metadata

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®). 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 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

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

The model has been trained with

Bias, Risks, and Limitations

###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

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Training regime: {{ training_regime | default("[More Information Needed]", true)}}

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

Metrics

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Results

Summary

Technical Specifications [optional]

Model Architecture and Objective

Citation [optional]

BibTeX:

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APA:

Contact

ai-challenge@ign.fr