发表机构
Saarland Informatics Campus; IIT Delhi; Oxford University（萨尔兰信息学校园; 德里印度理工学院; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个全球1公里分辨率移动网络覆盖栅格数据集（1999—2030年），融合机器学习、技术经济模拟和深度学习三种模型，生成2G/3G/4G覆盖概率及不确定性，用于数字鸿沟与人道规划。
AI 中文摘要
在数字时代，移动信号的可用性决定了谁能够工作、学习、办理银行业务、寻求医疗保健以及应对危机，然而目前尚不存在全球一致、次国家级的移动网络覆盖记录。我们提供了这样一份记录：覆盖214个国家和地区、1999年至2030年间的年度1公里分辨率2G、3G和4G覆盖概率地图。这些地图由三个独立模型生成：一个经过校准的机器学习模型、一个技术经济网络建设模拟器，以及一个空间深度学习模型。随后，这三个估计值按国家和技术，并依据其测量精度按比例合并，形成单一最优估计，并附有逐像素90%的不确定性区间；所有四层数据均作为数据集的一部分发布。由于移动网络的部署与一个国家的社会经济条件（人口分布、电气化、物理基础设施）密切相关，这些模型基于现有的地理空间数据，并利用截至2020年可获得的2409幅经质量筛选的运营商报告覆盖图进行调优。2021—2024年的地图仅根据近期地理空间数据预测；2025—2030年的地图则根据人口和基础设施预测进行外推。在训练期间保留的国家上，机器学习模型达到了AUC 0.89—0.92。基线比较和组合产品的外部验证在技术验证部分报告。该数据集支持绘制全球数字鸿沟地图、将连接性与家庭调查结果关联，以及人道主义和基础设施规划。
英文摘要
Where a mobile signal is available shapes who can work, learn, bank, seek health care and respond to crises in the digital age, yet no globally consistent, sub-national record of mobile network coverage exists. We present such a record: annual 1km maps of the probability of 2G, 3G and 4G coverage for 214 countries and territories for the years 1999 to 2030. The maps are produced by three independent models: a calibrated machine-learning model, a techno-economic simulator of network build-out, and a spatial deep-learning model. The three estimates are then combined, per country and technology and in proportion to their measured accuracy, into a single best estimate with per-pixel 90% uncertainty bands; all four layers are released as part of the dataset. Because mobile roll-out closely follows a country's socio-economic conditions (population distribution, electrification, physical infrastructure), the models are grounded in existing geospatial data and tuned on 2,409 quality-screened operator-reported coverage maps, which are available up to 2020. For 2021--2024 the maps are predicted from recent geospatial data alone; for 2025--2030 they are extrapolated from demographic and infrastructure projections. On countries held out during training, the machine-learning model attains AUC 0.89--0.92. Baseline comparisons and the combined product's external validation are reported in Technical Validation. The dataset supports mapping the global digital divide, linking connectivity to household-survey outcomes, and humanitarian and infrastructure planning.
CommentsDataset linked to that paper: https://zenodo.org/records/21594337
