StephenApX/MTL-TripleS

[ISPRS P&RS 2025] TripleS: Mitigating multi-task learning conflicts for semantic change detection in high-resolution remote sensing imagery

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Experimental

This project helps remote sensing analysts and geospatial professionals detect and categorize changes in high-resolution satellite or aerial imagery. It takes two images of the same area taken at different times and identifies not only where changes occurred, but also what kind of changes (e.g., forest to urban, water to land). This tool is designed for specialists working with satellite data for environmental monitoring, urban planning, or disaster assessment.

No commits in the last 6 months.

Use this if you need to precisely identify both the location and the semantic nature of changes in high-resolution remote sensing imagery, especially for large areas.

Not ideal if you only need to detect general changes without semantic classification or if you are working with low-resolution imagery.

remote-sensing geospatial-analysis land-cover-change environmental-monitoring urban-development
No License Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 0 / 25

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Language

Python

License

Last pushed

Oct 08, 2025

Commits (30d)

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