Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984)

The American satellite reconnaissance program (Keyhole imagery) is serving as a significant data source for geoscience research because of its high-resolution and early temporal coverage, while lack of spatial and temporal description of its uneven distribution could hinder researchers from selecting/accessing appropriate the Keyhole images. Here we introduce a global grid-based dataset that organizes declassified U.S. Keyhole imagery (1960-1984) for direct reuse, built on a global equal-area sinusoidal grid. This dataset standardizes scene metadata and provides indicators designed to inform study design and data integration: coverage count (how often a place was imaged), unique acquisition dates (temporal sampling richness), first/last observation year (temporal bounds), observation span (duration), peak observation year and a three-year window (temporal concentration), resolution class (C1-C3), temporal-coverage class across five five-year intervals, and resolution-coverage class (A-G) for multi-scale availability. This dataset enables users to quickly locate usable scenes, assess temporal suitability, combine historical images with modern satellites, and determine which non-free images to purchase if free images were unsuitable for their research.

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Publication Details

Journal
Scientific Data
Published
2026-02-17
DOI
https://doi.org/10.1038/s41597-026-06866-4
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
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article

Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984)

HuanJun Liu, Hao Li, Jiaheng Wang, Jinyu Sun et al.
Scientific Data
Remote-Sensing Image Classification
article

Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984)

HuanJun Liu, Hao Li, Jiaheng Wang, Jinyu Sun, Xinle Zhang, Tao Wang, Mingyuan Deng, Mulin Shan
article en

Abstract

The American satellite reconnaissance program (Keyhole imagery) is serving as a significant data source for geoscience research because of its high-resolution and early temporal coverage, while lack of spatial and temporal description of its uneven distribution could hinder researchers from selecting/accessing appropriate the Keyhole images. Here we introduce a global grid-based dataset that organizes declassified U.S. Keyhole imagery (1960-1984) for direct reuse, built on a global equal-area sinusoidal grid. This dataset standardizes scene metadata and provides indicators designed to inform study design and data integration: coverage count (how often a place was imaged), unique acquisition dates (temporal sampling richness), first/last observation year (temporal bounds), observation span (duration), peak observation year and a three-year window (temporal concentration), resolution class (C1-C3), temporal-coverage class across five five-year intervals, and resolution-coverage class (A-G) for multi-scale availability. This dataset enables users to quickly locate usable scenes, assess temporal suitability, combine historical images with modern satellites, and determine which non-free images to purchase if free images were unsuitable for their research.

Scientific DataVol. 13(1)
Liaocheng University (CN), Chinese Academy of Sciences (CN), Northeast Institute of Geography and Agroecology (CN), Jilin Agricultural University (CN)
Openalex Percentile: Top 58%
Remote-Sensing Image Classification
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Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960–1984) — HuanJun Liu, Hao Li, et al. · Scientific Data (2026) | TGRS Research Map | TGRS