SIH26162/Disaster Management

Industrial fire geospatial detection system

An AI-enabled geospatial web system that uses satellite imagery and thermal data to classify industrial fires and show them on a map.

National Technical Research Organisation (NTRO) · Software

Teams already in: Not read yet
Buildable in 36 hours: ModerateOur 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

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
Data Integration Module
Combines NASA FIRMS thermal anomaly data, land-cover information, and industrial databases.
02
AI Classification Engine
Automatically distinguishes industrial fires from forest fires, agricultural burning, and mining activity.
03
GIS Mapping Interface
Visualizes the classified thermal sources and fires as an overlay on standard maps.
04
Storage and Monitoring System
Stores processed data and supports ongoing monitoring of persistent thermal sources.

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 if your system can correctly separate industrial fires from natural fires using the required data sources, and whether your GIS interface displays the output clearly as a map overlay.

They never say

specific industrial database schemas to use
exact satellite imagery sources required beyond NASA FIRMS
performance or accuracy targets for the AI classification

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 industrial database schemas to use?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact satellite imagery sources required beyond NASA FIRMS?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Performance or accuracy targets for the AI classification?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

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

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.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No measurable target
The description does not provide an accuracy percentage or speed target for the AI classification.
No specific user named
The text states the risks to public safety and infrastructure, but never names who will actually operate the software.

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.

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 SIH26162, set by National Technical Research Organisation (NTRO).
Its official title, word for word: "AI-Based Detection and Classification of Industrial Fires and Persistent Thermal Sources Using NASA FIRMS, OSM & Satellite Data"
Theme: Disaster Management. Category: software.

In plain terms: An AI-enabled geospatial web system that uses satellite imagery and thermal data to classify industrial fires and show them on a map.

## The organisers' full description, verbatim

• Background Industrial facilities generate thermal signatures that can be observed from space, but current satellite-based monitoring systems like NASA FIRMS cannot distinguish between different types of thermal anomalies. To address this, there is a challenge to develop an AI-enabled geospatial system that integrates thermal data, land-cover information, industrial databases, and satellite imagery to automatically identify, classify, and monitor industrial fires and persistent thermal sources. • Description Industrial facilities such as oil refineries, petrochemical complexes, thermal power plants, steel industries, mining areas, and LNG terminals generate thermal signatures that can be observed from space. In addition, accidental industrial fires, gas leaks, explosions, and abnormal thermal events pose significant risks to critical infrastructure, public safety, and the environment. Current satellite-based fire monitoring systems such as NASA FIRMS provide thermal anomaly detections but do not distinguish between industrial fires, gas flares, agricultural burning, mining activity, and wildfires. The challenge is to develop an AI-enabled geospatial system that can automatically identify, classify, and monitor industrial fires and persistent thermal sources by integrating thermal anomaly data, land-cover information, industrial infrastructure databases, and satellite imagery. • Expected Solution/Deliverables: i. Classification and segregation of Industrial fires from forest fires and other natural fires. ii. GIS based solution for data storage, visualization of the output as an overlay over maps

## What the brief never says

- specific industrial database schemas to use
- exact satellite imagery sources required beyond NASA FIRMS
- performance or accuracy targets for the AI classification
## 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.
- Known obstacles in the brief: No target; No named user.

## 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 (213 words)

• Background Industrial facilities generate thermal signatures that can be observed from space, but current satellite-based monitoring systems like NASA FIRMS cannot distinguish between different types of thermal anomalies. To address this, there is a challenge to develop an AI-enabled geospatial system that integrates thermal data, land-cover information, industrial databases, and satellite imagery to automatically identify, classify, and monitor industrial fires and persistent thermal sources. • Description Industrial facilities such as oil refineries, petrochemical complexes, thermal power plants, steel industries, mining areas, and LNG terminals generate thermal signatures that can be observed from space. In addition, accidental industrial fires, gas leaks, explosions, and abnormal thermal events pose significant risks to critical infrastructure, public safety, and the environment. Current satellite-based fire monitoring systems such as NASA FIRMS provide thermal anomaly detections but do not distinguish between industrial fires, gas flares, agricultural burning, mining activity, and wildfires. The challenge is to develop an AI-enabled geospatial system that can automatically identify, classify, and monitor industrial fires and persistent thermal sources by integrating thermal anomaly data, land-cover information, industrial infrastructure databases, and satellite imagery. • Expected Solution/Deliverables: i. Classification and segregation of Industrial fires from forest fires and other natural fires. ii. GIS based solution for data storage, visualization of the output as an overlay over maps