Implement cloud shadow fill/segmentation/matching approach
Open
Nobody has claimed this yet.
enhancement
- Dominant language
- Python
- Stars
- 55
- Forks
- 12
- PR merge metrics
- No merged PRs in 30d
Description
The naive cloudshadow approach in #3 is better than nothing but barely acceptable. Let's study the full-blown cloud/shadow matching and implement it here.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the naive cloudshadow approach referenced in issue #3 and the current rio-cloudmask implementation. The issue names no files, tests, or entry points, so first map where cloud and shadow processing currently lives; done should mean the full cloud/shadow matching approach is implemented and validated against the plugin's expected imagery behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100