Measuring Urban Surface Reflectivity and Heat Mitigation Potential at High-Resolution with Remote Sensing and Machine Learning

Urban spaces expanded significantly in the past few decades and this trend is expected to continue in the future. The rapid growth of modern cities reduces the greenspaces and increases the amount of heat absorbent surfaces which alters of the local climate by trapping more heat from solar radiation and in turn increasing the temperature of urban areas, known as the urban heat island effect. The effects are more prominent in the central parts of cities and can cause severe risk to human health. The heat island effect can be reduced by increasing urban forestry and installing cool roofs and pavements with high solar reflectance. But cities lack and are seeking ways to target and meaningfully measure progress on heat mitigation. There is currently no cost-effective, easily repeatable and scalable way to measure urban surface changes. The lack of concrete measurability slows the adoption of urban heat mitigation policies. Cities are also seeking a scientifically sound way to select interventions and spatially target heat policy and projects to maximize the effectiveness of limited budgets. Producing open-source methods to generate a time-series of high-resolution maps of urban roof and pavement albedos will help to fill this need for large geographies at low cost.
We present an automated workflow to monitor the surface reflectivity of roofs and pavements in urban areas. We built on the methods developed in Ban-Weiss et al. 2015a & 2015b and scale them through cloud computing and machine learning. We use Microsoft building footprints and OpenStreetMap/SharedStreet API to get geometries of roofs and streets. Using open-source satellite imagery from National Agriculture Imagery Program (NAIP), ground truth measurements collected through project partners, and regression machine learning we create a high-resolution map of surface reflectivity for multiple urban areas in the United States for multiple time periods. The resulting data and maps provide an estimate of the existing surface reflectivity at a building and street-segment scale which can be superimposed with current heat vulnerability, green infrastructure, urban morphology, and urban heat data. This tool serves cities in developing and evaluating urban heat island reduction strategies and promoting extensive adoption of urban heat mitigation programs.