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Jul 2, 2025
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Adds demonstrator notebooks #43

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Adds demonstrator notebooks
veseln Jul 2, 2025
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35 changes: 27 additions & 8 deletions AgriDataValue/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,14 +6,33 @@ In this folder we show some examples how the toolbox can be used to perform task

## Table of Contents

* data_search.ipynb: querying Earth Observation data
* eo-data.ipynb: retrieving EO data
* indicators.ipynb: access to EO derived data for various existing indicators (e.g. Soil Moisture)
* ndvi_example.ipynb: example how to retrieve NDVI data using Sentinel Hub Process API
* cdse_example.ipynb: how to set up toolkit to work with CDSE
* Air pollution example: retrieving, visualising and analysing NO2 air pollution with the data from the Sentinel-5P satellite
* 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
* Cloud shadows projection example:
* [./data_search.ipynb](data_search.ipynb): querying Earth Observation data

* [./eo-data.ipynb](eo-data.ipynb): retrieving EO data

* [./indicators.ipynb](indicators.ipynb): access to EO derived data for various existing indicators (e.g. Soil Moisture)

* [./ndvi_example.ipynb](ndvi_example.ipynb): example how to retrieve NDVI data using Sentinel Hub Process API

* [./cdse_example](cdse_example.ipynb): how to set up toolkit to work with CDSE

* [Air pollution](./Air pollution example): retrieving, visualising and analysing NO2 air pollution with the data from the Sentinel-5P satellite

* [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

* [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.

### Examples focusing on ADV datasets and integration:

* [Satellite imagery data](./satellite_imagery_data.ipynb): example showing how to retrieve satellite imagery over a pilot AoI for longitudal agricultural monitoring.

* [Drone data](./drone_data_w_imagery.ipynb): Example showing how the drone data integrated into the toolbox can be retrieved alongside satellite imagery

* [Historical indcators](./historical_indicators_statistics): Example showing how historical indicators can be retrieved and processed over pilot areas

* [Projection indicators](./projection_indicators_statistics.ipynb): Example showing how 5-year and 50 year projection indicators can be retrieved and processed




## Acknowledgements
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258 changes: 258 additions & 0 deletions AgriDataValue/drone_data_w_imagery.ipynb
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354 changes: 354 additions & 0 deletions AgriDataValue/evalscripts.py
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evalscript_baresoil = """
//VERSION=3

//Author: Domagoj Korais

function setup() {
return {
input: [{
bands: ["B02", "B03", "B04", "B05", "B07", "B08", "B11"],
units: "reflectance"
}],
output: {
id:"baresoil",
bands: 1,
sampleType: "AUTO"
}
}
}

function evaluatePixel(sample) {
var num_NBSI = ((sample.B11 + sample.B04)-(sample.B08 + sample.B02));
var den_NBSI = ((sample.B11 + sample.B04)+(sample.B08 + sample.B02));

var NBSI = num_NBSI/den_NBSI;
var NDVI = index(sample.B08, sample.B04);
var NDVI_RE3 = index(sample.B08, sample.B07);
var CL_RE = sample.B07 / sample.B05 - 1;

var is_bare_soil = NDVI <= -0.1 ? false : predict(NBSI, NDVI, NDVI_RE3, CL_RE) > 0.5;
return [is_bare_soil]
}


var DecisionTreeClassifier = function() {

var findMax = function(nums) {
var index = 0;
for (var i = 0; i < nums.length; i++) {
index = nums[i] > nums[index] ? i : index;
}
return index;
};

this.predict = function(features) {
var classes = new Array(2);

if (features[1] <= 0.2880808413028717) {
if (features[2] <= -0.001884871511720121) {
if (features[2] <= -0.01514277933165431) {
if (features[0] <= -0.05856157839298248) {
if (features[3] <= 0.4919503927230835) {
if (features[1] <= 0.22891760617494583) {
classes[0] = 109;
classes[1] = 75;
} else {
classes[0] = 77;
classes[1] = 5;
}
} else {
if (features[3] <= 0.6081486344337463) {
classes[0] = 47;
classes[1] = 128;
} else {
classes[0] = 22;
classes[1] = 311;
}
}
} else {
if (features[1] <= 0.23794686794281006) {
if (features[3] <= 0.24695706367492676) {
classes[0] = 51;
classes[1] = 132;
} else {
classes[0] = 352;
classes[1] = 3683;
}
} else {
if (features[3] <= 0.4766134023666382) {
classes[0] = 91;
classes[1] = 57;
} else {
classes[0] = 209;
classes[1] = 1278;
}
}
}
} else {
if (features[0] <= -0.01485772943124175) {
if (features[3] <= 0.46598660945892334) {
if (features[1] <= 0.23513969033956528) {
classes[0] = 80;
classes[1] = 30;
} else {
classes[0] = 83;
classes[1] = 4;
}
} else {
classes[0] = 38;
classes[1] = 42;
}
} else {
if (features[1] <= 0.24381835758686066) {
if (features[0] <= 0.017081347294151783) {
classes[0] = 37;
classes[1] = 60;
} else {
classes[0] = 72;
classes[1] = 437;
}
} else {
if (features[3] <= 0.4962599277496338) {
classes[0] = 84;
classes[1] = 43;
} else {
classes[0] = 23;
classes[1] = 66;
}
}
}
}
} else {
if (features[2] <= 0.012518306728452444) {
if (features[0] <= 0.011857263278216124) {
if (features[3] <= 0.44026511907577515) {
if (features[0] <= -0.025940910913050175) {
classes[0] = 155;
classes[1] = 2;
} else {
classes[0] = 122;
classes[1] = 13;
}
} else {
classes[0] = 58;
classes[1] = 38;
}
} else {
if (features[1] <= 0.21599827706813812) {
classes[0] = 44;
classes[1] = 86;
} else {
if (features[3] <= 0.4378824234008789) {
classes[0] = 145;
classes[1] = 37;
} else {
classes[0] = 57;
classes[1] = 55;
}
}
}
} else {
if (features[3] <= 0.4603644013404846) {
if (features[1] <= 0.21943768113851547) {
if (features[0] <= 0.021366839297115803) {
classes[0] = 131;
classes[1] = 4;
} else {
classes[0] = 58;
classes[1] = 25;
}
} else {
if (features[3] <= 0.42150408029556274) {
classes[0] = 982;
classes[1] = 19;
} else {
classes[0] = 237;
classes[1] = 14;
}
}
} else {
classes[0] = 84;
classes[1] = 26;
}
}
}
} else {
if (features[2] <= -0.047297170385718346) {
if (features[1] <= 0.40251147747039795) {
if (features[3] <= 0.6912856698036194) {
classes[0] = 114;
classes[1] = 25;
} else {
if (features[1] <= 0.3502514660358429) {
if (features[3] <= 0.7766227126121521) {
classes[0] = 29;
classes[1] = 74;
} else {
classes[0] = 64;
classes[1] = 516;
}
} else {
if (features[0] <= -0.027021611109375954) {
classes[0] = 84;
classes[1] = 236;
} else {
classes[0] = 72;
classes[1] = 30;
}
}
}
} else {
if (features[1] <= 0.4671569764614105) {
if (features[0] <= -0.05327927693724632) {
if (features[2] <= -0.0706191249191761) {
classes[0] = 43;
classes[1] = 41;
} else {
classes[0] = 99;
classes[1] = 40;
}
} else {
if (features[1] <= 0.4272315502166748) {
classes[0] = 67;
classes[1] = 18;
} else {
classes[0] = 121;
classes[1] = 9;
}
}
} else {
if (features[3] <= 1.3283718824386597) {
classes[0] = 137;
classes[1] = 16;
} else {
if (features[2] <= -0.08075670152902603) {
classes[0] = 75;
classes[1] = 5;
} else {
classes[0] = 453;
classes[1] = 4;
}
}
}
}
} else {
if (features[1] <= 0.3474765121936798) {
if (features[3] <= 0.5934110283851624) {
if (features[2] <= 0.003244615043513477) {
if (features[0] <= -0.03575599752366543) {
classes[0] = 330;
classes[1] = 39;
} else {
classes[0] = 360;
classes[1] = 138;
}
} else {
if (features[2] <= 0.01407763920724392) {
classes[0] = 607;
classes[1] = 57;
} else {
classes[0] = 2837;
classes[1] = 48;
}
}
} else {
if (features[2] <= -0.021189325489103794) {
if (features[0] <= 0.008428129367530346) {
classes[0] = 113;
classes[1] = 342;
} else {
classes[0] = 82;
classes[1] = 90;
}
} else {
if (features[2] <= 0.010304238181561232) {
classes[0] = 290;
classes[1] = 266;
} else {
classes[0] = 142;
classes[1] = 28;
}
}
}
} else {
if (features[2] <= 0.00489223818294704) {
if (features[1] <= 0.4410252124071121) {
if (features[0] <= -0.034201690927147865) {
classes[0] = 1971;
classes[1] = 653;
} else {
classes[0] = 2273;
classes[1] = 234;
}
} else {
if (features[3] <= 1.035286784172058) {
classes[0] = 1973;
classes[1] = 195;
} else {
classes[0] = 9665;
classes[1] = 144;
}
}
} else {
if (features[2] <= 0.016115683130919933) {
if (features[1] <= 0.4877214878797531) {
classes[0] = 2990;
classes[1] = 137;
} else {
classes[0] = 1448;
classes[1] = 18;
}
} else {
if (features[0] <= -0.04386013746261597) {
classes[0] = 7991;
classes[1] = 130;
} else {
classes[0] = 7008;
classes[1] = 44;
}
}
}
}
}
}

return findMax(classes);
};

};

function predict(NBSI, NDVI, NDVI_RE3, CL_RE){
var clf = new DecisionTreeClassifier();
return [1/(1+Math.exp(-1*clf.predict([NBSI, NDVI, NDVI_RE3, CL_RE])))];
}
"""

evalscript_ndvi = """
//VERSION=3
function setup() {
return {
input: [
{
bands: ["B04", "B08", "dataMask"],
}
],
output: [
{
id: "bands",
bands: ["NDVI"],
sampleType: SampleType.FLOAT32
},
{
id: "dataMask",
bands: 1
}
] };
}

function evaluatePixel(samples) {
return {
bands: [index(samples.B08, samples.B04)],
dataMask: [samples.dataMask]
};
}
"""
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