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Lightweight Multimodal Fusion for Urban Tree Health and Ecosystem Services.

Abror Buriboev1, Djamshid Sultanov2, Ilhom Rahmatullaev3,4

  • 1Department of AI-Software, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a lightweight deep learning model for real-time urban tree health assessment. The framework accurately monitors tree condition and estimates oxygen and carbon dioxide exchange using fused sensor data.

Keywords:
carbon sequestration estimationedge-efficient AIenvironmental intelligenceimage–sensor integrationmulti-task neural networksmultimodal fusionoxygen production modelingtree health monitoringurban ecology

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Area of Science:

  • Environmental Science
  • Computer Science
  • Urban Ecology

Background:

  • Urban expansion necessitates advanced methods for monitoring tree health and ecosystem services.
  • Current manual tree inspections are subjective and inefficient.
  • Urban trees are vital for air quality and climate regulation.

Purpose of the Study:

  • To develop a scalable, real-time framework for assessing urban tree health.
  • To estimate daily oxygen production and carbon dioxide absorption by urban trees.
  • To overcome limitations of manual tree monitoring.

Main Methods:

  • A lightweight multimodal deep-learning framework fusing RGB imagery with environmental and biometric sensor data.
  • Utilized an EfficientNet-B0 vision encoder with MBConv and squeeze-and-excitation attention.
  • Implemented a three-task learning setup for simultaneous classification and regression.

Main Results:

  • Achieved 92.03% accuracy in tree health classification.
  • Reduced regression error for O2 (MAE = 1.28) and CO2 (MAE = 1.70).
  • Demonstrated superior performance compared to unimodal and multimodal baselines.

Conclusions:

  • The proposed multimodal framework offers accurate and efficient urban tree health assessment.
  • The model is suitable for deployment on edge devices for real-time monitoring.
  • This approach enhances the understanding and management of urban ecosystem services.