README.md

---
license: etalab-2.0
tags:
- segmentation
- pytorch
- aerial imagery
- landcover
- IGN
model-index:
- name: FLAIR-INC_RVBIE_unetresnet34_15cl_norm
  results:
  - task:
      type: semantic-segmentation
    dataset:
      name: IGNF/FLAIR#1-TEST
      type: earth-observation-dataset
    metrics:
    - name: mIoU
      type: mIoU
      value: 54.7168
    - name: Overall Accuracy
      type: OA
      value: 76.3711
    - name: Fscore
      type: Fscore
      value: 67.6063
    - name: Precision
      type: Precision
      value: 69.3481
    - name: Recall
      type: Recall
      value: 67.6565
      
    - name: IoU Buildings
      type: IoU
      value: 82.6313
    - name: IoU Pervious surface
      type: IoU
      value: 53.2351
    - name: IoU Impervious surface
      type: IoU
      value: 74.1742
    - name: IoU Bare soil
      type: IoU
      value: 60.3958
    - name: IoU Water
      type: IoU
      value: 87.5887
    - name: IoU Coniferous
      type: IoU
      value: 46.3504
    - name: IoU Deciduous
      type: IoU
      value: 67.4473
    - name: IoU Brushwood
      type: IoU
      value: 30.2346
    - name: IoU Vineyard
      type: IoU
      value: 82.9251
    - name: IoU Herbaceous vegetation
      type: IoU
      value: 55.0283      
    - name: IoU Agricultural land
      type: IoU
      value: 52.0145
    - name: IoU Plowed land
      type: IoU
      value: 40.8387   
    - name: IoU Swimming pool
      type: IoU
      value: 48.4433    
    - name: IoU Greenhouse
      type: IoU
      value: 39.4447
      
pipeline_tag: image-segmentation
---

# FLAIR model collection
The FLAIR models is a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial ortho-images ([BD ORTHO®](https://geoservices.ign.fr/bdortho)). 
The distributed pre-trained model differ in their :
- input modalities : RVB (true colours), RVBI (true colours + infrared), RVBIE (true colours + infrared + elevation)
- model architecture : U-Net with a Resnet-34 encoder, Deeplab
- target class nomenclature : 12 or 15 land cover classes
- dataset for training : [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) or the increased version of this dataset FLAIR-INC.


# FLAIR FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model
The general characteristics of this specific model *FLAIR-INC_RVBIE_resnet34_unet_15cl_norm* are :
* RVBIE images (true colours + infrared + elevation)
* U-Net with a Resnet-34 encoder
* 15 class nomenclature [building,pervious_surface,impervious_surface,bare_soil,water,coniferous,deciduous,brushwood,vineyard,herbaceous,agricultural_land,plowed_land,swimming pool,snow,greenhouse]



## Model Informations

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- **Repository:** https://github.com/IGNF/FLAIR-1-AI-Challenge
- **Paper [optional]:** https://arxiv.org/pdf/2211.12979.pdf
- **Developed by:** IGN
- **Compute infrastructure:** 
    - software: python, pytorch-lightning
    - hardware: GENCI, XXX
- **License:** : Apache 2.0


  
## Uses
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The model has been trained with


## Bias, Risks, and Limitations

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###Spatial resolution of input images :
The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with fixed scale conditions. All patches used for training are serived from aerial images of 0.2 meters spatial resolution. 
No data augmentation method concerning scale change was used during training. The user should pay attention that generalization issues can occur while applying this model to images that have different spatial resolutions.








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### Recommendations

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## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Metrics

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### Results

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#### Summary

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Contact
ai-challenge@ign.fr