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Related Concept Videos

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Related Experiment Video

Updated: Jul 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Fast and efficient geo-referencing for drone-based wildlife tracking.

Imran Samad1,2, Dipani Sutaria3,4, Damien Farine5,6

  • 1Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560012, India. imransamad7@gmail.com.

Scientific Reports
|July 11, 2026
PubMed
Summary

Researchers developed a new georeferencing method using drone footage to accurately track animal movement. This technique translates pixel data into real-world coordinates, offering a robust and scalable solution for ecological studies.

Keywords:
Animal group trackingDeep learningGUI-based implementationGeo-referencingMonoplottingUnmanned aerial vehicles

Related Experiment Videos

Last Updated: Jul 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Ecology
  • Animal Behaviour
  • Remote Sensing

Background:

  • Drones offer advanced capabilities for studying animal behaviour and movement.
  • Current drone applications in animal tracking have not reached their full potential, particularly for group tracking.
  • Georeferencing presents a novel opportunity to enhance animal tracking methodologies.

Purpose of the Study:

  • To develop and validate a mathematical method for georeferencing drone video data.
  • To translate pixel positions in drone imagery to real-world latitude and longitude coordinates.
  • To assess the accuracy and robustness of the georeferencing method for animal tracking applications.

Main Methods:

  • Utilized drone position, height, and camera perspective to mathematically translate pixel coordinates to geographic coordinates.
  • Validated the georeferencing method using ground control points and GPS-equipped volunteer tracking.
  • Assessed the impact of varying camera angles and drone height on georeferencing error.

Main Results:

  • Achieved a median georeferencing error of 1.5 meters with a downward-facing camera.
  • Realized an error of 2.38 meters when tracking a GPS-equipped volunteer, comparable to state-of-the-art GPS loggers.
  • Demonstrated robustness to varying camera angles and drone movement, with minimal error increase.

Conclusions:

  • The developed georeferencing method provides accurate and robust real-world positioning for drone-based animal tracking.
  • This technique significantly enhances the potential of drones for studying fine-scale animal behaviour and movement.
  • Integration with deep learning offers automated tracking capabilities for free-ranging animals, supported by the FastGeoRef Python package.