Build notes · Goh Kun Ming
Satellite-to-Simulation Data Pipeline
Research · Public project
Apr 2026 – Aug 2026
Project overview
A research pipeline for harmonising imagery annotations, tracking provenance and checking segmentation data quality.
A governed satellite-imagery pipeline that converts heterogeneous annotations into segmentation, classification and context outputs. The work combines consistent label mapping, geometry-derived features and traceable quality checks to prepare data for simulation-related research.
Inside this build
- Harmonised raster, COCO, XML, TXT and GeoJSON annotations without silently accepting ambiguous labels.
- Prepared 13,487 segmentation samples and 56,096 classification rows across 14 dataset roots.
- Standardised one background and 19 foreground classes.
- Derived geometry components, topology edges and context features.
- Produced provenance logs, structured inventories, stage reports and 117 visual QA previews.
- Checked image-mask alignment and duplicate-image leakage, with unreadable data quarantined.