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Multimodal Tree Crown Detection and Carbon Stock Estimation from Remote Sensing Imagery

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Manh-Tan Doan
Quoc-Ngoc Ly
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter explores the use of multimodal deep learning for tree crown detection and carbon stock estimation in forests. The study introduces HAF R-CNN, a model that combines RGB and canopy height model (CHM) data using cross-attention fusion to improve detection accuracy in diverse forest conditions. The research also presents a pipeline for tree-level carbon stock estimation, which integrates structural and spectral features with a Random Forest regressor. Evaluation on benchmark datasets demonstrates the model's superior performance compared to existing methods. The study highlights the potential of remote sensing and machine learning for scalable carbon estimation, particularly in open-canopy forests, while also discussing challenges in dense stands. The findings underscore the importance of accurate tree crown detection and the need for further research in structurally diverse forests.
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previous chapter A Small Language Model and Domain-Specific Resources for Vietnamese Public Services
next chapter A Classificatory Topos: Refining Evolving Knowledge in Multi-agent Learning Systems
Title
Multimodal Tree Crown Detection and Carbon Stock Estimation from Remote Sensing Imagery
Authors
Manh-Tan Doan
Quoc-Ngoc Ly
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-59-7

Electronic ISBN: 978-981-9549-60-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4960-3_3
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