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FireMM-IR: An Infrared-Enhanced Multi-Modal Large Language Model for Comprehensive Scene Understanding in Remote

Jinghao Cao1,2, Xiajun Liu3, Rui Xue4

  • 1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China.

Sensors (Basel, Switzerland)
|January 28, 2026
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This study introduces FireMM-IR, a multi-modal large language model for advanced forest fire monitoring. It uses infrared and visual data fusion for better fire detection and understanding in remote sensing imagery.

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Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Forestry

Background:

  • Traditional forest fire monitoring models in remote sensing imagery are limited to detection or segmentation.
  • These models struggle with complex fire dynamics, contextual reasoning, and cross-modal interpretation.
  • Multi-modal large language models (MLLMs) offer potential for holistic scene understanding by integrating semantics, spatial distribution, and language.

Purpose of the Study:

  • To develop a multi-modal large language model (MLLM) for pixel-level scene understanding in remote-sensing forest-fire imagery.
  • To enhance forest fire monitoring by integrating infrared and visual data for improved detection and analysis.
  • To enable instruction-driven analysis and reasoning for wildfire scenes.

Main Methods:

  • Introduced FireMM-IR, an MLLM incorporating an infrared-enhanced classification module for fusing infrared and visual modalities.
  • Designed a mask-generation module guided by language-conditioned segmentation tokens for accurate instance mask generation.
  • Implemented a class-aware memory mechanism for multi-scale fire feature learning and contextual consistency.
  • Constructed the FireMM-Instruct corpus (83,000 RGB-IR pairs with aligned annotations and descriptions).

Main Results:

  • FireMM-IR achieved superior performance in pixel-level segmentation of forest-fire imagery.
  • Demonstrated strong results in instruction-driven captioning and reasoning tasks.
  • Maintained competitive performance on image-level benchmarks.
  • Infrared-optical fusion and instruction-aligned learning were identified as crucial for physically grounded wildfire scene understanding.

Conclusions:

  • FireMM-IR advances forest fire monitoring by enabling holistic scene understanding beyond traditional perception models.
  • The fusion of infrared and visual data significantly improves the detection of fire intensity and hidden ignition areas, even under smoke.
  • Instruction-aligned learning with multi-modal data is key for developing physically grounded and interpretable wildfire analysis systems.