DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery
DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery
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DOI：
10.1145/3292500.3330741
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发表时间：
2019-07
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通讯作者：
Stephen Lee;Srinivasan Iyengar;Menghong Feng;Prashant J. Shenoy;Subhransu Maji
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作者：
Stephen Lee;Srinivasan Iyengar;Menghong Feng;Prashant J. Shenoy;Subhransu Maji
屋顶太阳能的部署是产生清洁能源的绝佳来源。因此，多年来，它们在房主中的受欢迎程度显著增加。不幸的是，估计屋顶的太阳能潜力需要房主咨询太阳能顾问，他们手动评估网站。最近，人们开始努力自动评估城市内任何屋顶的太阳能潜力。然而，目前的方法只适用于有激光雷达数据的地方，因此它们的覆盖范围仅限于世界上的少数几个地方。在这篇文章中，我们提出了DeepRoof，一种数据驱动的方法，使用广泛可用的卫星图像来评估屋顶的太阳能潜力。DeepRoof使用卫星图像确定屋顶的几何形状，并利用公开可用的房地产和太阳辐射数据来提供每个平面屋顶段的太阳能潜力的像素级估计。这样的估计可以用来确定屋顶上安装太阳能电池板的理想位置。此外，我们在一个带注释的屋顶数据集上对我们的方法进行了评估，与太阳能专家验证了结果，并将其与基于LIDAR的方法进行了比较。实验结果表明，DeepRoof能够准确提取平面屋面段及其朝向等屋面几何信息，对屋面的识别正确率为91.1%，平均朝向误差为9.3度。我们还表明，DeepRoof对可用太阳能安装面积的中值估计与基于LIDAR的方法的中值估计在11%以内。
Rooftop solar deployments are an excellent source for generating clean energy. As a result, their popularity among homeowners has grown significantly over the years. Unfortunately, estimating the solar potential of a roof requires homeowners to consult solar consultants, who manually evaluate the site. Recently there have been efforts to automatically estimate the solar potential for any roof within a city. However, current methods work only for places where LIDAR data is available, thereby limiting their reach to just a few places in the world. In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. Using satellite images, DeepRoof determines the roof's geometry and leverages publicly available real-estate and solar irradiance data to provide a pixel-level estimate of the solar potential for each planar roof segment. Such estimates can be used to identify ideal locations on the roof for installing solar panels. Further, we evaluate our approach on an annotated roof dataset, validate the results with solar experts and compare it to a LIDAR-based approach. Our results show that DeepRoof can accurately extract the roof geometry such as the planar roof segments and their orientation, achieving a true positive rate of 91.1% in identifying roofs and a low mean orientation error of 9.3 degree. We also show that DeepRoof's median estimate of the available solar installation area is within 11% of a LIDAR-based approach.