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/
          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 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 patches) .
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

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

Although the model can be applied to other type of very high spatial earth observation images, it was initially developed to tackle the problem of classifying aerial images acquired on the French Territory. The product called (BD ORTHO®) has its own spatial and radiometric specifications. The model is not intended to be generic to other type of very high spatial resolution images but specific to BD ORTHO images. As a result, the prediction produced by the model would be all the better as the user images are similar to the original ones.

Radiometry of input images : The BD ORTHO input images are distributed in 8-bit encoding format per channel. When 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 frequent and are mainly due to : the date of acquisition of the aerial survey (april to november), the spatial domain (equivalent to a french department administrative division) and downstream radimetric processing. By construction (sampling 75 domains) the model is robust to these shifts, and can be applied to any images of the (BD ORTHO® product).

Land Cover classes of prediction : The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes(See the FLAIR dataset page for details). However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor labelling (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 : (1) should appear at 0 in the logits (2) should never predicted in the Argmax.

Bias, Risks, Limitations and Recommendations

Using the model on input images with other spatial resolution : The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained with fixed scale conditions.All patches used for training are derived from aerial images of 0.2 meters spatial resolution. Only flip and rotate augmentation were performed during the training process.
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 was trained with aerial images of the (BD ORTHO® product) 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 was 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.

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

218 400 patches of 512 x 512 pixels were used to train the FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model. The train/validation split was performed patchwise to obtain a 80% / 20% distribution between train and validation. Annotation was performed at the zone level (~100 patches per zone). Spatial independancy between patches is guaranted as patches from the same zone were assigned to the same set (TRAIN or VALIDATION). Here are the number of patches used for train and validation : | TRAIN set | 174 700 patches | | VALIDATION set | 43 700 patchs |

Training Procedure

Preprocessing [optional]

For traning the model, input normalization was performed so as the input dataset has a mean=0 and a standard deviation = 1 channel wise. We used the statistics of TRAIN+VALIDATION for input normalization. It is recommended that the user apply the same type of input normalization. Here are the statistics of the TRAIN+VALIDATIOn set :

Modalities	Mean (Train + Validation)	Std (Train + Validation)
Red Channel (R)	105.08	52.17
Green Channel (G)	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

Training Hyperparameters

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

Model architecture: Unet (implementation from the Segmentation Models Pytorch library
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]

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.

TRAIN loss	VALIDATION loss
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Evaluation

Testing Data, Factors & Metrics

Testing Data

The evaluation was performed on a TEST set of 31750 patches that are independant from the TRAIN and VALIDATION patches. They represent 15 spatio-temporal domains. The TEST set corresponds to the reunion of the TEST set of scientific challenges FLAIR#1 and FLAIR#2. See the FLAIR challenge page for more details.

The choice of a separate TEST set instead of cross validation was made to be coherent with the FLAIR challenges. However the metrics for the Challenge were calculated on 12 classes and the TEST set acordingly. As a result the Snow class is unfortunately absent from the TEST set.

Metrics

With the evaluation protocol, the FLAIR-INC_RVBIE_resnet34_unet_15cl_norm have been evaluated to OA= 76.37% and mIoU=54.71%. The following table give the class-wise metrics :

Modalities	IoU (%)	Fscore (%)	Precision (%)	Recall (%)
building	82.63	90.49	90.26	90.72
pervious surface	53.24	69.48	68.97	70.00
impervious surface	74.17	85.17	86.28	84.09
bare soil	60.40	75.31	80.49	70.75
water	87.59	93.38	93.16	93.61
coniferous	46.35	63.34	63.52	63.16
deciduous	67.45	80.56	77.44	83.94
brushwood	30.23	46.43	63.55	36.58
vineyard	82.93	90.67	91.35	89.99
herbaceous vegetation	55.03	70.99	70.59	71.40
agricultural land	52.01	68.43	59.18	81.12
plowed land	40.84	57.99	68.28	50.40
swimming_pool	48.44	65.27	81.62	54.37
snow	00.00	00.00	00.00	00.00
greenhouse	39.45	56.57	45.52	74.72
-----------------------	----------	---------	---------	---------
average	54.72	67.60	69.35	67.66

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

Confusion Matrix (precision)	Confusion Matrix (recall)
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Results

Summary

Technical Specifications [optional]

Model Architecture and Objective

Citation [optional]

BibTeX:

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

APA:

Contact

ai-challenge@ign.fr