ArticleScientific reports2025
Spatial heterogeneity of keyhole imagery coverage in China and imagery dataset cost estimation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Adaptive super-resolution UAV inspection and image perception strategy planning for distribution networks.Scientific reports · 2026Article
- Global 0.05° Grid-Based Dataset of Keyhole Imagery with Spatio-Temporal Indicators (1960-1984).Scientific data · 2026Article
- The Applicability of a Complete Archive of Keyhole Imagery for Land-Use Change Detection in China (1960-1984).Sensors (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
Keyhole imagery documented global surface from 1960 to 1980s and has contributed to earth surface change research, while evaluation of its' coverage spatial heterogeneity is rare. In this work the boundary vectors with attributes of all freely Keyhole images within China were obtained from USGS website to automatically investigate the spatial coverage characteristics using the ArcPy library in Python. Images were categorized into meter-level (C1), five-meter-level (C2), and ten-meter-level (C3). The resolution and spatial coverage distribution of Keyhole imagery at national and provincial levels were investigated using geostatistical and statistical methods. Combining coverage area of free imagery and the costs of non-free imagery, the cost of Keyhole datasets construction was calculated. The results indicated: (1) the coverage of C1, C2, and C3 across China was 58%, 95%, and 76%, respectively. The average number of coverages were 4.9, 4.5, and 3.6 times, respectively, with variation coefficients of 0.7, 1.3, and 1.3. All of C1, C2, and C3 imageries exhibited significant global and local spatial clustering characteristics. (3) The acquisition costs for datasets with triple coverage of C1, C2, and C3 imagery in China were 103, 103, and 23 thousand dollars, respectively. We demonstrated how the large data amount Keyhole imagery that could not analyzed manually were automatically reorganized using the metadata, to facilitate the spatial distribution and cost estimation analysis.
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