{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang L"],"funding":["National Natural Science Foundation of China","National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["414"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11035565"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["Nighttime light remote sensing has been an increasingly important proxy for human activities. Despite an urgent need for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. A Night-Time Light convolutional LSTM network is proposed and applied the network to produce a 1-km annual Prolonged Artificial Nighttime-light DAtaset of China (PANDA-China) from 1984 to 2020. Assessments between modeled and original images show that on average the RMSE reaches 0.73, the coefficient of determination (R<sup>2</sup>) reaches 0.95, and the linear slope is 0.99 at the pixel level, indicating a high confidence in the quality of generated data products. Quantitative and visual comparisons witness PANDA-China's superiority against other NTL datasets in its significantly longer NTL dynamics, higher temporal consistency, and better correlations with socioeconomics (built-up areas, gross domestic product, population) characterizing the most relevant indicator in different development phases. The PANDA-China product provides an unprecedented opportunity to trace nighttime light dynamics in the past four decades."],"journal":["Scientific data"],"pubmed_title":["A Prolonged Artificial Nighttime-light Dataset of China (1984-2020)."],"pmcid":["PMC11035565"],"funding_grant_id":["No. 41871331, 41801343, T2125006, U1839206"],"pubmed_authors":["Ren Z","Gong P","Xu B","Fu H","Zhang L","Chen B"],"additional_accession":[]},"is_claimable":false,"name":"A Prolonged Artificial Nighttime-light Dataset of China (1984-2020).","description":"Nighttime light remote sensing has been an increasingly important proxy for human activities. Despite an urgent need for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. A Night-Time Light convolutional LSTM network is proposed and applied the network to produce a 1-km annual Prolonged Artificial Nighttime-light DAtaset of China (PANDA-China) from 1984 to 2020. Assessments between modeled and original images show that on average the RMSE reaches 0.73, the coefficient of determination (R<sup>2</sup>) reaches 0.95, and the linear slope is 0.99 at the pixel level, indicating a high confidence in the quality of generated data products. Quantitative and visual comparisons witness PANDA-China's superiority against other NTL datasets in its significantly longer NTL dynamics, higher temporal consistency, and better correlations with socioeconomics (built-up areas, gross domestic product, population) characterizing the most relevant indicator in different development phases. The PANDA-China product provides an unprecedented opportunity to trace nighttime light dynamics in the past four decades.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-06-03T01:32:03.253Z","creation":"2026-04-22T03:13:27.978Z"},"accession":"S-EPMC11035565","cross_references":{"pubmed":["38649344"],"doi":["10.1038/s41597-024-03223-1"]}}