GrIML v1.0.1

What's Changed

• JOSS pre review edits by @PennyHow in #21
• Bugfix/joss edits by @PennyHow in #22
• Editorial corrections for JOSS paper by @AdamRJensen in #23
• Features/old readthedocs files by @PennyHow in #28
• Version call added by @PennyHow in #29
• JOSS pre-review paper edits by @PennyHow in #30
• Exception removed for version print by @PennyHow in #31
• Acknowledgements edit by @PennyHow in #32

New Contributors

• @AdamRJensen made their first contribution in #23

Full Changelog: v1.0.0...v1.0.1

GrIML v1.0.0

What's Changed

• Clean up of module scripts with local directories by @PennyHow in #13
• Paper and readthedocs updates by @PennyHow in #14
• Features/new docs by @PennyHow in #18
• Figure links changed for valid paper construction by @PennyHow in #19
• Bugfix/remove absolute paths by @PennyHow in #20

Full Changelog: v0.1.0...v1.0.0

GrIML v0.1.0

What's Changed

• Small bade update by @PennyHow in #3
• Package re-structuring and updated docs by @PennyHow in #4
• Unit testing patch by @PennyHow in #5
• Update to Python 3.11 configuration by @PennyHow in #6
• Readthedocs and paper draft updated by @PennyHow in #7
• Update issue templates by @PennyHow in #9
• action badge added by @PennyHow in #10
• Load module added by @PennyHow in #11
• Assign regions by @PennyHow in #12

Full Changelog: v0.0.1...v0.1.0

GrIML v0.0.1

The GrIML python package

A GrIML workflow for classifying water bodies from satellite imagery using a multi-sensor, multi-method approach. This workflow is part of the ESA GrIML project.

Quickstart

The GrIML package can be installed using pip:

pip install griml

Or cloned from the Github repository:

git clone https://github.com/PennyHow/GrIML

Workflow

GrIML builds on the existing workflows from the ESA Glaciers CCI (Option 6, An Inventory of Ice-Marginal Lakes in Greenland), refined here to form a unified processing chain that is shared openly on Github and pip.

Cloud processing

Primary processing is performed using the Google Earth Engine Python API, including satellite data retrieval and binary classification from multiple sensors. By doing so, the workflow avoids the handling of heavy data downloads and operations.

Subject to funding, it is intended to include add-on modules to the workflow, which take advantage of the cloud processing capabilities provided by the SentinelHub APIs. SentinelHub is a cloud processing platform that can be used to retrieve and process data from many satellite products.

Offline processing

Key Python packages that will be used in the offline components of the workflow:

• geopandas - for vector dataset handling
• numpy - for numerical operations
• pandas - for dataframe handling
• scipy - for matrix operations
• shapely - for geometric operations