| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| base_model: |
| - Ultralytics/YOLO11 |
| pipeline_tag: object-detection |
| tags: |
| - yolos |
| - tree |
| - genus |
| - multichannel |
| - RGB+NIR+Height |
| --- |
| |
| # Urban Tree Detection and Genera Mapping with YOLOv11-L |
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| Urban Tree Detection and Genera Mapping with YOLOv11-L |
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| This repository provides YOLOv11-L object detection models trained for urban tree mapping using multichannel very-high-resolution remote sensing imagery, including RGB, near-infrared (NIR), and height information derived from airborne LiDAR. |
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| Two model variants are released: |
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| 1. Tree detection model for robust localization of individual urban trees |
| 2. Tree genera detection model that assigns genus labels to detected trees |
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| Both models are designed for large-scale urban environments and optimized for heterogeneous backgrounds, occlusions, and complex canopy structures. |
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| ## Overview |
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| Urban tree mapping is challenging due to heterogeneous backgrounds, occlusions, and varying tree structures. |
| To address these challenges, the models in this repository leverage both spectral and structural information by combining: |
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| - RGB orthophotos (visual structure and context) |
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| - Near-infrared (NIR) imagery (vegetation discrimination) |
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| - Height information derived from LiDAR (vertical structure of objects) |
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| - The height channel represents absolute object height, not only canopy height, and is used to distinguish trees from other vertical structures and to improve robustness in dense urban environments. |
|
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| ## Tree Detection Model |
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| 0. Tree |
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| ## Tree Genera Detection Model |
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| The genera model predicts bounding boxes with genus labels for common urban trees in Baden-Württemberg, Germany. |
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| Included genera: |
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| 0. Acer |
| 1. Aesculus |
| 2. Carpinus |
| 3. Coniferous |
| 4. Fagus |
| 5. Other Deciduous |
| 6. Platanus |
| 7. Prunus |
| 8. Quercus |
| 9. Tilia |
