markrodrigo/

vegetation-image-segmentation-wildfire-fuel

Model Information - fire火-fuel薪-vegetation植被-image-segmentation-1.0

• Red Green Blue (RGB) binary segmentation of particular vegetation in leaf. Shrub or tree applications.
  

• Likely medical applications as the model was originally designed for.
  

• Originally trained to specific species. Semantically segmented for accuracy.
  

• Keras / Tensorflow .h5 supervised model.
  
• Opportunities are to transfer learn or further fine-tune with LoRA, etc.
  

• Data sources are proprietary via hand drawn masked samples.
  

• Some extrapolation of source data to synthetic data.
  

• Novel applications – require specific vegetation imagery.
  

• Other applications - Vegetation 2D area calculations. Wildfire / fire fuel. Land Cover change. Medical. Line clearing. Noxious weeds. Environmental assessments. Camouflage object detection.
  

Forest Fire Fuel
LoRA adapter of base segmentation vegetation model.
Example - Model Not currently available.
Moderate to extreme beetle kill.
Colorado coniferous forest fuel source.

USFS fire severity for area.

LoRA adapted fuel layer overlaying USFS fire severity.

Model Input / Output Overview:

• Input: 256, 256, 3
• Output: 256, 256, 1

Training Source Tile Example

Training Source Tile Histogram

Further Reference

Example Code

Evaluation / Accuracy of Target Vegetation

Training and Validation data

• 3840 256x256 RGB images and corresponding 256x256 binary mask images
• ~ 1/3 allocated to validation
• Separate test sets by latitude and region. Target species has regional color variations.

Training hyperparameters

• learning_rate: 1e-04
• train_batch_size: 8
• eval_batch_size: 3
• distributed_type: multi-GPU
• num_devices: 2
• batch steps: 60
• eval steps: 9
• optimizer: Adam
• num_epochs: 8

Training results

| Training Loss | Epoch  | Training Accuracy |
|:-------------:|:------:|:-----------------:|
| 0.4718        |   1    | 0.8227            |
| 0.3869        |   2    | 0.8328            |
| 0.3770        |   3    | 0.8403            |
| 0.2557        |   4    | 0.8562            |
| 0.2432        |   5    | 0.8587            |
| 0.0856        |   6    | 0.9557            |
| 0.0338        |   7    | 0.9870            |
| 0.0303        |   8    | 0.9891            |

Framework versions