cs.AIOct 4, 2026

A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation

Authors: Hind Yousif Alhammadi, Isam Mashhour Al Jawarneh

Organizations: Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, UAE · Department of Computer Science, University of Sharjah, P.O.Box. 27272 Sharjah, UAE

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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 interpretation.

Figures & tables

Satellite/BandsRedNIRThermal
Landsat 73 (0.63–0.69 µm), 30 m4 (0.77–0.90 µm), 30 m6 (10.40–12.50 µm), 60 m
Landsat 84 (0.64–0.67 µm), 30 m5 (0.85–0.88 µm), 30 m10/11 (10.6–11.19 µm), 100 m
Landsat 94 (0.64–0.67 µm), 30 m5 (0.85–0.88 µm), 30 m10/11 (10.6–11.19 µm), 100 m
TABLE I: Summary of the Landsat spectral bands used in this study.
Fig. 1: Flowchart showing the automated LST workflow.
Metadata FeildUse in equations
RADIANCE_MULT_BAND_XML in Equation 1
RADIANCE_ADD_BAND_XAL in Equation 1
K1_CONSTANT_BAND_XK1 in Equation 2
K2_CONSTANT_BAND_XK2 in Equation 2
TABLE II: Metadata field required to be used in equations.
Fig. 2: Parameters of LST Tool.
Fig. 3: Obtained LST map for RAK using Landsat 8.
Fig. 4: LST summary report generated using LLM.
# ScenesManual TimeTool TimeTime Saved
111m 33s3m 52s7m 41s
223m 06s4m 31s18m 35s
334m 39s7m 36s27m 03s
446m 12s9m 3s37m 09s
557m 45s11m 16s46m 29s
TABLE III: Comparison between manual and automated LST processing time.

Explore similar work

CardsList

  1. Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

    Apr 29, 2026Zeyuan Wang, Dongyang Hou, Cheng Yang +10Remote SensingGeospatial Reasoning

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China. · School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China. · Hunan Key Laboratory of Land Resources Evaluation and Utilization, Hunan Provincial Institute of Land and Resources Planning, Changsha 410013, China.

  2. HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities

    Aug 7, 2026Jesus Guerrero, Isaac Corley, Leon Najafirad +2Land Surface TemperatureRemote Sensing

    University of Texas at San Antonio · San Antonio, TX, USA · Taylor Geospatial +1

  3. RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

    Jun 11, 2024Wenjia Xu, Zijian Yu, Boyang Mu +3Remote SensingGeospatial Reasoning

    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China · City University of HongKong, Hong Kong 999077, China · School of Geographic Sciences, Hunan Normal University, Changsha 410081, China