leegasil.blogg.se - october 2023

Ccdc icare packages download#

arraySlice(1, coefIndex, coefIndex.add(1))Įe.String('$1_coef_').cat(coefIndex. Segment = segments.arraySlice(0, segmentIndex, segmentIndex.add(1))Įe.Image(acc).addBands(toCoef(coefIndex, segment)),Įe.Filter.stringEndsWith('item', '_coefs').Not() SegmentIndex = ee.Number(segmentIndex).int8() import eeĮe.Authenticate() # Skip if you're running this in SEPALĮe.quence(0, maxSegments.subtract(1))Įe.Image(acc).addBands(toSegment(segmentIndex, segments)), Have a look at Noel's article for some more tips on downloads. The below script of 1000x1000m and 6 segments gives you 250 bands and a 17MB download.

Ccdc icare packages download#

You will end up with a lot of bands, and you cannot download more than 32MB. You can turn the CCDC segments array image into an image with a band per array element, then use getDownloadURL() to extract the data. GREEN_coefs_aoi = np.array(ccdc_mosaic.sampleRectangle(aoi).get('GREEN_coefs').getInfo()) # Get coefficients for the area of interest GREEN_coefs_poi = np.array(ccdc_mosaic.sampleRectangle(poi).get('GREEN_coefs').getInfo()) # This pixel contains 4 time segments: OKĪrray(]]) TEnd_poi = np.array(ccdc_mosaic.sampleRectangle(poi).get('tEnd').getInfo()) Map # Display for inspection with 'Inspector' Poi = ee.Geometry.Point(-53.423000,-13.623000) # Point of interestĪoi = poi.buffer(50).bounds() # 1 ha rectangle centered on poiĬcdc = ee.ImageCollection('projects/CCDC/v3') # Load assetĬcdc_mosaic = ccdc.mosaic() # Create mosaic from 990 non-overlapping images covering the world I wonder if this could be a projection issue as hinted here.

I am aware of the gee-ccdc-tools package, but I am looking for a Python solution.

I was thinking of using map() and then concatenate/merge the results into an xarray.

Loop through the ee.FeatureCollection(): need input on an efficient way to iterate.

iCARE: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) version 1.18.0 from Bioconductor rdrr.

Try for a 1 ha square polygon using ee.Image.sampleRectangle(): FAIL, returns the same results as for the point Geometry while the aoi contains several pixels (asset resolution = 30 m). An R package to compute Individualized Coherent Absolute Risk Estimators.

Try for a given ee.Geometry.Point() location: OK.

I try to extract for a collection of 1 ha square polygons the bands coefficients from a preprocessed Landsat CCDC Google Earth Engine Asset ee.ImageCollection('projects/CCDC/v3').