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GP-AE-14ZUPCJAerospaceOpen for request

Satellite change detection for Indian hazards

A remote-sensing laboratory for comparing pre-event and post-event satellite imagery and mapping selected hazard-related surface changes in India.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • PostGIS
  • Python
  • Rasterio
  • PyTorch
  • Docker

Project definition

Problem statement

Differences between satellite images can come from a hazard, season, sensor, viewing geometry, cloud, shadow, water level, or imperfect registration.

The engineering problem is to prepare comparable image pairs, isolate selected change categories, quantify uncertainty, and validate mapped areas without treating every pixel difference as hazard damage.

Project objectives

  • Prepare public pre-event and post-event imagery for selected Indian flood, landslide, fire, or coastal-change cases.
  • Align imagery, harmonise resolution, and apply cloud, shadow, no-data, and quality masks.
  • Compare index, threshold, classical, and selected learned change-detection methods.
  • Validate change maps with prepared reference polygons or independently interpreted samples.
  • Calculate affected area, confidence, and error by administrative or physical analysis unit.

System design

System modules

01

Imagery registry

Records sensor, product, date, resolution, bands, orbit, licence, quality, and event metadata.

02

Preprocessing pipeline

Reprojects, co-registers, resamples, normalises, clips, and masks each image pair.

03

Change engine

Runs selected spectral, statistical, and learned methods through a common experiment interface.

04

Validation workspace

Manages reference samples, reviewer labels, confusion matrices, area error, and disagreement.

05

Hazard map

Displays paired imagery, change classes, confidence, masks, validation points, and aggregate area.

Methodology

System workflow

  1. 01
    Select case

    The student opens a prepared hazard event, study boundary, image pair, and target change class.

  2. 02
    Prepare images

    The pipeline aligns spatial resolution and geometry and removes invalid or cloud-affected observations.

  3. 03
    Run methods

    Baseline and selected advanced methods process the same valid comparison area.

  4. 04
    Validate

    Predicted change is compared with held-out reference samples and area estimates are adjusted or qualified.

  5. 05
    Publish map

    The accepted experiment exports a map with source dates, masks, confidence, metrics, and limitations.

Demonstration scenario

Pre-flood and post-flood images for a prepared Indian study area are aligned and masked. A water-index baseline and a learned change method produce inundation maps. The dashboard compares them against held-out reference samples and exports the selected map with confidence and valid-area coverage.

Engineering

Technical architecture

Web application
Nuxt and Vue for image pairing, swipe comparison, layer controls, validation, charts, and export.
Geospatial API
FastAPI for imagery metadata, raster jobs, model inference, validation, summaries, and tiles.
Spatial data layer
PostgreSQL and PostGIS for study areas, events, footprints, reference samples, changes, and metrics.
Raster pipeline
Python and Rasterio for registration checks, masks, indices, feature stacks, zonal calculations, and exports.
Model pipeline
Transparent baselines and optional PyTorch change models with event-separated tests and saved configurations.

Testing

Evaluation

Evaluation measures

  • Registration error and valid comparison-area coverage
  • Per-class precision, recall, F1 score, and intersection over union
  • Affected-area error against prepared reference polygons
  • Performance across sensors, seasons, clouds, terrain, and event types
  • False changes caused by misregistration, shadow, or seasonal differences
  • Processing time across resolution, band count, and study-area size

System boundaries

  • The project uses public or explicitly permitted satellite imagery and prepared hazard cases.
  • Detected surface change does not establish damage, cause, ownership, compensation, or legal responsibility.
  • Cloud, resolution, acquisition timing, and reference-data limitations are shown with every map.
  • Outputs are educational analysis products and not operational emergency-response information.

Included

  1. 01Satellite-pair and study-area interface
  2. 02Image preparation, change detection, validation, and mapping modules
  3. 03Before-and-after, confidence, error, and area dashboards
  4. 04Prepared public imagery, hazard cases, labels, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, remote-sensing workflow diagrams, change maps, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-AE-14ZUPCJ
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.