A Novel Modeling Framework and Data Product for Extended VIIRS-like Artificial Nighttime Light Image Reconstruction (1986-2024)

2025-08-01

AI Summary

Existing VIIRS-like nighttime light (NTL) time series reconstructions for 1986–2012 suffer from systematic underestimation of radiance intensity and loss of spatial structural fidelity, severely limiting the accuracy of long-term human activity quantification. To address this, we propose a two-stage deep reconstruction framework: (1) a spatiotemporally consistent foundational NTL sequence is constructed by fusing multi-source DMSP/OLS and VIIRS data; (2) a hierarchical fusion decoder coupled with a dual-feature refinement network—guided by high-resolution impervious surface masks—is employed to recover fine-scale spatial details. The resulting EVAL-NTL dataset for China (1986–2023) achieves an R² of 0.80 and RMSE of 0.99, outperforming state-of-the-art products. It exhibits superior temporal consistency and strong correlations with socioeconomic indicators. The dataset is publicly available.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Feature Construction/Reformulation
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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. Despite the progress in extending VIIRS-like NTL time-series, current methods still suffer from two significant shortcomings: the underestimation of light intensity and the structural omission. To overcome these limitations, we propose a novel reconstruction framework consisting of a two-stage process: construction and refinement. The construction stage features a Hierarchical Fusion Decoder (HFD) designed to enhance the fidelity of the initial reconstruction. The refinement stage employs a Dual Feature Refiner (DFR), which leverages high-resolution impervious surface masks to guide and enhance fine-grained structural details. Based on this framework, we developed the Extended VIIRS-like Artificial Nighttime Light (EVAL) product for China, extending the standard data record backwards by 26 years to begin in 1986. Quantitative evaluation shows that EVAL significantly outperforms existing state-of-the-art products, boosting the $ ext{R}^2$ from 0.68 to 0.80 while lowering the RMSE from 1.27 to 0.99. Furthermore, EVAL exhibits excellent temporal consistency and maintains a high correlation with socioeconomic parameters, confirming its reliability for long-term analysis. The resulting EVAL dataset provides a valuable new resource for the research community and is publicly available at https://doi.org/10.11888/HumanNat.tpdc.302930.

Problem

• Extend VIIRS-like NTL data back to 1986 for long-term studies
• Address underestimation and structural omission in current NTL methods
• Enhance NTL reconstruction fidelity with hierarchical and dual-feature techniques

Innovation

• Hierarchical Fusion Decoder enhances reconstruction fidelity
• Dual Feature Refiner improves fine-grained structural details
• Extended VIIRS-like product covers 1986-2024 with high accuracy