Using deep learning and satellite imagery to predict spatial and temporal variations in transport and employment accessibility in data-sparse urban co

使用深度学习和卫星图像来预测数据稀疏的城市中交通和就业可达性的时空变化

• 批准号: 2273037
• 金额: --
• 依托单位: University of Liverpool
• 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-2273037
• 关键词: Using; deep; learning; satellite; imagery

This project seeks to develop a neural network model (NNM) to produce estimates of transport andemployment accessibility in data-sparse urban areas by drawing on data from both rich and sparseurban data contexts and satellite imagery. Specifically, the project seeks to:1) Measure Transport and Employment Accessibility (TA and EA) at neighbourhood level in richandsparse-data urban settings;2) Build a NNM based on satellite imagery to predict longitudinal and geographical changes inTA and EA;3) Develop a time-adjusted weighting system for the NNM to create reliable longitudinal TA and EA predictions in sparse urban data settings.By addressing these aims, the project will innovate by developing a scalable, transferablemethodology for leveraging existing data and open access satellite imagery to create annual TA andEA estimates at high spatial resolution in sparse urban data contexts. By doing so, it ultimately seeksto help progress the United Nations' (UN) Sustainable Development Goals (SDGs) by generating nonexistent, timely, geographically disaggregated data on TA and EA in these areas (SDG 17). Specifically it seeks to inform interventions to reduce inequalities (SDG 10) and improve access to Employment and public transport (SDG 11).

该项目旨在开发一个神经网络模型（NNM），通过利用来自丰富和稀疏城市数据背景和卫星图像的数据，对数据稀疏的城市地区的交通和就业可达性进行估计。具体而言，该项目旨在：1)在数据丰富或稀疏的城市环境中，衡量邻里层面的交通和就业可达性（TA和EA）；2)建立基于卫星影像的NNM预测ta和EA的纵向和地理变化；3)为NNM开发一个时间调整加权系统，以在稀疏的城市数据设置中创建可靠的纵向TA和EA预测。通过实现这些目标，该项目将通过开发一种可扩展、可转移的方法进行创新，利用现有数据和开放获取卫星图像，在稀疏的城市数据背景下以高空间分辨率创建年度TA和dea估计。通过这样做，它最终寻求通过生成这些领域中不存在的、及时的、按地理分类的TA和EA数据来帮助推进联合国的可持续发展目标（SDG） （SDG 17）。具体而言，它旨在为干预措施提供信息，以减少不平等（可持续发展目标10），改善就业和公共交通（可持续发展目标11）。