A deep learning software framework that uses daily surface satellite observations to reconstruct three-dimensional subsurface ocean temperature profiles for the North Indian Ocean.
Ministry of Earth Sciences (MoES) · Indian National Centre for Ocean Information Services (INCOIS) Ocean Valley · Software
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Decomposed from what the description asks for. Nothing added.
Data Preprocessing Pipeline
Preprocess and harmonize multi-source satellite and ocean datasets to a daily temporal resolution and 0.25 degree spatial resolution.
Satellite Embedding Engine
Generate compact latent representations from surface variables like SST, SSS, SSH, currents, and winds using deep learning architectures.
Subsurface Reconstruction Model
Train a model using deep learning to estimate ocean temperature at standard depth levels ranging from zero to one thousand meters.
Validation Framework
Evaluate the reconstructed temperature profiles using independent ARGO observations and standard skill metrics such as correlation and RMSE.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check if your model successfully uses surface satellite inputs to estimate subsurface temperatures across the specified standard depth levels and evaluates results using standard skill metrics against independent observations.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.
Specific temporal range required for training datasets?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact evaluation error thresholds that constitute success?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for data preprocessing pipeline. How would you build that?
Decoded from SIH26066 itself: Preprocess and harmonize multi-source satellite and ocean datasets to a daily temporal resolution and 0.25 degree spatial resolution. The brief asks for it by name.
The brief asks for satellite embedding engine. How would you build that?
Decoded from SIH26066 itself: Generate compact latent representations from surface variables like SST, SSS, SSH, currents, and winds using deep learning architectures. The brief asks for it by name.
The brief asks for subsurface reconstruction model. How would you build that?
Decoded from SIH26066 itself: Train a model using deep learning to estimate ocean temperature at standard depth levels ranging from zero to one thousand meters. The brief asks for it by name.
The brief asks for validation framework. How would you build that?
Decoded from SIH26066 itself: Evaluate the reconstructed temperature profiles using independent ARGO observations and standard skill metrics such as correlation and RMSE. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check if your model successfully uses surface satellite inputs to estimate subsurface temperatures across the specified standard depth levels and evaluates results using standard skill metrics against independent observations.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Each one is quoted from a gap in the brief, not a guess about your team.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
No measurable target
The text lists skill metrics like correlation, RMSE, and bias to use, but never states a passing numerical target for them.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.