Leveraging satellite imagery to determine Oil spills at sea along with AIS data correlations to identify vessel responsible for the spill.
National Technical Research Organisation (NTRO)
Good pick. Genuinely. Now sit down, because the judges are going to try anyway — and this is what they will try.
Strong pick. One of the best-value problems in this block — free data, a gripping end-to-end attribution demo, a thin field — but handle the SAR false positives and present the origin as a probability region, because compounding errors and over-precise attribution are where it can fall apart. Roughly 90–210 teams are expected to go here.
The receipts
Every red flag on this statement, in full. These are the four places it bites.
Exhibit A
SAR oil-slick detection is plagued by look-alikes — biogenic films, low-wind zones, ship wakes — and false positives are the dominant failure mode, so detection precision is where an NTRO judge will press first
It gets worse
Drift hindcasting is inherently uncertain, so the reconstructed origin is a probability region not a point, and presenting it as a precise location overstates what the physics supports
Still reading?
Attributing a spill to a named vessel is a forensic accusation, so the scoring must be presented as ranked suspicion with evidence, not as a determination
And the finisher
Chaining three subsystems means errors compound — a mislocated origin sends the AIS correlation to the wrong ships entirely
The damage report
Every score this statement earned, and what each one actually costs you.
Feasibility
3/5Buildable. Not comfortably. There is a week in here you have not planned for yet.
Sentinel-1 SAR and AIS data are both free and public, and there are labelled oil-spill SAR datasets, but coupling detection with physical drift hindcasting and AIS correlation is a genuine three-discipline integration, and distinguishing real slicks from the many look-alikes in SAR — algae, low wind, ship wakes — is the hard, error-prone core.
Innovation scope
4/5There is something genuinely new here. Do not bury it under another dashboard.
The drift hindcasting to an origin and the vessel-scoring logic — how you weight proximity against trajectory alignment and AIS anomalies — are genuinely open, and combining the three stages into one attribution pipeline is where the real contribution lies.
Clarity
5/5The ask is unambiguous, which quietly removes your favourite excuse.
The description numbers the three tasks precisely — detect and characterise the slick, hindcast drift to origin and forecast spread, attribute to a vessel by reconstructing and scoring AIS traffic — and even names the scoring factors, making the requirement exceptionally clear.
Acceptance potential
4/5Strong footing before you have written a line. Try not to waste it.
Underrated — all the data is free and public, the attribution pipeline is a genuinely compelling end-to-end story, and the maritime-forensic domain thins the field, though the SAR false-positive problem and the physical hindcasting are real difficulties you must handle honestly rather than gloss.
Effort
MassiveA semester of work wearing a hackathon costume. Something is getting cut; decide what now, not in week five.
SAR detection, physical drift modelling and AIS correlation with suspect scoring are three substantial subsystems in different disciplines that must be chained into one working pipeline.
Demo-ability
EasyEasy to demo — and so is everyone else's. Working is the floor here, not the achievement.
A slick detected, drifted back to an origin, and matched to a ranked suspect vessel on a map is a complete, self-explanatory investigative narrative that lands powerfully.
The demo they will have already seen
Somewhere around 90–210 teams are heading here, and the description is doing the choosing for most of them. They will read the same brief, reach the same architecture, and build a version of the same demo you are planning. Being correct is the floor. If your five minutes could be swapped with the team before you and nobody in the room would notice, you have not picked badly — you have built predictably, which costs exactly the same and hurts more.
What survives
The ground worth standing on when the questions start.
- Sentinel-1 SAR and AIS data are both entirely free and public, and marinecadastre.gov is linked directly for the AIS format, so the whole pipeline runs on open data
- The three-stage attribution story — detect, hindcast, attribute — is one of the most compelling end-to-end narratives in the entire problem set
- OpenDrift is a mature open-source Lagrangian drift model, so the physical hindcasting rests on established tooling rather than a hand-rolled model
Nothing here is fatal. It is just the list of places this statement pushes back, and you now get to push there first.
The framing is a joke. The findings are not — they are the same analysis on the statement page, and every line above is attached to a score or a fact in the record. It is one opinion with its reasoning attached, so argue with it before you trust it.