Remote Sensing Imagery: A new frontier for Urban Analytics?

遥感图像：城市分析的新领域？

• 批准号: 2276268
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2276268
• 关键词: Remote; Sensing; Imagery; new; frontier

In a tight economic climate, it is more important than ever to have the best possible understanding of how the built environment changes over time, so policy makers can prioritise and adapt effectively. Coupled with climate change, the global population living in the built environment is projected to increase to 68% by 2050 (United Nations 2018) and global patterns of urbanization will be heterogeneous (McGee 2010; Kourtit et al. 2014), so accurate measurements of changes in and between urban agglomerations (both physical and virtual) is critical for the planning of Smart Cities. Measurements of urban change is critical to meet the future challenges of housing, transportation, energy systems, education and healthcare.There are three sources of data that have emerged in the field of urban analytics, which are data collected by mobile sensors carried by individuals, data extracted from internet sites and government data released in an open format (Arribas-Bel 2014). We propose that Remote Sensing Information (RSI) will emerge as a large and thoroughly-used new source of information for urban studies. RSI contains an abundance of information, which has driven insights into socio-economic relationships (Jean et al. 2016). The potential for RSI to help automate change detection in the built environment is massive; it would be both time and cost efficient compared to the main alternative, field surveying. Whilst several studies have applied LiDAR to monitor changes in the built environment, historic applications of coarse-medium spatial resolution (15-60m) Multispectral Satellite Imagery (MSI) have been limited due to the inability to classify complex features such as buildings (Hegazy and Kaloop 2015). To overcome this limitation, some studies have fused high temporal resolution MSI with high spatial resolution LiDAR data to build better overall insights (Yan et al. 2014; Wu et al. 2015). Furthermore, fusion methods have the merits of high horizontal accuracy from aerial images and high vertical accuracy from LIDAR data (Alonso-Benito et al. 2016). For example, Dong et al (2010) combined LiDAR with Landsat Thematic Mapper to calculate small-area population estimates. Further, the space industrial sector is rapidly growing and there is a global movement towards the commercialisation of RSI: recent technological advances achieve spatial resolutions similar to LiDAR, less than 1m.The aim of this project is to evaluate the potential of applying RSI for the analysis of changes to cities over time. Specifically, we will compare whether the current technologies of LiDAR, MSI or a fusion of these technologies is best practice for classifying complex features such as buildings and roads in the built environment and how they change over time. Building up a detailed geo- referenced space-time database on urban evolution is important for forecasting and policy. For example, identifying socio-economic and demographic diversity within and between urban areas. Such a study is forward looking, given the increasing spatial and temporal resolution of RSI.

在紧张的经济环境下，尽可能充分地了解建筑环境随时间的变化比以往任何时候都更加重要，以便政策制定者能够有效地确定优先顺序并进行调整。再加上气候变化，预计到 2050 年，全球居住在建筑环境中的人口将增加到 68%（联合国，2018），全球城市化模式将呈现异质性（McGee，2010；Kourtit 等人，2014），因此准确测量城市群（物理和虚拟）内部和之间的变化对于智慧城市规划至关重要。城市变化的衡量对于应对未来住房、交通、能源系统、教育和医疗保健的挑战至关重要。城市分析领域出现了三种数据来源，即个人携带的移动传感器收集的数据、从互联网网站提取的数据以及以开放格式发布的政府数据（Arribas-Bel 2014）。我们建议遥感信息（RSI）将成为城市研究的一个大型且广泛使用的新信息源。 RSI 包含大量信息，推动了对社会经济关系的深入了解（Jean 等人，2016）。 RSI 在帮助自动化构建环境中的变化检测方面具有巨大的潜力；与主要的替代方案——实地测量相比，它既节省时间又节省成本。虽然一些研究已应用激光雷达来监测建筑环境的变化，但由于无法对建筑物等复杂特征进行分类，粗中空间分辨率（15-60m）多光谱卫星图像（MSI）的历史应用受到限制（Hegazy 和 Kaloop 2015）。为了克服这一限制，一些研究将高时间分辨率 MSI 与高空间分辨率 LiDAR 数据融合起来，以建立更好的整体见解（Yan 等人，2014 年；Wu 等人，2015 年）。此外，融合方法具有航拍图像水平精度高和激光雷达数据垂直精度高的优点（Alonso-Benito et al. 2016）。例如，Dong 等人 (2010) 将 LiDAR 与 Landsat Thematic Mapper 结合起来计算小区域人口估计值。此外，航天工业部门正在迅速发展，全球范围内出现了 RSI 商业化运动：最近的技术进步实现了类似于 LiDAR 的空间分辨率，小于 1m。该项目的目的是评估应用 RSI 来分析城市随时间变化的潜力。具体来说，我们将比较当前的激光雷达、MSI 技术或这些技术的融合是否是对建筑环境中的建筑物和道路等复杂特征进行分类的最佳实践，以及它们如何随时间变化。建立关于城市演变的详细地理参考时空数据库对于预测和政策非常重要。例如，确定城市地区内部和之间的社会经济和人口多样性。鉴于 RSI 的空间和时间分辨率不断提高，此类研究具有前瞻性。