Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification

• URL: http://arxiv.org/abs/2607.24160v1
• Date: Mon, 27 Jul 2026 08:37:49 GMT
• Title: Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification
• Abstract summary: Calibrated EcoTreeFuseNet-Plus is a tree-neural probability-fusion framework.<n>The proposed model achieved an accuracy of 0.8000, a macro F1-score of 0.7768, a balanced accuracy of 0.7903, and an MCC of 0.7903.<n>Results demonstrate a reliable discrimination-calibration trade-off for small-sample, fine-grained ecological classification.
• Score: 5.365444289459681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes. Existing studies often rely on standalone tree ensembles or generic neural networks, although fine-grained ecological classes frequently exhibit overlapping spectral, topographic, and structural characteristics. Many frameworks also provide limited protection against stacking leakage, insufficient probability calibration, weak minority-class evaluation, and little evidence of stability across repeated data splits. To address these limitations, this study proposes Calibrated EcoTreeFuseNet-Plus, a tree-neural probability-fusion framework that combines out-of-fold tree probabilities, EcoFuseNet-V2 outputs, validation-selected meta-learning, and post-hoc temperature scaling. Raster values from six LiDAR-derived terrain and canopy variables and two hyperspectral vegetation indices were extracted at coordinate-based reference locations. Quality control removed 26 samples with missing elevation and one sample with non-finite NDWI, producing 1,833 complete records across 29 vegetation and non-vegetation classes. On the held-out test set, the proposed model achieved an accuracy of 0.8000, a macro F1-score of 0.7768, a balanced accuracy of 0.7903, and an MCC of 0.7903. Calibration reduced the expected calibration error from 0.3866 to 0.0651 without changing class predictions. Five-seed evaluation yielded a macro F1-score of 0.7717 +/- 0.0112, indicating stable performance across repeated splits. The results demonstrate a reliable discrimination-calibration trade-off for small-sample, fine-grained ecological classification.

Related papers

• Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan [22.126775072131608]
  Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS)<n>This work makes three main contributions: (i) it frames Christmas tree plantation mapping as a distinct rare-target semantic segmentation problem; (ii) it introduces a Hard Negative Mining (HNM) strategy to improve discrimination against confusing background patterns; and (iii) it evaluates the proposed framework across complementary levels, including supervised testing, temporal transfer, and large-scale validation.
  arXiv  Detail & Related papers  (2026-08-27T15:58:20Z)
• Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors [69.38367341895632]
  Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and harvesting along the Portuguese Atlantic coast.<n>We develop and evaluate a machine-learning framework to predict harmful algal bloom occurrence using exclusively satellite-derived predictors.
  arXiv  Detail & Related papers  (2026-07-08T18:14:24Z)
• Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops [3.466303200807006]
  Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually.<n>This paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification.
  arXiv  Detail & Related papers  (2026-07-05T06:04:20Z)
• MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears [0.0]
  Malaria diagnosis from blood smear microscopy is a critical challenge in global health AI.<n>Three compounding failure modes prevent reliable clinical deployment of existing deep learning systems.<n>We present MalariAI, a two-stage decoupled framework that addresses all three failure modes in a unified pipeline.
  arXiv  Detail & Related papers  (2026-07-01T03:27:04Z)
• A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation [78.66324246922831]
  This work presents a pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations.<n>The dataset contains 67,337 patches from five European countries and uses a reduced taxonomy of ten crop classes plus background.<n>A four-level U-Net with Group Normalization was trained using 10 Sentinel-2 spectral bands and a composite loss combining class-weighted cross-entropy and Dice loss.
  arXiv  Detail & Related papers  (2026-05-30T11:20:29Z)
• Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification [0.9607593117607193]
  Random forests construct each tree with a different, randomised representation of the feature space.<n>Their uniform voting cannot correct errors in regions where trees with incorrect representations outnumber correct ones.<n>This paper proposes using the structural pattern of each tree's decision path as a per-sample reliability signal.
  arXiv  Detail & Related papers  (2026-05-20T05:15:09Z)
• Differentiable latent structure discovery for interpretable forecasting in clinical time series [38.473526928012724]
  We introduce StructGP, a continuous-time multi-task Gaussian process that couples differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.<n>We propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns.
  arXiv  Detail & Related papers  (2026-04-30T14:59:50Z)
• Evaluating the Limitations of Protein Sequence Representations for Parkinson's Disease Classification [6.24518052314374]
  Protein sequences constitute fundamental and widely available source of biological information.<n>Discriminative capacity for complex disease classification remains unclear.<n>Best-performing configuration (ProtBERT +) achieves F1-score of 0.704 +/- 0.028 and ROC-AUC of 0.748 +/- 0.047.
  arXiv  Detail & Related papers  (2026-04-13T03:54:24Z)
• Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction [4.750682174151462]
  We present a fully reproducible machine learning framework for 5-year overall survival prediction in breast cancer.<n>We integrate clinical variables with high-dimensional transcriptomic and copy-number alteration (CNA) features from the METABRIC cohort.<n>Performance was assessed using time-dependent area under the ROC curve (AUC), average precision (AP), calibration curves, Brier score, and bootstrapped 95 percent confidence intervals.
  arXiv  Detail & Related papers  (2026-02-25T07:20:43Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• Variational phylogenetic inference with products over bipartitions [48.2982114295171]
  We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density of the resulting distribution over trees.<n>Our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable.
  arXiv  Detail & Related papers  (2025-02-21T00:06:57Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.