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Merge pull request #43 from sentinel-hub/toolbox-demonstrator-d43
Adds demonstrator notebooks
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‎AgriDataValue/README.md

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@@ -6,14 +6,33 @@ In this folder we show some examples how the toolbox can be used to perform task
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## Table of Contents
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* data_search.ipynb: querying Earth Observation data
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* eo-data.ipynb: retrieving EO data
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* indicators.ipynb: access to EO derived data for various existing indicators (e.g. Soil Moisture)
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* ndvi_example.ipynb: example how to retrieve NDVI data using Sentinel Hub Process API
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* cdse_example.ipynb: how to set up toolkit to work with CDSE
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* Air pollution example: retrieving, visualising and analysing NO2 air pollution with the data from the Sentinel-5P satellite
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* Burned-up area example: an example of how Sentinel Hub can be used together with eo-learn and eo-grow to train and deploy a model for burned up area detection
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* Cloud shadows projection example:
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* [./data_search.ipynb](data_search.ipynb): querying Earth Observation data
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* [./eo-data.ipynb](eo-data.ipynb): retrieving EO data
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* [./indicators.ipynb](indicators.ipynb): access to EO derived data for various existing indicators (e.g. Soil Moisture)
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* [./ndvi_example.ipynb](ndvi_example.ipynb): example how to retrieve NDVI data using Sentinel Hub Process API
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* [./cdse_example](cdse_example.ipynb): how to set up toolkit to work with CDSE
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* [Air pollution](./Air pollution example): retrieving, visualising and analysing NO2 air pollution with the data from the Sentinel-5P satellite
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* [Burned up area](./Burned-up_area) : an example of how Sentinel Hub can be used together with eo-learn and eo-grow to train and deploy a model for burned up area detection
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* [Cloud shadows projection](./cloud-shadows-projection): Example showing how to use the toolbox to compute sloud shadows. Showing advanced capabilities ot the toolbox for complex processing tasks.
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### Examples focusing on ADV datasets and integration:
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* [Satellite imagery data](./satellite_imagery_data.ipynb): example showing how to retrieve satellite imagery over a pilot AoI for longitudal agricultural monitoring.
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* [Drone data](./drone_data_w_imagery.ipynb): Example showing how the drone data integrated into the toolbox can be retrieved alongside satellite imagery
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* [Historical indcators](./historical_indicators_statistics): Example showing how historical indicators can be retrieved and processed over pilot areas
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* [Projection indicators](./projection_indicators_statistics.ipynb): Example showing how 5-year and 50 year projection indicators can be retrieved and processed
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## Acknowledgements

‎AgriDataValue/drone_data_w_imagery.ipynb

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‎AgriDataValue/evalscripts.py

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evalscript_baresoil = """
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//VERSION=3
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//Author: Domagoj Korais
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function setup() {
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return {
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input: [{
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bands: ["B02", "B03", "B04", "B05", "B07", "B08", "B11"],
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units: "reflectance"
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}],
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output: {
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id:"baresoil",
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bands: 1,
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sampleType: "AUTO"
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}
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}
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}
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function evaluatePixel(sample) {
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var num_NBSI = ((sample.B11 + sample.B04)-(sample.B08 + sample.B02));
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var den_NBSI = ((sample.B11 + sample.B04)+(sample.B08 + sample.B02));
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var NBSI = num_NBSI/den_NBSI;
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var NDVI = index(sample.B08, sample.B04);
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var NDVI_RE3 = index(sample.B08, sample.B07);
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var CL_RE = sample.B07 / sample.B05 - 1;
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var is_bare_soil = NDVI <= -0.1 ? false : predict(NBSI, NDVI, NDVI_RE3, CL_RE) > 0.5;
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return [is_bare_soil]
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}
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var DecisionTreeClassifier = function() {
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var findMax = function(nums) {
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var index = 0;
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for (var i = 0; i < nums.length; i++) {
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index = nums[i] > nums[index] ? i : index;
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}
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return index;
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};
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this.predict = function(features) {
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var classes = new Array(2);
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if (features[1] <= 0.2880808413028717) {
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if (features[2] <= -0.001884871511720121) {
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if (features[2] <= -0.01514277933165431) {
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if (features[0] <= -0.05856157839298248) {
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if (features[3] <= 0.4919503927230835) {
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if (features[1] <= 0.22891760617494583) {
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classes[0] = 109;
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classes[1] = 75;
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} else {
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classes[0] = 77;
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classes[1] = 5;
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}
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} else {
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if (features[3] <= 0.6081486344337463) {
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classes[0] = 47;
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classes[1] = 128;
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} else {
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classes[0] = 22;
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classes[1] = 311;
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}
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}
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} else {
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if (features[1] <= 0.23794686794281006) {
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if (features[3] <= 0.24695706367492676) {
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classes[0] = 51;
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classes[1] = 132;
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} else {
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classes[0] = 352;
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classes[1] = 3683;
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}
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} else {
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if (features[3] <= 0.4766134023666382) {
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classes[0] = 91;
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classes[1] = 57;
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} else {
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classes[0] = 209;
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classes[1] = 1278;
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}
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}
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}
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} else {
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if (features[0] <= -0.01485772943124175) {
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if (features[3] <= 0.46598660945892334) {
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if (features[1] <= 0.23513969033956528) {
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classes[0] = 80;
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classes[1] = 30;
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} else {
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classes[0] = 83;
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classes[1] = 4;
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}
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} else {
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classes[0] = 38;
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classes[1] = 42;
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}
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} else {
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if (features[1] <= 0.24381835758686066) {
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if (features[0] <= 0.017081347294151783) {
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classes[0] = 37;
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classes[1] = 60;
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} else {
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classes[0] = 72;
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classes[1] = 437;
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}
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} else {
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if (features[3] <= 0.4962599277496338) {
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classes[0] = 84;
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classes[1] = 43;
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} else {
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classes[0] = 23;
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classes[1] = 66;
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}
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}
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}
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}
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} else {
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if (features[2] <= 0.012518306728452444) {
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if (features[0] <= 0.011857263278216124) {
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if (features[3] <= 0.44026511907577515) {
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if (features[0] <= -0.025940910913050175) {
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classes[0] = 155;
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classes[1] = 2;
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} else {
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classes[0] = 122;
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classes[1] = 13;
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}
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} else {
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classes[0] = 58;
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classes[1] = 38;
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}
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} else {
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if (features[1] <= 0.21599827706813812) {
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classes[0] = 44;
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classes[1] = 86;
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} else {
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if (features[3] <= 0.4378824234008789) {
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classes[0] = 145;
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classes[1] = 37;
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} else {
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classes[0] = 57;
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classes[1] = 55;
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}
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}
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}
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} else {
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if (features[3] <= 0.4603644013404846) {
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if (features[1] <= 0.21943768113851547) {
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if (features[0] <= 0.021366839297115803) {
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classes[0] = 131;
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classes[1] = 4;
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} else {
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classes[0] = 58;
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classes[1] = 25;
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}
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} else {
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if (features[3] <= 0.42150408029556274) {
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classes[0] = 982;
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classes[1] = 19;
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} else {
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classes[0] = 237;
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classes[1] = 14;
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}
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}
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} else {
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classes[0] = 84;
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classes[1] = 26;
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}
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}
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}
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} else {
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if (features[2] <= -0.047297170385718346) {
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if (features[1] <= 0.40251147747039795) {
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if (features[3] <= 0.6912856698036194) {
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classes[0] = 114;
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classes[1] = 25;
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} else {
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if (features[1] <= 0.3502514660358429) {
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if (features[3] <= 0.7766227126121521) {
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classes[0] = 29;
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classes[1] = 74;
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} else {
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classes[0] = 64;
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classes[1] = 516;
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}
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} else {
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if (features[0] <= -0.027021611109375954) {
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classes[0] = 84;
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classes[1] = 236;
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} else {
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classes[0] = 72;
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classes[1] = 30;
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}
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}
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}
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} else {
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if (features[1] <= 0.4671569764614105) {
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if (features[0] <= -0.05327927693724632) {
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if (features[2] <= -0.0706191249191761) {
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classes[0] = 43;
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classes[1] = 41;
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} else {
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classes[0] = 99;
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classes[1] = 40;
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}
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} else {
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if (features[1] <= 0.4272315502166748) {
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classes[0] = 67;
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classes[1] = 18;
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} else {
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classes[0] = 121;
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classes[1] = 9;
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}
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}
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} else {
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if (features[3] <= 1.3283718824386597) {
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classes[0] = 137;
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classes[1] = 16;
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} else {
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if (features[2] <= -0.08075670152902603) {
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classes[0] = 75;
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classes[1] = 5;
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} else {
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classes[0] = 453;
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classes[1] = 4;
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}
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}
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}
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}
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} else {
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if (features[1] <= 0.3474765121936798) {
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if (features[3] <= 0.5934110283851624) {
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if (features[2] <= 0.003244615043513477) {
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if (features[0] <= -0.03575599752366543) {
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classes[0] = 330;
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classes[1] = 39;
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} else {
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classes[0] = 360;
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classes[1] = 138;
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}
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} else {
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if (features[2] <= 0.01407763920724392) {
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classes[0] = 607;
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classes[1] = 57;
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} else {
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classes[0] = 2837;
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classes[1] = 48;
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}
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}
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} else {
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if (features[2] <= -0.021189325489103794) {
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if (features[0] <= 0.008428129367530346) {
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classes[0] = 113;
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classes[1] = 342;
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} else {
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classes[0] = 82;
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classes[1] = 90;
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}
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} else {
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if (features[2] <= 0.010304238181561232) {
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classes[0] = 290;
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classes[1] = 266;
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} else {
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classes[0] = 142;
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classes[1] = 28;
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}
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}
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}
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} else {
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if (features[2] <= 0.00489223818294704) {
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if (features[1] <= 0.4410252124071121) {
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if (features[0] <= -0.034201690927147865) {
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classes[0] = 1971;
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classes[1] = 653;
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} else {
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classes[0] = 2273;
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classes[1] = 234;
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}
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} else {
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if (features[3] <= 1.035286784172058) {
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classes[0] = 1973;
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classes[1] = 195;
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} else {
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classes[0] = 9665;
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classes[1] = 144;
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}
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}
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} else {
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if (features[2] <= 0.016115683130919933) {
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if (features[1] <= 0.4877214878797531) {
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classes[0] = 2990;
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classes[1] = 137;
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} else {
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classes[0] = 1448;
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classes[1] = 18;
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}
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} else {
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if (features[0] <= -0.04386013746261597) {
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classes[0] = 7991;
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classes[1] = 130;
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} else {
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classes[0] = 7008;
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classes[1] = 44;
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}
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}
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}
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}
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}
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}
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return findMax(classes);
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};
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};
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function predict(NBSI, NDVI, NDVI_RE3, CL_RE){
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var clf = new DecisionTreeClassifier();
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return [1/(1+Math.exp(-1*clf.predict([NBSI, NDVI, NDVI_RE3, CL_RE])))];
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}
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"""
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evalscript_ndvi = """
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//VERSION=3
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function setup() {
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return {
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input: [
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{
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bands: ["B04", "B08", "dataMask"],
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}
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],
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output: [
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{
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id: "bands",
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bands: ["NDVI"],
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sampleType: SampleType.FLOAT32
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},
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{
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id: "dataMask",
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bands: 1
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}
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] };
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}
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function evaluatePixel(samples) {
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return {
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bands: [index(samples.B08, samples.B04)],
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dataMask: [samples.dataMask]
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};
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}
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"""

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