Python based library to use Earth Observation data to retrieve biophysical maps using Gaussian Process Regression
Project description
pyeogpr 
Python based machine learning library to use Earth Observation data to map biophysical traits using Gaussian Process Regression (GPR) models. Works with Google Earth Engine and openEO cloud back-ends.
Features
- Access to GEE/openEO is required. Works best with the Copernicus Data Space Ecosystem. Register here or here
- Hybrid retrieval methods were used: the Gaussian Process Regression retrieval algorithms were trained on biophysical trait specific radiative transfer model (RTM) simulations
- Uncertainties provided!
- Runs "in the cloud" with the GEE/openEO Python API. No local processing is needed.
- Resulting maps in .tiff or netCDF format
Get started
Refer to the Documentation for instructions and examples.
Satellites and biophysical variables
You can select from a list of trained variables developed for the following satellites:
Cite as / Contact
- Kovács DD, Reyes-Muñoz P, Salinero-Delgado M, Mészáros VI, Berger K, Verrelst J. Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine. Remote Sensing. 2023; 15(13):3404. https://doi.org/10.3390/rs15133404
or
- Dávid D.Kovács. (2024). pyeogpr (zenodo). Zenodo. https://doi.org/10.5281/zenodo.13373838
- david.kovacs@tuwien.ac.at
Supported by the European Union (European Research Council, FLEXINEL, 101086622) project.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pyeogpr-2.4.7.tar.gz.
File metadata
- Download URL: pyeogpr-2.4.7.tar.gz
- Upload date:
- Size: 16.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.9.23
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b2bb9a3ed5db35d5405ba15dc357f6c3ad37f3f5ab5c8e4e7186dc5dc9e53805
|
|
| MD5 |
2a2dd3080dd6d36e5fb7872cf474bae2
|
|
| BLAKE2b-256 |
700e9696545a1a270766ee8865ebe0c079e2552fe2a153c1da87bf91035aaf8c
|
File details
Details for the file pyeogpr-2.4.7-py3-none-any.whl.
File metadata
- Download URL: pyeogpr-2.4.7-py3-none-any.whl
- Upload date:
- Size: 17.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.9.23
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c7ea0954d65e83982873b35d19135e9642e86cbc7b36924b8b798dc19d498ad8
|
|
| MD5 |
af91df043c0c52d5352284ef29fbc87a
|
|
| BLAKE2b-256 |
398da35da7cd4ef0d01f8241cfe2c270ef57ba61ffb6f9c35bcc446d556b62f5
|
