SIH26175/Disaster Management

Disaster management satellite 3D terrain model

A software pipeline that takes a single optical satellite image and turns it into a 3D terrain model you can navigate like a video game.

Indian Space Research Organisation(ISRO) · Department of Space / Indian Space Research Organisation · 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 11 problem statement(s) in SIH 2025.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Elevation Estimation Module
Accepts single-view optical satellite imagery and outputs a high-fidelity Digital Surface Model.
02
Scale Calibration Module
Converts relative depth to absolute height using low-resolution DEMs or Ground Control Points.
03
Interactive Visualization Platform
Provides a 3D flythrough experience that lets users upload imagery and navigate the reconstructed terrain.

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 accuracy metrics like RMSE, MAE, and correlation against reference data across different landscape types, alongside the rendering quality and smoothness of the 3D flythrough.

They never say

specific reference datasets to be used for validation
exact hardware specifications for the rendering engine

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 reference datasets to be used for validation?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact hardware specifications for the rendering engine?
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.

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

The brief asks for elevation estimation module. How would you build that?
Decoded from SIH26175 itself — Accepts single-view optical satellite imagery and outputs a high-fidelity Digital Surface 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.

Also from Indian Space Research Organisation(ISRO)

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 SIH26175, set by Indian Space Research Organisation(ISRO).
Its official title, word for word: "DepthWizard - Single-View Height Estimation and 3D Flythrough"
Theme: Disaster Management. Category: software.

In plain terms: A software pipeline that takes a single optical satellite image and turns it into a 3D terrain model you can navigate like a video game.

## The organisers' full description, verbatim

Background Accurate Digital Elevation Models (DEMs) and Digital Surface Models (DSMs) are fundamental to urban planning, disaster management, and military reconnaissance. Traditionally, elevation data is acquired through stereo-imaging pairs, LiDAR, or Interferometric Synthetic Aperture Radar (InSAR). These approaches can be cost-prohibitive, dependent on specific sensor availability, and computationally intensive. Single-view height estimation offers an agile alternative, but foundational monocular depth models are trained largely on natural egocentric imagery and predict relative depth. When applied to remote sensing, they face domain gaps, structural variations, and a lack of absolute-scale mapping. Converting relative depth into metric elevation remains a critical challenge, alongside the operational need to transform static elevation profiles into interactive 3D assets that can be navigated in real time. Description Develop an end-to-end software pipeline that transforms single-view optical RGB remote-sensing images into high-precision elevation maps. The framework must support both non-georeferenced and georeferenced imagery. • Non-Georeferenced RGB Imagery (for example, PNG or JPG): Produce a Relative Digital Surface Model (rDSM) for images without spatial metadata. • Georeferenced RGB Imagery (for example, GeoTIFF): Produce an Absolute Digital Surface Model (DSM) with metric height values for images containing coordinate-system metadata. The solution should use a pre-trained monocular depth-estimation backbone to generate initial relative-depth maps. For georeferenced imagery, a lower-resolution DEM source such as SRTM or a limited set of Ground Control Points may be used to map scale-agnostic depth features to absolute metric elevations. For non-georeferenced imagery, relative height may be used directly in the visualization stage. After computing the elevation map, the system should project the original optical image onto a generated 3D terrain mesh and integrate the result with a rendering engine such as Unity, Three.js, or Babylon.js. The interface should support seamless first-person navigation and analysis of structural heights and slopes from arbitrary aerial perspectives. Key Milestones • Elevation Extraction: Use a robust pre-trained monocular depth model to extract geometric and structural representations from single-view optical imagery. • Scale Calibration: Develop a module that converts relative depth to absolute height using scene-level statistics, low-resolution DEMs, semantic priors, or minimal Ground Control Points for georeferenced inputs. • Visualization Layer: Build an immersive, preferably interactive, rendering pipeline that converts the optical texture and derived depth map into a navigable 3D environment deployable as a standalone application. Evaluation Criteria: • DSM Estimation - Accuracy and Validation (50%): Evaluate RMSE, MAE, and correlation against LiDAR or reference data, including performance stability across urban, sparse, hilly, and forested landscapes. • Visualization - Rendering Quality and User Experience (50%): Assess projection accuracy, visual fidelity, navigability of the 3D flythrough, interface intuitiveness, software stability, and successful standalone deployment. Expected Solution Deliver a fully integrated software suite with complete source code and technical documentation. The solution must be deployable as a unified module containing the following components: • Elevation Estimation Module: Accept single-view optical satellite imagery in PNG, JPG, or TIFF format and output a high-fidelity DSM in a standard geospatial format. • Interactive Visualization Platform: Provide a user-friendly 3D flythrough experience that lets users upload imagery, visualize reconstructed terrain, and validate estimated height values against reference datasets.

## What the brief never says

- specific reference datasets to be used for validation
- exact hardware specifications for the rendering engine
## 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.

---
Problem statement facts assembled by Wynit from Indian Space Research Organisation(ISRO)'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 (512 words)

Background Accurate Digital Elevation Models (DEMs) and Digital Surface Models (DSMs) are fundamental to urban planning, disaster management, and military reconnaissance. Traditionally, elevation data is acquired through stereo-imaging pairs, LiDAR, or Interferometric Synthetic Aperture Radar (InSAR). These approaches can be cost-prohibitive, dependent on specific sensor availability, and computationally intensive. Single-view height estimation offers an agile alternative, but foundational monocular depth models are trained largely on natural egocentric imagery and predict relative depth. When applied to remote sensing, they face domain gaps, structural variations, and a lack of absolute-scale mapping. Converting relative depth into metric elevation remains a critical challenge, alongside the operational need to transform static elevation profiles into interactive 3D assets that can be navigated in real time. Description Develop an end-to-end software pipeline that transforms single-view optical RGB remote-sensing images into high-precision elevation maps. The framework must support both non-georeferenced and georeferenced imagery. • Non-Georeferenced RGB Imagery (for example, PNG or JPG): Produce a Relative Digital Surface Model (rDSM) for images without spatial metadata. • Georeferenced RGB Imagery (for example, GeoTIFF): Produce an Absolute Digital Surface Model (DSM) with metric height values for images containing coordinate-system metadata. The solution should use a pre-trained monocular depth-estimation backbone to generate initial relative-depth maps. For georeferenced imagery, a lower-resolution DEM source such as SRTM or a limited set of Ground Control Points may be used to map scale-agnostic depth features to absolute metric elevations. For non-georeferenced imagery, relative height may be used directly in the visualization stage. After computing the elevation map, the system should project the original optical image onto a generated 3D terrain mesh and integrate the result with a rendering engine such as Unity, Three.js, or Babylon.js. The interface should support seamless first-person navigation and analysis of structural heights and slopes from arbitrary aerial perspectives. Key Milestones • Elevation Extraction: Use a robust pre-trained monocular depth model to extract geometric and structural representations from single-view optical imagery. • Scale Calibration: Develop a module that converts relative depth to absolute height using scene-level statistics, low-resolution DEMs, semantic priors, or minimal Ground Control Points for georeferenced inputs. • Visualization Layer: Build an immersive, preferably interactive, rendering pipeline that converts the optical texture and derived depth map into a navigable 3D environment deployable as a standalone application. Evaluation Criteria: • DSM Estimation - Accuracy and Validation (50%): Evaluate RMSE, MAE, and correlation against LiDAR or reference data, including performance stability across urban, sparse, hilly, and forested landscapes. • Visualization - Rendering Quality and User Experience (50%): Assess projection accuracy, visual fidelity, navigability of the 3D flythrough, interface intuitiveness, software stability, and successful standalone deployment. Expected Solution Deliver a fully integrated software suite with complete source code and technical documentation. The solution must be deployable as a unified module containing the following components: • Elevation Estimation Module: Accept single-view optical satellite imagery in PNG, JPG, or TIFF format and output a high-fidelity DSM in a standard geospatial format. • Interactive Visualization Platform: Provide a user-friendly 3D flythrough experience that lets users upload imagery, visualize reconstructed terrain, and validate estimated height values against reference datasets.