The process towards a carbon-neutral society requires a substantial amount of investment into electricity distribution networks for integrating more sustainable technologies such as photovoltaic systems, electric vehicles, heat pumps, and others. In order to execute the large number of network expansion measures associated with this effort, strategic and operational planning of distribution networks benefit from higher degrees of automation. A key component in these planning processes is the routing of feeder segments, i.e., the task of finding feasible and cost-efficient paths for underground power lines between two geographic locations, such as residential buildings and transformer stations. In this paper, we present , a novel and innovative approach to automate feeder routing based exclusively on aerial images. integrates deep neural networks, which encode geoinformation from aerial images, and A* search, which identifies effective feeder routes based on this encoding. We train and evaluate using a large data set of more than 7000 real-world electricity distribution network feeder segments. Thereby, we compare to state-of-the-art approaches from related work and find that the feeder segments generated by are on par or superior with regard to several characteristics and metrics. achieves this performance by relying exclusively on aerial images, whereas competing approaches require data on geocoded land cover and estimated construction cost, which is not as widely available and is frequently based on potentially subjective expert opinion.

中文翻译：

---

基于航拍图像的地下配电网自动馈线路由


迈向碳中和社会的进程需要对配电网络进行大量投资，以整合光伏系统、电动汽车、热泵等更可持续的技术。为了执行与此相关的大量网络扩展措施，配电网络的战略和运营规划受益于更高程度的自动化。这些规划过程中的一个关键组成部分是馈线段的布线，即为两个地理位置（例如住宅楼和变电站）之间的地下电力线寻找可行且经济高效的路径的任务。在本文中，我们提出了一种完全基于航空图像的自动馈线路由的新颖且创新的方法。集成了深度神经网络和 A* 搜索，深度神经网络对航空图像中的地理信息进行编码，A* 搜索可根据该编码识别有效的支线路线。我们使用包含 7000 多个现实世界配电网络馈线段的大型数据集进行训练和评估。因此，我们与相关工作中最先进的方法进行比较，发现 生成的馈线段在几个特征和指标方面达到了同等或更好的水平。通过完全依赖航空图像来实现这一性能，而竞争方法需要有关地理编码土地覆盖和估计建筑成本的数据，这些数据并不广泛可用，并且经常基于潜在的主观专家意见。