An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024)
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908801425211392 |
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| author | Tian, Yihe Cheng, Kwan Man Zhang, Zhengbo Zhang, Tao Feng, Junning Ren, Zhehao Li, Suju Yan, Dongmei Xu, Bing |
| author_facet | Tian, Yihe Cheng, Kwan Man Zhang, Zhengbo Zhang, Tao Feng, Junning Ren, Zhehao Li, Suju Yan, Dongmei Xu, Bing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024) Tian, Yihe Cheng, Kwan Man Zhang, Zhengbo Zhang, Tao Feng, Junning Ren, Zhehao Li, Suju Yan, Dongmei Xu, Bing Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024) |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2508.00590 |