Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed , a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an score of 66% w.r.t. a ground truth reference map, with an average error of only 10 inhabitants/ha. Conveniently, retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: .

中文翻译：

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来自 Sentinel-1 和 Sentinel-2 的高分辨率人口地图


详细的人口地图在从人道主义行动到城市规划等各个领域发挥着重要作用。及时且可扩展的方式生成此类地图是一项挑战，特别是在数据稀缺的地区。为了解决这个问题，我们开发了一种人口测绘方法，其唯一输入是来自 Sentinel-1 和 Sentinel-2 的免费、全球可用的卫星图像；以及粗略人口普查地区的少量总人口计数以进行校准。尽管数据要求极低，但我们的方法超越了现有方案的测绘精度，其中包括一些依赖于高分辨率图像的建筑足迹的方案。例如，我们能够根据不到 400 个地区人口普查数据，制作出 1 亿 GSD 的卢旺达人口地图。在基加利，这些地图相对于地面真实参考地图的得分达到 66%，平均误差仅为 10 名居民/公顷。方便地检索建成区和当地建筑占用率的明确地图，使绘图过程可解释并提供额外的见解，例如关于建成区但无人居住的区域（例如工业仓库）的分布。通过我们的工作，我们的目标是实现最新、高分辨率人口地图的民主化，并认识到一些人口动态特别强劲的地区可能缺乏开展昂贵的微观人口普查活动的资源。项目页面： .