Analysis error in Batch V2 mode with no clear message

Apps / Services
1

Hi Guys,
I am trying to use batch mode and getting an analysis error with no clear message.
Do you know what might be wrong?

This is my code

from sentinelhub import SentinelHubRequest, DataCollection, MimeType, CRS, BBox, SHConfig, BatchProcessClient

# Configuration
config = SHConfig()

# Define evalscript to calculate LAI
evalscript = """
//VERSION=3
var degToRad = Math.PI / 180;

function evaluatePixelOrig(samples) {
  var sample = samples[0];
  var b03_norm = normalize(sample.B03, 0, 0.253061520471542);
  var b04_norm = normalize(sample.B04, 0, 0.290393577911328);
  var b05_norm = normalize(sample.B05, 0, 0.305398915248555);
  var b06_norm = normalize(sample.B06, 0.006637972542253, 0.608900395797889);
  var b07_norm = normalize(sample.B07, 0.013972727018939, 0.753827384322927);
  var b8a_norm = normalize(sample.B8A, 0.026690138082061, 0.782011770669178);
  var b11_norm = normalize(sample.B11, 0.016388074192258, 0.493761397883092);
  var b12_norm = normalize(sample.B12, 0, 0.493025984460231);
  var viewZen_norm = normalize(Math.cos(sample.viewZenithMean * degToRad), 0.918595400582046, 1);
  var sunZen_norm  = normalize(Math.cos(sample.sunZenithAngles * degToRad), 0.342022871159208, 0.936206429175402);
  var relAzim_norm = Math.cos((sample.sunAzimuthAngles - sample.viewAzimuthMean) * degToRad);

  var n1 = neuron1(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm);
  var n2 = neuron2(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm);
  var n3 = neuron3(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm);
  var n4 = neuron4(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm);
  var n5 = neuron5(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm);

  var l2 = layer2(n1, n2, n3, n4, n5);

  var lai = denormalize(l2, 0.000319182538301, 14.4675094548151);
  return {
    default: [lai / 3]
  };
}

// Helper functions for calculations
function neuron1(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm) {
  var sum = 4.96238030555279 - 0.023406878966470 * b03_norm + 0.921655164636366 * b04_norm + 0.135576544080099 * b05_norm - 1.938331472397950 * b06_norm - 3.342495816122680 * b07_norm + 0.902277648009576 * b8a_norm + 0.205363538258614 * b11_norm - 0.040607844721716 * b12_norm - 0.083196409727092 * viewZen_norm + 0.260029270773809 * sunZen_norm + 0.284761567218845 * relAzim_norm;

  return tansig(sum);
}

function neuron2(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm) {
  var sum = 1.416008443981500 - 0.132555480856684 * b03_norm - 0.139574837333540 * b04_norm - 1.014606016898920 * b05_norm - 1.330890038649270 * b06_norm + 0.031730624503341 * b07_norm - 1.433583541317050 * b8a_norm - 0.959637898574699 * b11_norm + 1.133115706551000 * b12_norm + 0.216603876541632 * viewZen_norm + 0.410652303762839 * sunZen_norm + 0.064760155543506 * relAzim_norm;

  return tansig(sum);
}

function neuron3(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm) {
  var sum = 1.075897047213310 + 0.086015977724868 * b03_norm + 0.616648776881434 * b04_norm + 0.678003876446556 * b05_norm + 0.141102398644968 * b06_norm - 0.096682206883546 * b07_norm - 1.128832638862200 * b8a_norm + 0.302189102741375 * b11_norm + 0.434494937299725 * b12_norm - 0.021903699490589 * viewZen_norm - 0.228492476802263 * sunZen_norm - 0.039460537589826 * relAzim_norm;

  return tansig(sum);
}

function neuron4(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm) {
  var sum = 1.533988264655420 - 0.109366593670404 * b03_norm - 0.071046262972729 * b04_norm + 0.064582411478320 * b05_norm + 2.906325236823160 * b06_norm - 0.673873108979163 * b07_norm - 3.838051868280840 * b8a_norm + 1.695979344531530 * b11_norm + 0.046950296081713 * b12_norm - 0.049709652688365 * viewZen_norm + 0.021829545430994 * sunZen_norm + 0.057483827104091 * relAzim_norm;

  return tansig(sum);
}

function neuron5(b03_norm,b04_norm,b05_norm,b06_norm,b07_norm,b8a_norm,b11_norm,b12_norm, viewZen_norm,sunZen_norm,relAzim_norm) {
  var sum = 3.024115930757230 - 0.089939416159969 * b03_norm + 0.175395483106147 * b04_norm - 0.081847329172620 * b05_norm + 2.219895367487790 * b06_norm + 1.713873975136850 * b07_norm + 0.713069186099534 * b8a_norm + 0.138970813499201 * b11_norm - 0.060771761518025 * b12_norm + 0.124263341255473 * viewZen_norm + 0.210086140404351 * sunZen_norm - 0.183878138700341 * relAzim_norm;

  return tansig(sum);
}

function layer2(neuron1, neuron2, neuron3, neuron4, neuron5) {
  var sum = 1.096963107077220 - 1.500135489728730 * neuron1 - 0.096283269121503 * neuron2 - 0.194935930577094 * neuron3 - 0.352305895755591 * neuron4 + 0.075107415847473 * neuron5;

  return sum;
}

function normalize(unnormalized, min, max) {
  return 2 * (unnormalized - min) / (max - min) - 1;
}
function denormalize(normalized, min, max) {
  return 0.5 * (normalized + 1) * (max - min) + min;
}
function tansig(input) {
  return 2 / (1 + Math.exp(-2 * input)) - 1; 
}

function setup() {
  return {
    input: [{
      bands: [
          "B03", "B04", "B05", "B06", "B07", "B8A", "B11", "B12",
          "viewZenithMean", "viewAzimuthMean", "sunZenithAngles", "sunAzimuthAngles"
      ]
    }],
    output: [
        {
          id: "default",
          sampleType: "AUTO",
          bands: 1
        }
    ]
  };
}

function evaluatePixel(sample) {
  const result = evaluatePixelOrig([sample]);
  return result["default"];
}
"""


# Set up the request
request = SentinelHubRequest(
    evalscript=evalscript,
    input_data=[
        SentinelHubRequest.input_data(
            data_collection=DataCollection.SENTINEL2_L2A,
            time_interval=('2020-03-01', '2020-09-30'),
        ),
    ],
    responses=[
        SentinelHubRequest.output_response("default", MimeType.TIFF)
    ],
    config=config,
    bbox=BBox(bbox.tolist(), CRS.WGS84),
)

# Initialize batch process client
client = BatchProcessClient(config=config)

# IAM Role and S3 output configuration
iam_role_arn = "iam_role_arn"

# Define the batch request for S3 output
batch_request = client.create(
    process_request=request,
    input=client.geopackage_input(client.s3_specification(f"s3://geopackage_bucket_path", iam_role_arn=iam_role_arn, region="eu-central-1")),
    description="sample_3",
    output=client.raster_output(client.s3_specification(f"s3:://bucket_name", iam_role_arn=iam_role_arn, region="eu-central-1")),
)
2

Hi spacesense,

I’ve looked at your task and it seems your GeoPackage is missing the “id” column, as per the documentation.

Your GeoPackage has the “fid”, “geom”, “identifier” and “resolution” columns. BatchV2 however expects “id” instead of “fid”. We’ll look into forgoing this requirement if possible, as some GeoPackage tools use “fid” as PK by default.

Thank you for bringing to our attention that the error returned is unhelpful. We’ll fix this immediately.

Best regards,
Igor