LAUSR

Abstract Sky View Factor (SVF) is a commonly used indicator of urban geometry. The availability of street-level SVF measurements has been fairly limited due to the high costs of field survey. The Google Street View (GSV) serves a massive storage of panorama data that can be utilized to obtain SVF measurements. Yet, automatic extraction of SVFs from panoramas is a complicated process that involves multiple sophisticated computation technologies including machine learning, big image data processing, SVF estimation and geographic information systems (GIS), which constitute major hurdles for the end users. In this light, we developed an easy-to-use GIS-integrated tool (GSV2SVF) to streamline the workflow of extracting SVFs from GSV images and therefore making this vast treasure trove of information conveniently available to everyone at a mouse click. As by-products in addition to the SVF, the results obtained from each GSV panorama are accompanied with the tree view factor (TVF) and the building view factor (BVF), which together can provide a more holistic characterization of the outdoor built environment. GSV2SVF is freely available with source code at https://github.com/jian9695/GSV2SVF . A video is available at https://github.com/jian9695/GSV2SVF/blob/master/Video.mp4 and https://youtu.be/k00wCnuzuvE .