  As the impact of climate change escalates, the global necessity to transition
to sustainable energy sources becomes increasingly evident. Renewable energies
have emerged as a viable solution for users, with Photovoltaic energy being a
favored choice for small installations due to its reliability and efficiency.
Accurate mapping of PV installations is crucial for understanding the extension
of its adoption and informing energy policy. To meet this need, we introduce
S3Former, designed to segment solar panels from aerial imagery and provide size
and location information critical for analyzing the impact of such
installations on the grid. Solar panel identification is challenging due to
factors such as varying weather conditions, roof characteristics, Ground
Sampling Distance variations and lack of appropriate initialization weights for
optimized training. To tackle these complexities, S3Former features a Masked
Attention Mask Transformer incorporating a self-supervised learning pretrained
backbone. Specifically, our model leverages low-level and high-level features
extracted from the backbone and incorporates an instance query mechanism
incorporated on the Transformer architecture to enhance the localization of
solar PV installations. We introduce a self-supervised learning phase (pretext
task) to improve the initialization weights on the backbone of S3Former. We
evaluated S3Former using diverse datasets, demonstrate improvement
state-of-the-art models.
