eo-toolbox
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Agenda
Jupyter Notebook
SNAP
UDS - Universal Desktop Service
Google Earth Engine
Access data
Region of Interest
Copernicus dataspace ecosystem
Other data sources for Sentinel missions
Planet
Digital Elevation Model (DEM)
Satellites
Sentinel-2
Sentinel-2 (with GEE)
Sentinel-1
Landsat
Analysis
Classification
In situ
data preparation
In situ
split calibration-validation
In situ
split calibration-validation with complex strategies (
optional
)
Random Forest Classification
Validation
Extract random points
Zonal Statistics
Composites
Reclassify raster by defined intervals
Other
QGIS Color Map
Plotting using rasterio
eo-toolbox
Classification
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Classification
In situ
data preparation
1. Apply 10m inner buffer to each polygons
2. Add polygon area
3. Add pixel count
4. Remove unnecessary
in situ
polygons
Plot histogram
In situ
split calibration-validation
1. Open
in situ
data prepared
2. Add number of pixel of each class for each polygons
3. Split polygons in training and validation datasets
In situ
split calibration-validation with complex strategies (
optional
)
1. Open
in situ
data prepared
2. Get “pixel ratio” for each polygons
3. Assign sampling design strategy for each polygons
Random Forest Classification
1. Prepare classification features associated to
in situ
data
2. Train the Random Forest
3. Predict the rest of the image
4. Reclassify classification
5. Filter classification with moving window
6. Write classification products into GeoTIFF files
Validation
1. Prepare data
2. Confusion Matrix
3. Accuracy metrics
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