Geospatial Design of Energy Systems for Africa: Citizen Science

非洲能源系统地理空间设计（GeoDESA） Citizen Science旨在将公民科学整合到发展中地区的农村电网规划中。目前仍有超过8亿人无法获得可靠的电力，而联合国的目标是到2030年使所有人都能获得负担得起的清洁能源，因此需要创新的电气化战略来缩小这一差距。数据驱动的地理空间系统规划是加速电气化的有效途径。高分辨率地理数据和现代计算能力可以用来规划社区特定的电网发展，成本低，速度快，与传统方法相比，成本降低了一个数量级。这种方法的一个关键挑战是需要详细的家庭位置数据。主位置对于设计网格拓扑和体系结构是必要的。这种程度的特殊性使得电网成本估算和社区设计更加合理。目前可用的家庭位置数据集，如OpenStreetMap，通常是不完整的，特别是在最难到达的农村贫困地区。高分辨率卫星图像和现代计算机视觉算法可以填补这些数据集的空白。虽然过去已经训练了许多算法来检测住房，但它们通常是基于丰富的城市环境。必须充分考虑到农村和偏远地区住宅风格的多样性，以便进行电气化设计所需的住宅检测。通过让熟悉当地农村住房风格的公民科学家在卫星图像中标记房屋，可以将当地知识纳入可扩展的数据驱动方法中。有了准确的、背景信息丰富的标记卫星数据，计算机视觉算法就可以得到训练，在最难到达的地区可靠地定位农村住宅，从而有效地设计网格，以满足社区需求。该提案补充了牛津大学在非洲农村电气化SONG和RELCON项目中过去和正在进行的工作。所开发的方法和产生的数据将对许多农村和偏远地区的电力系统规划有用，并可交叉应用于其他地理规划学科，如城市规划、移民跟踪和地理空间贫困估计。

Geospatial Design of Energy Systems for Africa (GeoDESA) Citizen Science aims to integrate citizen science in rural electrical grid planning for developing regions. As over 800 million people still lack reliable access to electricity, and the UN is targeting affordable clean energy access for all by 2030, innovative electrification strategies are needed to close the gap. Data-driven geospatial system planning can be an effective way to accelerate electrification. High-resolution geographic data and modern computing power can be leveraged to plan community-specific grid development cheaply and quickly, reducing costs by an order of magnitude compared to traditional methods. One key challenge in this approach is the need for detailed home location data. Home locations are necessary to design grid topologies and architectures. This level of specificity enables better grid cost estimates and community-appropriate design. Currently available home location datasets, such as OpenStreetMap, are typically incomplete, particularly in the hardest-to-reach rural poor areas. High-resolution satellite imagery and modern computer vision algorithms can fill the gaps in these datasets. While many algorithms have been trained to detect housing in the past, they are typically based on rich urban contexts. The full diversity of rural and remote housing styles must be taken into account to enable the dwelling detection needed for electrification design.By engaging citizen scientists fluent in local rural housing styles in the labeling of homes in satellite imagery, local knowledge can be incorporated in a scalable data-driven approach. With accurate and context-informed labelled satellite data, computer vision algorithms can be trained to reliably locate rural dwellings in the hardest to reach areas, allowing grids to be efficiently designed to suit community needs.This proposal complements past and ongoing work undertaken at the University of Oxford in rural Africa electrification from the SONG and RELCON projects. The methods developed and data generated will be useful for electrical system planning in many rural and remote contexts, and can cross-apply into other geographic planning disciplines, such as urban planning, migration tracking, and geospatial poverty estimation.