diff --git a/docs/source/examples.md b/docs/source/examples.md index 3e3ae903b92013fbc69c3db5b2a9a90749bb49c4..a4a46d81956d0d372018a4f913c8b2b0905880cb 100644 --- a/docs/source/examples.md +++ b/docs/source/examples.md @@ -83,4 +83,60 @@ And with pre-processing: ``` -<!-- ## Mapping Land Cover in La Réunion --> +## Mapping Land Cover change in La Reunion island + +The objective is to characterize land cover change in La Reunion island based on the analysis of aerial IGN ortho-photography from 1950 and 2022. + +Data : +- The collection of historical Orthophotos (1950) is accessible from the following website : https://geoservices.ign.fr/bdorthohisto +The historical map is divided into a mosaic of 130 grayscale tiles, i.e. in a 1-band rasters, ranging from 0 to 255 to represent the light intensity. +The resolution of each image is 50cm/pixel. + +- The collection of recent Orthophotos (2022) is accessible from the following website : https://geoservices.ign.fr/bdortho +The recent map is divided into a mosaic of 130 color tiles, i.e. in RGB (Red, Green, Blue) 3-bands rasters where each band uses pixel value ranging from 0 to 255 to represent the corresponding color intensity. The resolution of each image is 20cm/pixel. + +- Preliminary step : a data transformation for the comparison 1950 / 2022 : +The recent images resolution (2022) is transformed from 20cm/pixel to 50cm/pixel using QGIS in order to obtain the same resolution as the historical images (1950) . In addition, a conversion from color (RGB or 3-bands) to grayscale (1-band) is applied using QGIS plugin OTB (OTB > feature extraction > radiometric indices >set band 3 2 1 and choice Bi index (Brightness index) . Thanks to these two steps the comparison between both maps can be done pixel by pixel. + +- Method +Each image is fed through a ViT base DINO encoder before fitting a random forest (RF) classifier on the obtained features. + + +- The Land Cover classes : +A photo-identification is achieved on 30 randomly selected tiles for each dataset. The variability inherent to each class is accounted for by the identification of 10 polygones per landcover class. +The different landcover classes are the following : +- Agricultural +- Low vegetation +- Forest +- Shade +- Urban + + +Here is the figure showing the classification results for 1950 + +```{image} ./_static/examples/classif_1950.png +:alt: Image 1 +:class: centered-image +:width: 600px +:align: center +``` + + +Here is the figure showing the classification results for 2022 + +```{image} ./_static/examples/classif_2022.png +:alt: Image 2 +:class: centered-image +:width: 600px +:align: center +``` + + +Here is the figure showing the land cover changes between 1950 and 2022 + +```{image} ./_static/examples/landcover_change.png +:alt: Image 3 +:class: centered-image +:width: 600px +:align: center +```