README.md

metadata

license: apache-2.0
title: tree_canopy
sdk: docker
emoji: 💻
colorFrom: blue
colorTo: yellow
short_description: tree segmentation

🌳 Tree Segmentation from Aerial Imagery (Solafune Platform)

📌 Overview

This project focuses on segmenting individual trees and groups of trees from high-resolution aerial imagery.
The dataset comes from the Solafune platform and includes two segmentation targets:

• individual_tree
• group_of_trees

Each image is 1204 × 1204 pixels, captured at ground resolutions ranging from 20 cm to 80 cm per pixel.
This means one pixel corresponds to 0.2 m to 0.8 m of ground area.

A key challenge in this dataset is the extremely high object density:
➡️ ~4000 tree instances per image, requiring robust segmentation techniques and efficient processing strategies.

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🌱 Problem Description

Given aerial RGB images, the task is to segment tree regions into:

• Individual trees
• Groups of trees

Challenges include:

• Very high object density
• Varying tree scales
• Large variation in resolution (20 cm to 80 cm)
• Shadows, illumination differences
• Large image size (1204×1204)

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🧪 Approaches Implemented

Two separate solutions were developed:

1. YOLO11-Seg (with SAHI slicing)
2. Mask2Former (with Swin-Tiny backbone + SAHI)

Both solutions use SAHI (Slicing Aided Hyper Inference) to efficiently handle large images and to improve small-object performance.

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🔷 1. Mask2Former Implementation

🛠 Model Details

• Backbone: Swin Transformer Tiny
• Architecture: Mask2Former (query-based segmentation)
• Pretraining: COCO-Segments
• Training Resolution: 480 × 480 (sliced)
• Why sliced training?
  • Full 1204×1204 images have ~4000 tree instances
  • Mask2Former uses query-based detection, where:
    • #queries ≈ #objects
  • Using thousands of queries significantly slows down cross-attention
  • Training on smaller slices reduces both:
    • Compute cost
    • GPU memory usage
    • Attention complexity

🌐 Data Augmentations

Standard Albumentations:

• Horizontal / vertical flip
• Random rotate
• Color jitter
• Gaussian noise

Custom augmentations:

• Synthetic shadows added to mimic aerial lighting variations
• Noise injection for individual_tree class (high sample count)
  • Avoids oversampling imbalance
  • Preserves dataset richness

📊 Performance

Metric	Score
mAP@75	0.40

Mask2Former performs significantly better because of its strong global reasoning and segmentation-focused architecture.

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🔷 2. YOLO11-Seg Implementation

🛠 Model Details

• Model: YOLO11x-Seg
• Image Handling: SAHI slicing
• Reason:
  • YOLO struggles with extremely dense small objects on large images
  • Memory constraints prevent full-resolution training on 1204×1204
  • SAHI allows per-slice detection, making training feasible

📊 Performance

Metric	Score
mAP@75	0.11

YOLO11 underperforms in this scenario because:

• Dense small objects are harder for YOLO
• Segmentation masks become noisy in crowded environments
• Attention-free architecture limits instance separation

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🧠 Key Insights

• High-density segmentation (>4000 objects) requires query-based or transformer-style models.
• Mask2Former handles dense tree structures far better than YOLO.
• SAHI is essential for both training and inference:
  • Reduces GPU memory load
  • Improves small-object detection
  • Accelerates training on large images
• Custom shadow and noise augmentations helped improve model robustness.
• Lower-resolution images (20-80 cm) require scale-robust architectures.

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🎉 Results Summary

Model	Backbone	mAP@75	Notes
Mask2Former	Swin-Tiny	0.40	Best performer, strong with dense segments
YOLO11x-Seg	CSP-based	0.11	Struggles with extremely dense small objects

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🚀 Future Improvements

• Use Swin-Large or Swin-Base for deeper feature representation
• Train Mask2Former with larger query capacity
• Add class-specific weighting to handle imbalance between individual_tree and group_of_trees