Fire - Fuel - Vegetation - Image - Segmentation - 1.0 Open - Source Model: Precise Segmentation of Leaf Vegetation to Aid Wildfire Fuel Assessment

Fire Fuel Vegetation Image Segmentation 1.0

Developed by markrodrigo

RGB three-channel binary segmentation model for leaf vegetation, suitable for shrub/tree scenarios, supporting multi-domain applications such as wildfire fuel assessment

Image Segmentation Supports Multiple Languages#Vegetation Binary Segmentation #Wildfire Fuel Assessment #Enhanced U-Net Architecture

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Release Time : 9/28/2024

Model Overview

Deep learning model based on enhanced U-Net architecture, specifically designed for vegetation image segmentation, capable of generating high-precision binary masks, supporting cross-domain applications like medical image analysis and wildfire prevention

Model Features

Cross-domain Adaptability

Original design includes potential for medical image analysis, extendable to agriculture, environmental monitoring and other fields

High-precision Segmentation

Utilizes semantic segmentation technology with Rand Index reaching 0.92-0.96, accurately handling regional vegetation color differences

Transfer Learning Support

Built-in LoRA adapter functionality for rapid fine-tuning to new scenarios

Professional Data Training

Trained with 3,840 manually annotated samples and synthetic augmented data, validation set accounting for 1/3

Model Capabilities

Vegetation Binary Segmentation

2D Area Measurement

Wildfire Risk Assessment

Land Cover Analysis

Medical Image Processing

Power Line Clearance

Harmful Weed Identification

Camouflage Target Detection

Use Cases

Forestry Management

Forest Fire Prevention

Analyze coniferous forest fuel distribution to generate fire risk level overlay layers

Practical application case in Colorado forest area

Pest Monitoring

Identify dead vegetation areas caused by moderate to severe pest infestation

Agricultural Applications

Weed Identification

Precisely segment harmful weeds in farmland

Environmental Assessment

Land Cover Change Monitoring

Track vegetation coverage area trends over time

🚀 Image Segmentation Model for Vegetation and Related Applications

This model focuses on image segmentation, especially for vegetation, with various potential applications in different fields such as biology, agriculture, and medical.

🚀 Quick Start

The model can be loaded using Keras or TensorFlow. Here are the example codes to load the model:

💻 Usage Examples

Basic Usage

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

# Or another way to load the model
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()

Advanced Usage

For more details about saving and loading models in TensorFlow, you can refer to TensorFlow official tutorial.

✨ Features

RGB Binary Segmentation: It can perform Red Green Blue (RGB) binary segmentation of particular vegetation in leaf, shrub, or tree applications.
Multiple Applications: Has potential in medical applications, and can be used for various other purposes like vegetation 2D area calculations, wildfire/fire fuel analysis, land cover change detection, medical applications, line clearing, noxious weeds detection, environmental assessments, and camouflage object detection.
Transfer Learning Opportunities: There are opportunities to transfer learn or further fine-tune the model with techniques like LoRA.

📦 Model Information

Model Details

Property	Details
Model Type	Keras / TensorFlow .h5 supervised model
Training Data	3840 256x256 RGB images and corresponding 256x256 binary mask images. ~ 1/3 allocated to validation. Separate test sets by latitude and region considering target species' regional color variations.
Metrics	Rand Index: .92 - .96 (geographic latitude and regional vegetation color variations)
Model Architecture	Modified U-Net
Model Developer	Mark Rodrigo
Associated Code	https://github.com/mprodrigo - coming soon

Model Input / Output

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

Model Applications

Original Design: Likely designed for medical applications originally.
Training Specificity: Originally trained to specific species and semantically segmented for accuracy.
Data Sources: Data sources are proprietary via hand drawn masked samples, and some source data is extrapolated to synthetic data.
Novel Applications: Require specific vegetation imagery.

Forest Fire Fuel Application

It is a LoRA adapter of the base segmentation vegetation model. For example, it can be used in scenarios like moderate to extreme beetle kill areas in Colorado coniferous forest for fire fuel analysis.