Seattle Demo Accompanying Files

<p dir="ltr">We release the data, code, and prepared city graph objects to facilitate city scale building operating energy prediction with Seattle as a case study. </p><p dir="ltr">The zipped folder consists of five separate folders:</p><ul><li>Trained_Model (Pretrained GNN model weights in PyTorch format)</li><li>Seattle Graphs (Contains city object nodes and COO format edges)</li><li>LCZ_raster (100M global local climate zone raster, obtained from https://doi.org/10.5194/essd-14-3835-2022)</li><li>Image_Model (Pretrained Resnet-18 image encoder)</li><li>Building_Satellite (Placeholder folder for building satellite images)</li></ul><p dir="ltr">Due to file storage limitations, building satellite files are separately available at: 1) 10.6084/m9.figshare.28188230, 2)10.6084/m9.figshare.28188005, and 3) 10.6084/m9.figshare.27091783.</p><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. </p><p dir="ltr">For any questions or clarifications, please contact: winyap@mit.edu. </p>