作为大数据气候传感器的地景艺术
Land Art as a Big-Data Climate Sensor
- Işık University（伊什克大学）
- Toronto Metropolitan University（多伦多都会大学）
- York University（约克大学）
- Dr. Robot Inc.（Dr. Robot 公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将史密森地景艺术《螺旋码头》的卫星影像作为大数据气候传感器，通过复杂度特征分析揭示影像复杂度领先湖泊水位约三年，并发现AI特征可无监督追踪CO2与湖泊变化。
AI中文摘要：
罗伯特·史密森1970年的地景艺术作品《螺旋码头》位于犹他州大盐湖北部支臂，在湖泊严重消退期间反复经历淹没与暴露。我们分析了1984年至2025年间覆盖每一年和每个日历月的1,744个配准的Landsat 4-9和Sentinel-2影像块。一个14特征复杂度签名结合了香农熵、多尺度排列熵、分形维数、空隙度、灰度共生矩阵纹理、强度统计以及ImageNet预训练的ResNet50特征。这些测量与来自NASA GISTEMP、USGS NWIS、Open-Meteo和全球碳预算的42年月度气候和水文面板数据进行了比较。Bootstrap分析表明，香农熵是一个弱代理指标，不支持早期小样本声称的与全球温度的正相关。相比之下，粗尺度排列熵和平均强度强烈追踪湖泊高程，Spearman相关系数为0.85至0.88，95%置信区间排除零。ResNet50嵌入的第三主成分在无监督情况下浮现为一个AI气候轴，与累积CO2的相关性为0.86，与湖泊高程的相关性为-0.83。影像复杂度领先湖泊水位约三年，在滞后+3处Pearson r=0.58，95%置信区间为0.40至0.73。STL分解揭示了一个非单调趋势，从1984年到2015年上升，此后随着湖泊接近创纪录低水位而急剧下降。控制月份和传感器的偏相关证实了对季节和传感器效应的稳健性。这些结果将艺术作为温度计隐喻精炼为艺术作为水文状态先行指标的解释。数据集、特征流程和分析代码作为公共基准发布。
英文摘要：
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.
