一种用于自动化多源卫星数据分析与基于LLM的报告生成的框架
A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation
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中文总结 AI 辅助
本文提出一个基于ArcPy的自动化工具，从Landsat 7/8/9数据中提取地表温度，结合LLM进行结果解释，显著缩短处理时间并提高精度，在拉斯海马验证了其有效性。
中文摘要 AI 辅助
本文介绍了使用ArcPy构建自动化ArcGIS Pro工具的工作流程，该工具用于从Landsat 7、8和9数据集中提取地表温度（LST）。该工具通过自动化辐射定标、基于NDVI的发射率校正和热转换的多步骤工作流程，消除了手动波段选择和重复栅格计算的需要。除了支持批处理和单场景处理外，该工具还集成了可选的大型语言模型（LLM），用于统计结果解释和报告生成。根据批处理规模的不同，该工具将处理时间从手动操作的约11-58分钟缩短至使用工具的约4-11分钟。我们使用阿联酋拉斯海马（RAK）的数据对该工具进行了测试，拉斯海马获得的LST范围约为25°C至50°C，展示了与RAK天气条件相符的准确LST映射。总之，我们的工具减少了人为错误，提高了热和环境遥感应用的处理精度和效率，并提供了基于LLM的交互式界面用于结果解释。
英文摘要
This paper presents the workflow for building an automated ArcGIS Pro tool using ArcPy to extract the Land Surface Temperature (LST) from Landsat 7, 8 and 9 datasets. The tool eliminates the need for manual band selection and repetitive raster computations by automating the multi-step workflow of radiometric calibration, NDVI-based emissivity correction, and thermal conversion. In addition to supporting batch and single-scene processing, the tool has an optional Large Language Model (LLM) for statistical result interpretation and reporting. Depending on batch size, the tool reduced the processing time from around 11-58 minutes when done manually to around 4-11 minutes using the tool. We tested the tool with data from Ras Al Khaimah (RAK) in the UAE, and the LST obtained for Ras Al Khaimah ranged from approximately 25C to 50C, demonstrating an accurate LST mapping compatible with the weather conditions of RAK. In summary, our tool reduces human errors and improves processing accuracy and efficiency for thermal and environmental remote sensing applications, in addition to providing an interactive LLM-based interface for result interpretation.
发表机构
- University of Sharjah（沙迦大学）
机构由 AI 辅助整理，请以论文原文为准。
