Project case study / 02
Trace
From pixels to a routable city graph — then a test of what breaks first.
Live public demo · Three-person team · ML and graph ownership
At a glance / inspectable evidence
Project evidence
- Validation IoU
- 0.670
- CPU tests
- 76
- Public demo
- Live
Held-out DeepGlobe road-segmentation data
Topology and application logic covered independently
Explore road extraction and closure simulation
01 / Overview
- Role
- ML pipeline, graph analysis, and product engineering in a team of three
- Stack
- Python · PyTorch · SegFormer · NetworkX · Streamlit · Folium
An end-to-end geospatial ML pipeline that extracts roads, reconstructs network topology, and lets users simulate critical junction closures and rerouting.
02 / Context & problem
The problem
Satellite imagery shows roads visually but does not directly reveal which junctions are structurally critical or how closures affect connectivity.
The response
Trace segments road pixels, heals and reconstructs them as a graph, ranks junctions through network analysis, and exposes the results in an interactive map-based dashboard.
03 / System path
Follow the work from input to outcome.
- 01
Satellite image
- 02
Road segmentation
- 03
Gap healing
- 04
Network graph
- 05
Closure simulation
04 / Contribution
My contribution.
- 01
Owned the road-segmentation pipeline, data tooling, evaluation, and cross-stage integration
- 02
Compared model variants and released the selected checkpoint with 0.670 validation IoU
- 03
Connected segmentation outputs to graph artifacts and coordinated the CPU deployment and live demo
05 / Engineering pressure
The hard parts.
- 01Recovering road continuity beneath trees, shadows, and ambiguous terrain
- 02Converting noisy masks into useful routable topology
- 03Making graph resilience analysis understandable through interaction
06 / Outcome & reflection
What came out of it.
Reached 0.670 IoU on held-out DeepGlobe validation; the trained model and repository were released publicly.
Lessons carried forward
- A visually plausible mask is not automatically a useful graph.
- Domain metrics matter more when they connect to an explorable decision.
- Testing topology logic separately made model iteration safer.