Abstract

SolarFormer targets accurate mapping of photovoltaic installations from aerial imagery. The project uses a multi-scale Transformer encoder and a masked-attention Transformer decoder to improve localization of solar PV installations across different ground sampling distances.

The model is evaluated with datasets including GGE, IGN, and USGS imagery, supporting solar panel segmentation for sustainable energy analysis.