English(EN) An Empirical Study on Learning Rooftop Solar Potential from Satellite Imagery Directly Without Explicit Geometry

新方法直接从卫星图像预测屋顶太阳能潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种名为直接屋顶太阳能预测（DRSP）的新方法，该方法可以直接从卫星图像中估算屋顶太阳能潜力，而无需使用激光雷达或数字表面模型等显式几何数据。这种端到端的方法将卫星图像和区域日照信息直接映射到太阳能输出预测。研究团队创建了两个数据集，一个包含美国中西部约 60,000 个屋顶的数据，另一个包含德克萨斯州 87,000 个屋顶的数据，使用了来自 Google Solar API 的数据。他们的研究结果表明，有价值的屋顶规模太阳能信息可以直接从二维图像中学习，从而简化了大规模太阳能评估。 AI

影响通过直接从二维图像学习，无需复杂的几何处理，简化了大规模太阳能评估。

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arXiv cs.CV TIER_1 English(EN) · Joshua Lee · 2026-11-02 05:00

An Empirical Study on Learning Rooftop Solar Potential from Satellite Imagery Directly Without Explicit Geometry

arXiv:2610.41111v1 Announce Type: new Abstract: Rooftop solar potential estimation often relies on LiDAR, digital surface models (DSMs), or pipelines that explicitly reconstruct roof geometry before estimating solar potential. We propose Direct Rooftop Solar Prediction (DRSP), an…

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An Empirical Study on Learning Rooftop Solar Potential from Satellite Imagery Directly Without Explicit Geometry

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