  Urban areas consume over two-thirds of the world's energy and account for
more than 70 percent of global CO2 emissions. As stated in IPCC's Global
Warming of 1.5C report, achieving carbon neutrality by 2050 requires a scalable
approach that can be applied in a global context. Conventional methods of
collecting data on energy use and emissions of buildings are extremely
expensive and require specialized geometry information that not all cities have
readily available. High-quality building footprint generation from satellite
images can accelerate this predictive process and empower municipal
decision-making at scale. However, previous deep learning-based approaches use
supplemental data such as point cloud data, building height information, and
multi-band imagery - which has limited availability and is difficult to
produce. In this paper, we propose a modified DeeplabV3+ module with a Dilated
ResNet backbone to generate masks of building footprints from only
three-channel RGB satellite imagery. Furthermore, we introduce an F-Beta
measure in our objective function to help the model account for skewed class
distributions. In addition to an F-Beta objective function, we incorporate an
exponentially weighted boundary loss and use a cross-dataset training strategy
to further increase the quality of predictions. As a result, we achieve
state-of-the-art performance across three standard benchmarks and demonstrate
that our RGB-only method is agnostic to the scale, resolution, and urban
density of satellite imagery.
