vegetation-image-segmentation-wildfire-fuel-1.0 Open Source Model - Leaf Vegetation Segmentation, Applicable to Multiple Scenarios such as Wildfire Assessment and Medical Applications

Vegetation Image Segmentation Wildfire Fuel 1.0

Developed by markrodrigo

A binary segmentation model for RGB three-channel leaf vegetation, suitable for shrub or tree scenes, with potential medical applications, supporting multi-scenario applications such as wildfire fuel assessment

Image Segmentation Supports Multiple Languages#Vegetation Binary Segmentation #Wildfire Fuel Assessment #Medical Imaging Adaptation

Downloads 73

Release Time : 9/28/2024

Model Overview

A semantic segmentation model based on an improved U-Net architecture, specifically designed for vegetation image segmentation, applicable in agriculture, forest fire prevention, medical imaging analysis, and other fields

Model Features

Multi-scenario Applicability

The model is trained on specific species and can adapt to shrub or tree scenes, with potential medical applications

High-Precision Segmentation

Uses semantic segmentation technology to ensure accuracy, with a Rand Index of 0.92-0.96

Transfer Learning Support

Supports transfer learning and fine-tuning extensions such as LoRA, enabling rapid adaptation to new scenarios

Professional Data Support

Trained on proprietary datasets derived from hand-drawn mask samples and synthetic data

Model Capabilities

Vegetation Image Segmentation

2D Area Measurement

Wildfire Fuel Assessment

Land Cover Change Monitoring

Medical Imaging Analysis

Camouflaged Object Detection

Use Cases

Forest Fire Prevention

Fuel Distribution Analysis

Analyze the distribution of combustible vegetation in forests

Can be overlaid with forest service fire risk level data for analysis

Deadwood Detection

Detect deadwood caused by moderate to severe beetle infestations

Fuel distribution analysis in Colorado coniferous forests

Agricultural Applications

Weed Identification

Identify weed distribution in farmland

Toxic Weed Identification

Detect the distribution of harmful plants

Medical Applications

Medical Imaging Analysis

Potential medical image segmentation applications

🚀 Image Segmentation Model for Vegetation and Related Applications

This model focuses on the red, green, blue (RGB) binary segmentation of specific vegetation, with potential applications in multiple fields such as biology, agriculture, and medical. It offers opportunities for transfer learning and fine - tuning, and has shown good performance in accuracy evaluation.

🚀 Quick Start

To use this model, you can refer to the following code examples:

Keras

import keras
model = keras.models.load_model('../model/image-segmentation-vegetation-1.0.keras')
model.summary()

import keras
loaded_model = keras.models.load_model('/home/phantom/Projects/agverde/data/product/Agverde/z1/model/image-segmentation-vegetation-1.0.h5')
loaded_model.summary()

TensorFlow

For more information on saving and loading models in TensorFlow, please refer to TensorFlow Tutorial.

✨ Features

RGB Binary Segmentation: Performs red, green, blue binary segmentation of particular vegetation in leaf, shrub, or tree applications.
Medical Potential: Likely has medical applications as it was originally designed for such purposes.
Species - Specific Training: Originally trained on specific species and semantically segmented for accuracy.
Transfer Learning: Offers opportunities for transfer learning or further fine - tuning with methods like LoRA.
Multiple Applications: Can be used in various fields such as vegetation 2D area calculations, wildfire/fire fuel analysis, land cover change monitoring, medical applications, line clearing, noxious weeds detection, environmental assessments, and camouflage object detection.

📦 Installation

There is no specific installation information provided in the original document.

💻 Usage Examples

Basic Usage

The basic usage of the model in Keras is as shown above in the Quick Start section.

Advanced Usage

For advanced usage, you can consider transfer learning or fine - tuning the model with LoRA. However, specific code examples are not provided in the original document.

📚 Documentation

Model Information

Model Name: Model Information - fire火 - fuel薪 - vegetation植被 - image - segmentation - 1.0
Model Architecture: Modified U - Net
Input/Output:
- Input: 256, 256, 3
- Output: 256, 256, 1

Evaluation / Accuracy of Target Vegetation

The Rand Index of the model is between .92 - .96, considering geographic latitude and regional vegetation color variations.

Training and Validation data

The training data consists of 3840 256x256 RGB images and corresponding 256x256 binary mask images.
Approximately 1/3 of the data is allocated to validation.
The test sets are separated by latitude and region due to the regional color variations of the target species.

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

Keras 3.6.0
Tensorflow 2.16.2

🔧 Technical Details

The model is a Keras / Tensorflow .h5 supervised model. The data sources are proprietary via hand - drawn masked samples, and some source data is extrapolated to synthetic data.