  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 clear
understanding of urban geometry. 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 face consequential issues such as scale invariance and defective
footprints, partly due to ever-present class-wise imbalance. Additionally, most
approaches require supplemental data such as point cloud data, building height
information, and multi-band imagery - which has limited availability and are
tedious to produce. In this paper, we propose a modified DeeplabV3+ module with
a Dilated Res-Net backbone to generate masks of building footprints from
three-channel RGB satellite imagery only. Furthermore, we introduce an F-Beta
measure in our objective function to help the model account for skewed class
distributions and prevent false-positive footprints. In addition to F-Beta, 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 performances across three public benchmarks and
demonstrate that our RGB-only method produces higher quality visual results and
is agnostic to the scale, resolution, and urban density of satellite imagery.
