🧶 plaknit
Processing Large-Scale PlanetScope Data
Note: plaknit is fully operational as of v0.3.0. Continue to look for updates, and please share feedback or ideas through the GitHub Issues tab.
PlanetScope Scene (PSS) data are reveared for its quality and distinct ability to balance spatial and temporal resolution in Earth Observation data. While PSS has proven itself a valuable asset in monitoring small-scale areas, the literature has pointed out the shortcomings when creating a single image from individual tiles (Frazier & Hemingway, 2021).
plaknit bundles the workflow I use to operationalize large-area mosaics so
you can run the same process locally or in an HPC environment. The goal is to
spend time answering big questions, not making a big mess of your data.
- Free software: MIT License
- Documentation: https://dzfinch.github.io/plaknit
Features
- CLI + Python API that scale from local experimentation to HPC batch runs.
- Planning workflow that searches Planet's STAC/Data API and scores scenes, with ordering handled by a separate
plaknit orderworkflow. - GDAL-powered parallel masking of Planet strips with their UDM rasters.
- Tuned Orfeo Toolbox mosaicking pipeline with RAM hints for large jobs.
- Random Forest and Boosted Regression Tree ensemble training + inference utilities for classifying Planet stacks.
Quick Start
Train a BRT Ensemble Classifier
plaknit brt train \
--image stack.tif \
--labels training.gpkg \
--label-column class_id \
--output ./brt_ensemble/ \
--n-models 5 \
--gpu
Apply the Ensemble to New Imagery
plaknit brt predict \
--image new_stack.tif \
--ensemble-dir ./brt_ensemble/ \
--output-dir ./probability_outputs/ \
--feature-importance-out predictor_importance.csv
See Usage for complete examples and all supported options.
1 maintainer
Metadata
1 maintainer
Release files for plaknit 0.3.7
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| plaknit-0.3.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 177.4 kB
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