This paper presents a novel two-part pipeline for monitoring progress towards the UN Sustainable Development Goals (SDG's) related to Climate Action and Sustainable Cities and Communities. The pipeline consists of two main parts: the first part takes a raw satellite image of a motorway section and produces traffic count predictions for count sites within the image; the second part takes these predicted traffic counts and other variables to produce estimates of Local Authority (LA) motorway Average Annual Daily Traffic (AADT) and Greenhouse Gas (GHG) emissions on a per vehicle type basis. We also provide flexibility to the pipeline by implementing a novel method for estimating emissions when data on AADT per vehicle type or/and live vehicle speeds are not available. Finally, we extend the pipeline to also estimate LA A-Roads and minor roads AADT and GHG emissions. We treat the 2017 year as training and 2018 as the test year. Results show that it is possible to predict AADT and GHG emissions from satellite imagery, with motorway test year $R^2$ values of 0.92 and 0.78 respectively, and for A-roads' $R^2$ values of 0.94 and 0.98. This end-to-end two-part pipeline builds upon and combines previous research in road transportation traffic flows, speed estimation from satellite imagery, and emissions estimation, providing new contributions and insights into these areas.

翻译：本文提出了一个新颖的两阶段流水线，用于监测联合国可持续发展目标（SDG）中与气候行动及可持续城市和社区相关的进展。该流水线由两个主要部分组成：第一部分从高速公路路段的原始卫星图像出发，生成图像内计数点的交通流量预测；第二部分利用这些预测的交通流量及其他变量，按车辆类型估算地方当局（LA）高速公路的年平均日交通量（AADT）和温室气体（GHG）排放量。我们还通过实现一种新颖的方法，在缺乏按车辆类型划分的AADT数据或实时车速数据的情况下估算排放量，从而为流水线提供了灵活性。最后，我们将该流水线扩展至估算地方当局A级公路及次要公路的AADT和温室气体排放量。我们将2017年作为训练年份，2018年作为测试年份。结果表明，从卫星图像预测AADT和温室气体排放是可行的，高速公路测试年份的$R^2$值分别为0.92和0.78，而A级公路的$R^2$值分别为0.94和0.98。这种端到端的两阶段流水线建立在并整合了先前关于道路交通流量、卫星图像速度估算及排放估算的研究，为这些领域提供了新的贡献与见解。