised techniques, which eliminate the need for labeling but involve
intricate hyperparameter tuning. To address these challenges, we propose a
novel unsupervised change detection method named Prototype-oriented
Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures
changes by comparing features from pre-event, post-event, and
prototype-oriented change synthesis images via a foundational model, and
refines results using the Segment Anything Model (SAM). Although PUCD is an
unsupervised change detection, it does not require complex hyperparameter
tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the
disaster dataset and it achieves state-of-the-art performance compared to other
methods on the LEVIR-Extension dataset.

应对气候变化引发的自然灾害频率增加，有效的灾害监测变得至关重要。我们提出了一种名为 PUCD 的新型无监督变化检测方法，通过比较预事件、事后事件和基于原型的变化合成图像的特征，通过基础模型捕捉变化，并使用 SAM 模型对结果进行精化。在 LEVIR-Extension 数据集和灾害数据集上评估 PUCD 框架，与其他方法相比，其在 LEVIR-Extension 数据集上实现了最先进的性能。

面向灾害管理的基于原型的无监督变化检测

Prototype-oriented Unsupervised Change Detection for Disaster Management

The use of satellite imagery has become increasingly popular for disaster
monitoring and response. After a disaster, it is important to prioritize rescue
operations, disaster response and coordinate relief efforts. These have to be
carried out in a fast and efficient manner since resources are often limited in
disaster-affected areas and it's extremely important to identify the areas of
maximum damage. However, most of the existing disaster mapping efforts are
manual which is time-consuming and often leads to erroneous results. In order
to address these issues, we propose a framework for change detection using
Convolutional Neural Networks (CNN) on satellite images which can then be
thresholded and clustered together into grids to find areas which have been
most severely affected by a disaster. We also present a novel metric called
Disaster Impact Index (DII) and use it to quantify the impact of two natural
disasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework
achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the
gridded fire dataset.

本研究提出了一种卷积神经网络的框架来进行基于卫星图像的变化检测，通过阈值化和划分网格来找到灾害影响最严重的区域，并使用新的灾害影响指数（DII）来衡量自然灾害的影响。在应用于飓风哈维和圣塔罗莎大火数据集时，框架的 F1 分数分别为 81.2％和 83.5％。

从卫星图像到灾难洞察

From Satellite Imagery to Disaster Insights

Monitoring of disasters is crucial for mitigating their effects on the
environment and human population, and can be facilitated by the use of unmanned
aerial vehicles (UAV), equipped with camera sensors that produce aerial photos
of the areas of interest. A modern technique for recognition of events based on
aerial photos is deep learning. In this paper, we present the state of the art
work related to the use of deep learning techniques for disaster
identification. We demonstrate the potential of this technique in identifying
disasters with high accuracy, by means of a relatively simple deep learning
model. Based on a dataset of 544 images (containing disaster images such as
fires, earthquakes, collapsed buildings, tsunami and flooding, as well as
non-disaster scenes), our results show an accuracy of 91% achieved, indicating
that deep learning, combined with UAV equipped with camera sensors, have the
potential to predict disasters with high accuracy.

利用装备相机传感器的无人机进行灾害监测，并结合深度学习技术可以高准确性地识别灾害，544 幅图像实验结果表明，该技术有望成为高效的灾害预测方法。