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z_;*)Yt5bZAQOcwAbP3ehrVwY@@DB-0-<|BbrfTFMOECEF68=p!0G7A^b@l%F1*?EJ ZYzK6Hx4&Q_G{}Mg(&7qY)gp$0{|7p7v3vjk literal 0 HcmV?d00001 diff --git a/docs/source/examples.md b/docs/source/examples.md index 7a886dd..3e3ae90 100644 --- a/docs/source/examples.md +++ b/docs/source/examples.md @@ -1,11 +1,39 @@ # Examples + +## Mapping Bamboo forests in Thailand + + +The goal in this example is to map bamboo forests in Thailand using UAV RGB data. +Here is the image with overlaping training points (the different colors correspond to different bamboo species): + +```{image} ./_static/examples/drone_train.png +:alt: Image with training GT points +:class: centered-image +:width: 600px +:align: center +``` + +And here is the image with overlaping test points (train and test set are separated to avoid spatial auto-correlation): + +```{image} ./_static/examples/drone_test.png +:alt: Image with test GT points +:class: centered-image +:width: 600px +:align: center +``` + +This image is fed through a ViT base DINO encoder (with default encoding parameters) before fitting a random forest (RF) classifier on the obtained features. +We achieve 71% accuracy on this dataset alone. If we fit a RF directly on RGB data, we achieve only 45% accuracy. This shows that the encoder has produced meaningfull features used by the classifier afterwards. + + + ## Mapping Cameroon forest types with Sentinel 2 data The goal in this example is to map different forest types using multispectral Sentinel 2 data. -We have a raster and ... points that have been labelled by photo-interpretation. +We have a raster and points labelled by photo-interpretation: ```{image} ./_static/examples/original_points.png :alt: Original image with GT points @@ -13,12 +41,15 @@ We have a raster and ... points that have been labelled by photo-interpretation. :width: 600px :align: center ``` +One this image, the dark blue points correspond to open forests, green to swap forests and light blue to dense forests. +This dataset was randomly split into 80-20% between a train and a test set to learn a RF classifier. +The image was either fed into a deep learning (DL) backbone (ViT base DINO) or not. Here is a recap of the random forest accuracy given different pre- and post-processing of the encoder's features. | Using a DL encoder | Pre-processing | Post-processing | Accuracy | | :----------- | :----------- | :-------------- |---------: | -| No | No | No | | +| No | No | No |0.88 | | Yes | No | No |0.75 | | No | 3D PCA | No |**0.93** | | Yes | 3D PCA | No |0.86 | @@ -52,8 +83,4 @@ And with pre-processing: ``` -## Mapping Bamboo forests in Thailand - - - -## Mapping Land Cover in La Réunion +<!-- ## Mapping Land Cover in La Réunion --> -- GitLab