mlsanityData to 14 Oct 2026

SolarFormer: Multi-scale Transformer for Solar PV Profiling

arXiv 2310.20057 ↗First posted 30 Oct 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 53 references that are in the map. First posted on arXiv 30 Oct 2023.

Builds on

33 of 53 references.

References · 33

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024S3Former: Self-supervised High-resolution Transformer for Solar PV Profilingcited by 0