图 书 馆
DeepRoof：一种基于屋顶图像的数据驱动太阳势估算方法DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery |
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| 课程网址: | http://videolectures.net/kdd2019_lee_iyengar_feng/ |
| 主讲教师: | Stephen Lee |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  屋顶太阳能部署是产生清洁能源的绝佳来源。结果，这些年来，它们在房主中的受欢迎程度大大提高了。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，后者会手动评估场地。最近，人们在努力自动估计城市中任何屋顶的太阳能潜力。但是，当前方法仅适用于可获得LIDAR数据的地方，从而将其范围限制在世界上的少数地方。在本文中，我们提出了DeepRoof，这是一种数据驱动的方法，它使用广泛可用的卫星图像来评估屋顶的太阳能潜力。 DeepRoof使用卫星图像来确定屋顶的几何形状，并利用公开提供的房地产和太阳辐照度数据来提供每个平面屋顶部分的太阳势像素水平估计。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，我们在带注释的屋顶数据集上评估我们的方法，与太阳能专家验证结果并将其与基于LIDAR的方法进行比较。我们的结果表明，DeepRoof可以准确地提取屋顶几何形状，例如平面屋顶节段及其方向，在识别屋顶时达到91.1％的真实正向率，而平均方向误差仅为9.3度。我们还显示，DeepRoof对可用太阳能安装区域的中值估计是基于LIDAR方法的11％以内。 |
| 课程简介: | 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. |
| 关 键 词: | DeepRoof; 太阳能; 清洁能源; 数据驱动 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-26：zhouxj |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 90 |