SolarFormer: Multi-scale Transformer for Solar PV Profiling
Adrian de Luis Minh Tran Taisei Hanyu Anh Tran Liao Haitao Roy McCann Alan Mantooth Ying Huang Ngan Le
As climate change intensifies, the global imperative to shift towards
sustainable energy sources becomes more pronounced. Photovoltaic (PV) energy is
a favored choice due to its reliability and ease of installation. Accurate
mapping of PV installations is crucial for understanding their adoption and
informing energy policy. To meet this need, we introduce the SolarFormer,
designed to segment solar panels from aerial imagery, offering insights into
their location and size. However, solar panel identification in Computer Vision
is intricate due to various factors like weather conditions, roof conditions,
and Ground Sampling Distance (GSD) variations. To tackle these complexities, we
present the SolarFormer, featuring a multi-scale Transformer encoder and a
masked-attention Transformer decoder. Our model leverages low-level features
and incorporates an instance query mechanism to enhance the localization of
solar PV installations. We rigorously evaluated our SolarFormer using diverse
datasets, including GGE (France), IGN (France), and USGS (California, USA),
across different GSDs. Our extensive experiments consistently demonstrate that
our model either matches or surpasses state-of-the-art models, promising
enhanced solar panel segmentation for global sustainable energy initiatives.