SIH26142/Space Technology

Satellite imagery super-resolution web tool

A web software tool that uses deep learning models to convert medium-resolution Sentinel-2 satellite imagery into sharper high-resolution images.

National Technical Research Organisation (NTRO) · Software

Teams already in: Not read yet
Buildable in 36 hours: HighOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in Dataset Briefing video

Whether this suits your team

Why this fits you

This organisation declared winners on 13 problem statement(s) in SIH 2025 and on 13 in SIH 2024. It has awarded 4 joint winner(s), so it sometimes splits a prize between two teams.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Image Pre-processing Module
Takes medium-resolution Sentinel-2 satellite imagery and prepares it for the generative model.
02
Deep Learning Super-Resolution Model
Applies a trained generative model like a GAN, diffusion model, or transformer to enhance spatial resolution to under 4 meters.
03
Uncertainty and Validation Module
Validates enhanced outputs against high-resolution reference data and accounts for model-inferred error components.
04
Application Support Output
Produces final enhanced images suitable for crop monitoring, urban analysis, and disaster assessment.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will check whether your model transforms 10m Sentinel-2 imagery into products under 4m resolution while preserving geospatial and spectral consistency. They will also test how well you handle model uncertainty and validate your outputs against high-resolution reference data.

They never say

specific hardware requirements for training and inference
exact evaluation metrics for accuracy assessment

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Specific hardware requirements for training and inference?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact evaluation metrics for accuracy assessment?
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.

8 more— the ones a panel gets to if there is time

The brief asks for image pre-processing module. How would you build that?
Decoded from SIH26142 itself — Takes medium-resolution Sentinel-2 satellite imagery and prepares it for the generative model. The brief asks for it by name.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: Provided — open it
Does the brief need data?: Yes, and they gave it to you.
Briefing video: Published by the organisers

Also from National Technical Research Organisation (NTRO)

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26142, set by National Technical Research Organisation (NTRO).
Its official title, word for word: "Deep Learning Based Super Resolution Mapping (SRM) from Medium Resolution Satellite Imageries"
Theme: Space Technology. Category: software.

In plain terms: A web software tool that uses deep learning models to convert medium-resolution Sentinel-2 satellite imagery into sharper high-resolution images.

## The organisers' full description, verbatim

• Background Medium-resolution satellite imagery, typically ranging from 10 to 30 meters, is widely used in change detection, agriculture, land-cover mapping, disaster monitoring, and urban planning because it offers broad coverage and frequent revisit time. However, the spatial detail is often insufficient for fine-scale analysis, such as identifying small buildings, narrow roads, field boundaries, or localized damage assessment. This creates a need for advanced deep learning based generative enhancement techniques that can extract greater value from existing Earth observation data. • Description Medium-resolution satellite imagery, usually ranging from 10 to 30 meters, is widely used in remote sensing for agriculture monitoring, land-cover mapping, urban planning, disaster assessment, and environmental observation because it provides large-area coverage and frequent revisit capability. However, its spatial resolution is often not sufficient to clearly identify fine details such as narrow roads, small buildings, field boundaries, water edges, or localized damage. This limitation reduces the accuracy and confidence of interpretation and decision-making in applications that require detailed ground-level information. Generative AI super-resolution addresses this problem by using advanced models such as GANs, diffusion models, and deep neural networks to enhance medium-resolution satellite images into sharper and more information-rich finer outputs. These models learn spatial textures, patterns, edges, and spectral relationships from training data containing both medium-resolution and high-resolution image pairs. The goal is not simply to make the image visually clearer, but to reconstruct useful fine-scale details while preserving the original geographic and spectral consistency of the satellite data. The expected solution is a robust AI-based super-resolution framework that can take medium-resolution satellite imagery as input, perform pre-processing, apply a trained generative model, and produce an enhanced spatial resolution image, suitable for analysis. The system should improve feature visibility, support better classification, change detection, crop monitoring, urban mapping, and disaster response. At the same time, it must clearly manage uncertainty because some reconstructed details are inferred by the model and not directly observed. Therefore, validation against high-resolution reference data is essential to ensure that the enhanced outputs are scientifically reliable and useful for real-world remote sensing applications. • Expected Solution The expected solution is a robust super-resolution framework model based on the choice of participating team (Transformers/Generative/CNN etc.) that can transform the input medium-resolution satellite imagery (10m Sentinel-2 Satellite Imagery) into sharper, information-rich products (<4m) while preserving geospatial and spectral consistency. The solution should include pre-processing, model training with paired datasets, accuracy assessment, and validation against high-resolution references. Ideally, it should support applications such as crop monitoring, urban analysis, and disaster assessment. The final outcome should improve in-terms of interpretability and analytical utility, while clearly accounting for uncertainty and error components.

## What the brief never says

- specific hardware requirements for training and inference
- exact evaluation metrics for accuracy assessment
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- The organisers attached a dataset, so build against theirs rather than inventing one.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from National Technical Research Organisation (NTRO)'s published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (435 words)

• Background Medium-resolution satellite imagery, typically ranging from 10 to 30 meters, is widely used in change detection, agriculture, land-cover mapping, disaster monitoring, and urban planning because it offers broad coverage and frequent revisit time. However, the spatial detail is often insufficient for fine-scale analysis, such as identifying small buildings, narrow roads, field boundaries, or localized damage assessment. This creates a need for advanced deep learning based generative enhancement techniques that can extract greater value from existing Earth observation data. • Description Medium-resolution satellite imagery, usually ranging from 10 to 30 meters, is widely used in remote sensing for agriculture monitoring, land-cover mapping, urban planning, disaster assessment, and environmental observation because it provides large-area coverage and frequent revisit capability. However, its spatial resolution is often not sufficient to clearly identify fine details such as narrow roads, small buildings, field boundaries, water edges, or localized damage. This limitation reduces the accuracy and confidence of interpretation and decision-making in applications that require detailed ground-level information. Generative AI super-resolution addresses this problem by using advanced models such as GANs, diffusion models, and deep neural networks to enhance medium-resolution satellite images into sharper and more information-rich finer outputs. These models learn spatial textures, patterns, edges, and spectral relationships from training data containing both medium-resolution and high-resolution image pairs. The goal is not simply to make the image visually clearer, but to reconstruct useful fine-scale details while preserving the original geographic and spectral consistency of the satellite data. The expected solution is a robust AI-based super-resolution framework that can take medium-resolution satellite imagery as input, perform pre-processing, apply a trained generative model, and produce an enhanced spatial resolution image, suitable for analysis. The system should improve feature visibility, support better classification, change detection, crop monitoring, urban mapping, and disaster response. At the same time, it must clearly manage uncertainty because some reconstructed details are inferred by the model and not directly observed. Therefore, validation against high-resolution reference data is essential to ensure that the enhanced outputs are scientifically reliable and useful for real-world remote sensing applications. • Expected Solution The expected solution is a robust super-resolution framework model based on the choice of participating team (Transformers/Generative/CNN etc.) that can transform the input medium-resolution satellite imagery (10m Sentinel-2 Satellite Imagery) into sharper, information-rich products (<4m) while preserving geospatial and spectral consistency. The solution should include pre-processing, model training with paired datasets, accuracy assessment, and validation against high-resolution references. Ideally, it should support applications such as crop monitoring, urban analysis, and disaster assessment. The final outcome should improve in-terms of interpretability and analytical utility, while clearly accounting for uncertainty and error components.