A Human-Trustable Self-Improving Machine Learning Framework for Rapid Disaster Responses Using Satellite Sensor Imagery

人类可信的自我改进机器学习框架，利用卫星传感器图像快速响应灾难

• 批准号: EP/X027732/1
• 负责人: Xiaowei GU
• 金额: $ 34.1万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX027732%2F1
• 关键词: Human; Trustable; Self; Improving; Machine

Due to the abrupt changes in Earth's climate and the dramatic global rise of urbanisation, natural disasters have become unpredictable and caused great social and economic devastation in recent years. According to one published study, between 2015-2019, there were a total of 1624 reported natural disasters, such as earthquakes, floods, landslides, etc., killing on average 60,000 people each year globally. Although humans cannot prevent natural disasters in most cases, timely responses can play a critical role in disaster relief and life-saving. Rapid and accurate building damage assessment (BDA) is required in humanitarian assistance and disaster response to carry out life-saving efforts. However, current BDAs are mostly based on manual inspection and documentation, which is time consuming and labour-intensive. Although high-resolution satellite sensor images (HRSSIs) such as GeoEye-1, WorldView-2 and 3, have become the major source of first-hand information for BDA, those images often present a mosaic of complex geometrical structures and spatial patterns. Automatic information extraction from HRSSIs of disaster-affected areas is imperative under time-critical situations, and has the potential to facilitate post-disaster assessment, speed up the life-saving rescue processes. However, this remains an extremely challenging task for the state-of-the-art machine learning (ML) algorithms. In practice, human experts have to manually interpret and examine the captured HRSSIs, which involves significant time and labour.Conventional ML-based BDA methods leverage mainstream classifiers, such as support vector machine, random forest, to generate a damage map based on hand-crafted features extracted from pre- and post- disaster images. However, the complexity and heterogeneity of HRSSIs hinder the applicability of conventional methods, making feature extraction extremely difficult. Besides, buildings often involve only a few pixels, leaving minimal structural information to exploit. Although conventional methods do not require a large volume of training images and are more interpretable, they fail easily on real scenes. On the other hand, deep learning techniques, particularly, deep convolutional neural networks (DCNNs) have reported significant achievements in the field of computer vision and pattern recognition. Some recent studies have explored the capability of DCNNs on BDA and reported promising outcomes under experimental conditions. DCNN-based methods have become increasingly popular and are currently the state-of-the-art in BDA research. However, DCNNs are often characterised as black boxes, and are computationally intensive and data-hungry. As the underlying mechanisms are different from humans and not understandable, DCNNs can fail easily in unfamiliar scenarios due to uncertainties and are often observed to exhibit unexpected behaviours. These disadvantages hinder the practical utilities of DCNN-based BDA methods in real-world scenarios. As a result, emergency management services (EMSs), e.g., the International Charter Space and Major Disasters, still rely on visual interpretation of HRSSIs to assess building damage due to the reliability. To make ML-based BDA methods reliable for real-world scenarios, this project aims to catalyse a step-change in artificial intelligence by developing highly innovative explainable ML (XML) techniques to automate the BDA processes based on post-disaster HRSSIs. The developed XML techniques will act as a framework for scene understanding, building segmentation and damage assessment on both scene-level and pixel-level in a joint fashion, and have the capacity to self-adapt to different application scenarios in real-time to address real-world uncertainties. By achieving a reliable automated solution to facilitate the highly challenging post-disaster BDA task, we ultimately aim to assist EMSs for faster post-disaster assessment, facilitating life-saving process.

由于地球气候的突然变化和城市化的全球崛起，自然灾害变得不可预测，近年来造成了巨大的社会和经济灾难。根据一项已发表的研究，在2015 - 2019年之间，总共有1624例报告的自然灾害，例如地震，洪水，滑坡等，每年平均每年60,000人丧生。尽管人类在大多数情况下无法预防自然灾害，但及时的反应可以在救灾和挽救生命中发挥关键作用。在人道主义援助和灾难反应中，需要快速准确的建筑损害评估（BDA），以进行挽救生命。但是，当前的BDA主要基于手动检查和文档，这是耗时和劳动力密集的。尽管高分辨率卫星传感器图像（HRSSIS），例如Geoeye-1，Worldview-2和3，已成为BDA的第一手信息的主要来源，但这些图像通常呈现出复杂的几何结构和空间模式的镶嵌物。在关键时期的情况下，从受灾地区的HRSSI中进行自动提取的信息是必要的，并且有可能促进灾后评估，加快挽救生命的救援过程。但是，对于最先进的机器学习（ML）算法，这仍然是一项极具挑战性的任务。实际上，人类专家必须手动解释和检查被捕获的HRSSIS，涉及大量时间和劳动。基于ML的BDA方法利用主流分类器（例如支持向量机，随机森林）来生成基于手工制作的功能，从前和后灾难图像中提取的手工制作的功能。但是，HRSSIS的复杂性和异质性阻碍了常规方法的适用性，从而使特征提取极为困难。此外，建筑物通常只涉及几个像素，而留下最少的结构信息来利用。尽管常规方法不需要大量的培训图像，并且更容易解释，但它们在真实场景中很容易失败。另一方面，深度学习技术，尤其是深度卷积神经网络（DCNN）报告了计算机视觉和模式识别领域的重大成就。最近的一些研究探索了DCNN在BDA上的能力，并在实验条件下报告了有希望的结果。基于DCNN的方法已经变得越来越流行，目前是BDA研究的最新方法。但是，DCNN通常以黑匣子为特征，并且在计算密集型和渴望数据中。由于基本机制与人类不同，并且不可理解，因此由于不确定性，DCNN在陌生的情况下很容易失败，并且经常被观察到表现出意外的行为。这些缺点阻碍了现实情况下基于DCNN的BDA方法的实用性。结果，紧急管理服务（EMSS），例如国际宪章空间和重大灾难，仍然依靠对HRSSI的视觉解释来评估由于可靠性而导致的建筑损失。为了使基于ML的BDA方法可靠地对现实世界情景可靠，该项目旨在通过开发高度创新的可解释的ML（XML）技术来促进人工智能的逐步变化，以根据后迪沙斯特HRSSIS自动化BDA流程。开发的XML技术将成为场景理解，建立分割和损害评估的框架，以共同的方式对场景级别和像素级别进行，并有能力实时自适应不同的应用程序场景，以解决现实世界中的不确定性。通过实现可靠的自动化解决方案，以促进高度挑战的后BDA任务，我们最终旨在协助EMS进行更快的污点后评估，从而促进挽救生命的过程。