## 3 Dataset  
The data used in this research are composed of high-resolution satellite images collected as part of the Maxar/DigitalGlobe data program [22]. The aim of this program is to supply satellite imagery and data to aid in response and recovery after natural disasters. To do this, the program maintains an open imagery database of satellite images that covers disasters from 2010 to the present for all types of major natural disasters world-wide. To date, the database contains collected images and data from over 50 natural disasters, which is openly available to the public. The dataset used in this work was compiled from the images in the Maxar/DigitalGlobe open data program. Images from 17 different disasters were selected. This dataset is unique from other datasets utilized in automated building damage assessment for several reasons. First, the spatial resolution for all satellite images in the dataset is under one meter. This is multiple times higher than other datasets currently available to the public. This dataset also covers multiple types of disasters, including images and data from earthquakes, tsunamis, floods, volcanic eruptions, wildfires, and wind. To the best of our knowledge, other openly available datasets only cover a single disaster type. This creates a great opportunity for developing models that can scale across different disaster types. In addition to disaster types, the dataset also features a diverse set of locations. Included in this set are images and data for disasters in the United States, Mexico, Guatem, Portugal, Indonesia, India, and Australia. Figure 2 shows an example of one such before- and after-disaster image pair. This provides unique opportunities in disaster model development for two important reasons. First, building construction varies greatly in different parts of the world. Second, different locations vary greatly in density of buildings. Included in this dataset are images with no buildings at all, which provides data to train for the negative case, which is not offered by all datasets. These opportunities can greatly assist in creating a significantly more robust model that other datasets would allow. An important contribution the developers of the dataset created when compiling it is the joint damage scale [7]. The description for each damage classification as provided by the creators of the dataset can be found in Table 1. The joint damage scale is a method for semantically rating the magnitude of damage to buildings across multiple types of disasters. This is an important contribution as it provides a means for equating the degree of damage to buildings regardless of the type of damage they have received. The joint damage scale uses 0 for no damage; 1 for minor damage such as some fire, water, or structural damage; 2 for major damage such as significant fire, water, or structural damage; and 3 for destroyed in cases where the building was scorched, collapsed, flooded, or missing completely. Figure 2: From left to right: The before-disaster, after-disaster, and damage magnitude inference images from our pipeline. This scale was created in collaboration with the National Aeronautics and Space Administration (NASA), the California Department of Forestry and Fire Protection (CAL FIRE), the Federal Emergency Management Agency (FEMA), and the California Air National Guard [7]. The dataset was manually compiled and annotated in collaboration with the California Governor's Office of Emergency Services (Cal OES), FEMA, the United States Geological Survey (USGS), the Carnegie Mellon University (CMU) Software Engineering Institute, the National Security Innovation Network, NASA, Cal FIRE, the California National Guard (Cal Guard), and the National Geospatial Intelligence Agency [21]. The dataset was ultimately called \"xBD: A Dataset for Assessing Building Damage from Satellite Imagery\" and is hosted and freely available by the Carnegie Mellon University Software engineering Institute. It covers an area of over 45,000 sq km and has annotations for over 850,000 buildings.