M3FNet: Multi-modal multi-temporal multi-scale data fusion network for tree species composition mapping
Bibliographic record
Abstract
Accurate estimation and mapping of t ree s pecies c omposition (TSC) is crucial for sustainable forest management. Recent advances in Light Detection and Ranging (lidar) technology and the availability of moderate spatial resolution, surface reflectance time series passive optical imagery offer scalable and efficient approaches for automated TSC estimation. In this research we develop a novel deep learning framework, M3F-Net (Multi-modal, Multi-temporal, and Multi-scale Fusion Network), that integrates multi-temporal Sentinel-2 (S2) imagery and single photon lidar (SPL) data to estimate TSC for nine common species across the 630,000-hectare Romeo Malette Forest in Ontario, Canada. A dual-level alignment strategy combines (i) superpixel-based spatial aggregation to reconcile mismatched resolutions between high-resolution SPL point clouds (>25 pts/m 2 ) and coarser S2 imagery (20 m), and (ii) a grid-based feature alignment that transforms unordered 3D point cloud features into structured 2D representations, enabling seamless integration of spectral and structural information. Within this aligned space, a multi-level Mamba-Fusion module jointly models multi-scale spatial patterns and seasonal dynamics through selective state-space modelling, efficiently capturing long-range dependencies while filtering redundant information. The framework achieves an R 2 score of 0.676, outperforming existing point cloud-based methods by 6% in TSC estimation. For leading species classification, our results are 6% better in terms of weighted F1, using either the TSC-based method or the standalone leading species classification method. Addition of seasonal S2 imagery added a 10% R 2 gain compared to the SPL-only mode. These results underscore the potential of fusing multi-modal and multi-temporal data with deep learning for scalable, high-accurate TSC estimation, offering a robust tool for large-scale management applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".