---
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 models differ in their :
* dataset for training : [**FLAIR** dataset] (https://huggingface.co/datasets/IGNF/FLAIR) or the increased version of this dataset **FLAIR-INC** (x 3.5 patch size)  .
* input modalities : **RGB** (natural colours), **RGBI** (natural colours + infrared), **RGBIE** (natural colours + infrared + elevation)
* model architecture : **resnet34_unet** (U-Net with a Resnet-34 encoder), **deeplab**
* target class nomenclature : **12cl** (12 land cover classes) or **15cl** (15 land cover classes) 


# FLAIR FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model

The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** are :
* RGBIE 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

<!-- Provide the basic links for the model. -->

- **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
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Although the model can be applied to other type of very high spatial eart observation images, it was initially developed to tackle the problem of classifying aerial images acquired on the French Territory.
The product called ([BD ORTHO®](https://geoservices.ign.fr/bdortho)) has its own spatial and radiometric specifications. The prediction produced by the model would be all the better as the user task is similar 


**Radiometry of input images** :
The input images are distributed in 8-bit encoding format per channel. or traning the model, input normalization was performed (see section **Traing Details**). 
It is recommended that the user apply the same type of input normalization while inferring the model.

**Multi-domain model** :
The FLAIR-INC dataset that was used for training is composed of 75 radiometric domains. In the case of aerial images, domain shifts are due : the date of the aerial survey (april to november), spatial domain (equivalent to a french department administrative division) and downstream radimetric processing.
By construction the model is robust to theses shifts, and can be applied to any images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)).

**Land Cover classes of prediction** :
The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes(See the [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) page for details).
However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor label quantity (Clear cut (15)) were deasctivated during training.
As a result, the logits produced by the model are of size 19x1, but class 15,16,17 and 19 should appear at 0 in the logits. And labels 15,16,17 and 19 never predicted in the argmax.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

**Using the model on input images with other spatial resolution** :
The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with fixed scale conditions. All patches used for training are derived 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.

**Using the model for other remote sensing sensors** :
The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with aerial images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)) that encopass very specific radiometric image processing. 
Using the model on other type of aerial images or satellite images may imply the use of transfer learning or domain adaptation techniques.

**Using the model on other spatial areas** :
The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained on patches reprensenting the French Metropolitan territory. 
The user should be aware that applying the model to other type of landscapes may imply a drop in model metrics.  


{{ bias_risks_limitations | default("[More Information Needed]", true)}}



### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

## How to Get Started with the Model

Use the code below to get started with the model.


{{ get_started_code | default("[More Information Needed]", true)}}

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
218 400 patchs of 512 x 512 pixels were used to train the model. 
The train/validation split was performed patchwise to obtain a 80% / 20% distribution. Spatial independancy between patches is guaranted : neighbouring patches are assigned to the same partition :
* Train set : 174 700 patches
* Validation set : 43 700 patchs


{{ training_data | default("[More Information Needed]", true)}}

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

For traning the model, input normalization was performed so as the input dataset has a mean of 0 and a standart deviation of 1. For this model here are the statistics of the TRAIN+VALIDATION partition. It is recommended that the user apply the same type of input normalization.
Input normalization was performed 
| Modalities              | Mean (Train + Validation)       |Std    (Train + Validation)     |
| ----------------------- | ----------- |----------- |
| Red Channel (R)         | 105.08	    |52.17       |
| Green Channel (V)       | 110.87      |45.38       |
| Blue Channel (B)        | 101.82	    |44.00       |
| Infrared Channel (I)    | 106.38	    |39.69       |
| Elevation Channel (E)   | 53.26       |79.30       |


{{ preprocessing | default("[More Information Needed]", true)}}


#### 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 -->

* Model architecture: Unet (implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet)
* Encoder : Resnet-34 pre-trained with ImageNet
* Augmentation :
  * VerticalFlip(p=0.5)
  * HorizontalFlip(p=0.5)
  * RandomRotate90(p=0.5)
* Input normalization (mean=0 | std=1):
  * norm_means: [105.08, 110.87, 101.82, 106.38, 53.26]
  * norm_stds: [52.17, 45.38, 44, 39.69, 79.3]
* Seed: 2022
* Batch size: 10
* Number of epochs : 200
* Early stopping : patience 30 and val_loss as monitor criterium 
* Optimizer : SGD
* Schaeduler : mode = "min", factor = 0.5, patience = 10, cooldown = 4, min_lr = 1e-7
* Learning rate : 0.02
* Class Weights : [1-building: 1.0 , 2-pervious surface: 1.0 , 3-impervious surface: 1.0 , 4-bare soil: 1.0 , 5-water: 1.0 , 6-coniferous: 1.0 , 7-deciduous: 1.0 , 8-brushwood: 1.0 , 9-vineyard: 1.0 , 10-herbaceous vegetation: 1.0 , 11-agricultural land: 1.0 , 12-plowed land: 1.0 , 13-swimming_pool: 1.0 , 14-snow: 1.0 , 15-clear cut: 0.0 , 16-mixed: 0.0 , 17-ligneous: 0.0 , 18-greenhouse: 1.0 , 19-other: 0.0]


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

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 16 V100 GPUs were requested ( 4 nodes, 4 GPUS per node).

FLAIR-INC_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corresponding val_loss=0.56. 

<!-- <img src="train_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width:200px;"/>-->
<!-- ![](train_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png)| ![](val_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png)-->

| <div style="width:290px">property</div> |<div style="width:290px">description</div>  |
| --------------------------------------- | ------------------------------------- |
| `<img src="train_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width:200px;"/> |`<img src="val_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width:200px;"/>  |


{{ speeds_sizes_times | default("[More Information Needed]", true)}}

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

{{ testing_data | default("[More Information Needed]", true)}}

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

{{ testing_metrics | default("[More Information Needed]", true)}}

### Results

{{ results | default("[More Information Needed]", true)}}

#### Summary

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## Technical Specifications [optional]

### Model Architecture and Objective

{{ model_specs | default("[More Information Needed]", true)}}


## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

{{ citation_bibtex | default("[More Information Needed]", true)}}

**APA:**

{{ citation_apa | default("[More Information Needed]", true)}}

## Contact
ai-challenge@ign.fr