Land Art as a Big-Data Climate Sensor
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Computer Science > Machine Learning
arXiv:2609.13182 (cs)
[Submitted on 29 Jul 2026]
Title:Land Art as a Big-Data Climate Sensor
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Abstract:Robert Smithson's 1970 land artwork Spiral Jetty, located in the north arm of Utah's Great Salt Lake, has alternated between submergence and exposure during severe lake decline. We analyze 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips spanning every year and calendar month from 1984 to 2025. A 14-feature complexity signature combines Shannon entropy, multiscale permutation entropy, fractal dimension, lacunarity, gray-level co-occurrence texture, intensity statistics, and ImageNet-pretrained ResNet50 features. These measurements are compared with a 42-year monthly climate and hydrology panel from NASA GISTEMP, USGS NWIS, Open-Meteo, and the Global Carbon Budget. Bootstrap analysis shows that Shannon entropy is a weak proxy and does not support an earlier small-sample claim of positive correlation with global temperature. By contrast, coarse-scale permutation entropy and mean intensity track lake elevation strongly, with Spearman correlations of 0.85 to 0.88 and 95 percent confidence intervals excluding zero. The third principal component of the ResNet50 embeddings emerges without supervision as an AI climate axis, correlating 0.86 with cumulative CO2 and -0.83 with lake elevation. Image complexity leads lake stage by about three years, with Pearson r = 0.58 at lag +3 and a 95 percent confidence interval of 0.40 to 0.73. STL decomposition reveals a non-monotonic trend that rises from 1984 to 2015 and declines sharply thereafter as the lake approaches record-low elevations. Partial correlations controlling for month and sensor confirm robustness to seasonal and sensor effects. These results refine the art-as-thermometer metaphor into an art-as-leading-indicator-of-hydrological-state interpretation. The dataset, feature pipeline, and analysis code are released as a public benchmark.
| Comments: | 20 pages, 7 figures, 3 tables |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2609.13182 [cs.LG] |
| (or arXiv:2609.13182v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13182
arXiv-issued DOI via DataCite
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Submission history
From: Sean Kalaycioglu Dr. [view email][v1] Wed, 29 Jul 2026 22:15:09 UTC (1,342 KB)
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