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<p>This study assesses the practical implications of ChatGPT’s ability to identify hotspots by comparing its performance to Geographical Information System (GIS) software in detecting CO<sub>2</sub> sources and sinks observed by the Orbiting Carbon Observatory-3 (OCO-3) satellite. ChatGPT exhibited performance comparable to ArcGIS in both z-score statistics and spatial distribution patterns of XCO<sub>2</sub> hot and cold spots. The results generated by ChatGPT showed a strong correlation with ArcGIS-generated hotspots, demonstrating a z-score correlation coefficient of R²=0.82 and a cosine similarity score of 0.90. As multimodal artificial intelligence becomes more prevalent in earth monitoring, ChatGPT is expected to be a valuable tool for identifying CO<sub>2</sub> emission patterns, particularly for users who lack specialized GIS expertise. These findings establish a significant benchmark for ChatGPT’s potential in this field, offering a novel approach to identifying area-wide spatial patterns of CO<sub>2</sub> emissions compared to conventional GIS software.</p>

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Unlocking the potential of ChatGPT in detecting the XCO2 hotspot captured by orbiting carbon observatory-3 satellite

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月7日

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  • Young-Seok Hwang,
  • Stephan Schlueter,
  • Biswajeet Pradhan,
  • Jung-Sup Um

摘要

This study assesses the practical implications of ChatGPT’s ability to identify hotspots by comparing its performance to Geographical Information System (GIS) software in detecting CO2 sources and sinks observed by the Orbiting Carbon Observatory-3 (OCO-3) satellite. ChatGPT exhibited performance comparable to ArcGIS in both z-score statistics and spatial distribution patterns of XCO2 hot and cold spots. The results generated by ChatGPT showed a strong correlation with ArcGIS-generated hotspots, demonstrating a z-score correlation coefficient of R²=0.82 and a cosine similarity score of 0.90. As multimodal artificial intelligence becomes more prevalent in earth monitoring, ChatGPT is expected to be a valuable tool for identifying CO2 emission patterns, particularly for users who lack specialized GIS expertise. These findings establish a significant benchmark for ChatGPT’s potential in this field, offering a novel approach to identifying area-wide spatial patterns of CO2 emissions compared to conventional GIS software.