A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation
Abstract
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's interpretation.
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BibTeX
@misc{alhammadi2026framework,
title = {{A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation}},
author = {Hind Yousif Alhammadi and Isam Mashhour Al Jawarneh},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.05625},
eprint = {2610.05625},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.05625},
}
RIS
TY - UNPB
TI - A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation
AU - Hind Yousif Alhammadi
AU - Isam Mashhour Al Jawarneh
PY - 2026
DA - 2026/10/24
PB - arXiv
DO - 10.48550/arxiv.2610.05625
UR - https://arxiv.org/abs/2610.05625
AB - 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's interpretation.
ER -