The FLAIR models are a sophisticated collection of semantic segmentation models specifically designed to classify land cover within high-resolution aerial imagery. While they excel at data from the French territory, they can provide useful insights for other high spatial resolution images as well. This blog outlines how to get started using the FLAIR models, with a focus on the FLAIR-INC_rgbie_15cl_resnet34-unet model.
Table of Contents
Model Information
The FLAIR-INC_rgbie_15cl_resnet34-unet model has been tailored for tasks involving:
- Training Dataset: Uses the FLAIR-INC dataset, which is a specially crafted collection of aerial images with detailed labels.
- Input Modalities: This model utilizes RGBIE images, which include natural colors and infrared data, along with elevation information.
- Model Architecture: Based on U-Net architecture with a Resnet-34 encoder, designed to perform semantic segmentation effectively.
- Target Classes: It classifies into 15 distinct land cover classes such as buildings, water, and various types of vegetation.
Uses
The model was originally crafted to classify aerial images from the BD ORTHO® product. While it can be applied to different imagery, its efficiency is optimized for BD ORTHO specifications:
- Radiometric processing tailored for the French territory.
- Images should ideally be of similar characteristics to those initially used for training to ensure accuracy.
How to Get Started with the Model
To use the FLAIR model, you can visit the GitHub repository. It contains detailed instructions for fine-tuning and executing prediction tasks. Here’s a brief rundown of the initial steps:
- Clone the repository to your local machine.
- Set up your Python environment as specified in the repository.
- Download the necessary datasets and preprocess them according to the guidelines.
- Begin training your model by following the training procedures outlined.
Training Details
The training process for the FLAIR model is pivotal in ensuring high performance in segmentation tasks. Here are the key elements:
- Training Data: A total of 218,400 patches of 512 x 512 pixels were utilized.
- Normalization: Input images were normalized to have a mean of zero and a standard deviation of one for each channel.
- Training Hyperparameters: The model was trained over 200 epochs with detailed hyperparameters provided in the repository.
Troubleshooting
When working with the FLAIR models, you may run into some challenges. Here are some troubleshooting ideas:
- Inconsistent results: Ensure that your input images have been pre-processed in the same manner as those used for training. Inconsistent radiometric properties can lead to unpredictable outcomes.
- Deployment issues: If the model fails to deploy successfully, double-check the environment setup as per the guidelines on the GitHub page.
- Performance drops: If you experience a decrease in performance metrics, consider fine-tuning the model with your specific dataset, especially if your images differ significantly from the training images.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
