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 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
- 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 partition (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 of 0 and a standart deviation of 1 channel wise. 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)}}
- 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.
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Evaluation
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]
BibTeX:
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APA:
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