重建1986-2024年中国夜间灯光数据，更准更细。
An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024)
- 两阶段深度学习模型，融合不透水面数据提升细节
- 相比现有产品，灯光强度估算更准，时间序列更稳定
- 适合研究长期城市化、经济活动变化的学者
人工夜间灯光遥感是量化人类活动强度与空间分布的重要代理指标。尽管NPP-VIIRS传感器提供了高质量的夜间灯光观测，但其时间覆盖始于2012年，限制了对更早时期长期时间序列的研究。当前扩展的VIIRS类夜间灯光数据产品存在两个显著缺陷：灯光强度低估和结构细节缺失。为克服这些局限，本文提出扩展的VIIRS类人工夜间灯光（EVAL）数据集，该数据集为中国1986至2024年的年度夜间灯光数据，基于一种新型两阶段深度学习模型生成。模型首先构建初始估计，随后利用高分辨率不透水面数据作为引导，精细化重构结构细节。定量评估表明，EVAL显著优于现有先进产品，在时间一致性与社会经济指标相关性方面表现更优。
原文摘要 · Abstract (English)
Artificial Night-Time Light (NTL) remote sensing is a vital proxy for quantifying the intensity and spatial distribution of human activities. Although the NPP-VIIRS sensor provides high-quality NTL observations, its temporal coverage, which begins in 2012, restricts long-term time-series studies that extend to earlier periods. Current extended VIIRS-like NTL data products suffer from two significant shortcomings: the underestimation of light intensity and the omission of structural details. To overcome these limitations, we present the Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset, a new annual NTL dataset for China spanning from 1986 to 2024. This dataset was generated using a novel two-stage deep learning model designed to address the aforementioned shortcomings. The model first constructs an initial estimate and subsequently refines fine-grained structural details using high-resolution impervious surface data as guidance. Quantitative evaluations demonstrate that EVAL significantly outperforms state-of-the-art products, exhibiting superior temporal consistency and a stronger correlation with socioeconomic indicators.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。