Buildings and floods: Micro-scale flood risk assessment in cities

由于气候变化和城市足迹的持续扩张，我们的建筑环境面临洪水和侵蚀的风险。为了管理这种风险，决策者目前依赖于使用有关已建基础设施和自然景观的粗略数据组装的模型和数据库。新技术和分析技术提高了数据的分辨率，但也带来了一系列与数据管理和从大量原始数据中提取有用信息有关的新问题，这些问题必须在模型变得更加强大之前得到解决。拟议的合作伙伴关系的重点是“微观”或物业规模的建筑属性和排水细节的表征，以支持一种新的洪水建模策略和风险评估。我们将使用创新的数据收集技术和分析技术，大大提高现有信息的质量和效用，然后将信息与我们的合作伙伴正在开发的数据库和风险评估工具联系起来。数据收集技术将包括街道摄影和激光雷达扫描，以获得城市环境的完整调查。分析技术将包括图像分析，以提取有关建筑物和城市排水系统的信息，以及数字高程模型分析，以评估微型洪水。研究结果将输入加拿大统计局开发的可链接开放数据环境（矿脉）、信贷谷保护局开发的风险和投资回报工具（RROIT）以及加拿大自然资源局开发的CanFlood风险建模工具箱。加拿大的长期利益将包括新的强大方法，以实现我们城市的高分辨率开放数据，更精确的城市洪水建模，以及更好地管理和减轻洪水风险。

Our built environment is at risk of flooding and erosion due to climate change and the continued expansion of our urban footprint. To manage this risk, decision makers currently rely on models and databases that were assembled using coarse data about the built infrastructure and natural landscape. New technologies and analysis techniques increase the resolution of data but introduce a new set of problems related to data management and the extraction of useful information from massive volumes of raw data that must be solved before models can be made more robust. The focus of the proposed partnership is the "micro" or property-scale characterization of building attributes and drainage details to support a novel flood modeling strategy and risk assessment. We will use innovative data gathering technologies and analysis techniques to vastly improve the quality and utility of available information, and then link the information to databases and risk assessment tools that are already being developed by our partners. Data gathering technologies will include street level photography and lidar scans to obtain complete surveys of urban environments. Analysis techniques will include image analysis to extract information about buildings and urban drainage systems and digital elevation model analysis to assess micro-scale flooding. The results will feed into the Linkable Open Data Environment (LODE) developed by Statistics Canada, the Risk and Return on Investment Tool (RROIT) developed by Credit Valley Conservation, and the CanFlood risk modelling toolbox developed by Natural Resources Canada. Long-term benefits to Canada will include new robust methods to enable high-resolution open data of our cities, more precise modelling of urban floods, and better management and mitigation of flood risk.