The Alan Turing Institute, London, NW1 2DB, UK.
Accountable, Responsible and Transparent AI CDT, Department of Computer Science, University of Bath, Bath, UK.
Sci Rep. 2023 Jul 5;13(1):10922. doi: 10.1038/s41598-023-38100-1.
Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.
航空图像数据越来越普及，基于监督学习的分析技术正在推进其在各种遥感环境中的应用。然而，监督学习需要训练数据集，而这些数据集并不总是可用的，或者很难用航空图像构建。在这方面，无监督机器学习技术具有重要的优势。本研究提出了一种新的工作流程，以展示如何利用现有的航空图像来更好地提供与建筑环境相关的服务，以英国三个城市的道路交通碰撞 (RTC) 为例。在本文中，我们展示了如何利用航空图像从自上而下图像的纯视觉表示中提取建筑环境的潜在特征。有了这些潜在的图像特征来表示城市结构，本研究然后演示了如何对危险的道路段进行聚类，为道路安全专家提供数据增强的辅助，以增强他们对不同类型的 RTC 发生的方式和地点的细微理解。
