A global mobile network coverage raster product at 1km resolution, 1999--2030

2026-09-10

AI Summary

本文通过结合机器学习、技术经济模拟和深度学习模型，生成了1999-2030年全球2G、3G和4G移动网络覆盖概率图，以解决缺乏一致的次国家级移动网络记录的问题。

Abstract

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.

Problem

• mobile network coverage
• global digital divide
• sub-national record

Innovation

• machine-learning model
• techno-economic simulator
• spatial deep-learning model
• global mobile network coverage
• digital divide