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A Grassroots Remote Sensing Toolkit Using Live Coding, Smartphones, Kites and Lightweight Drones.

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  • 1Environment and Sustainability Institute, University of Exeter, Penryn Campus, Cornwall, United Kingdom.

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Summary
This summary is machine-generated.

This study developed an Android app utilizing smartphone sensors for aerial photography and spatial data collection. The application enables easy creation of GIS-ready images from drones and kites for grassroots mapping.

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Area of Science:

  • Geospatial Science
  • Mobile Application Development
  • Remote Sensing

Background:

  • Modern smartphones possess integrated sensors (GPS, accelerometer, compass, camera) suitable for geospatial data capture.
  • Grassroots mapping initiatives require accessible and cost-effective tools for acquiring aerial imagery and spatial metadata.
  • Existing solutions may lack the flexibility for field customization or integration with open-source geospatial workflows.

Purpose of the Study:

  • To develop an Android application enabling automatic aerial photograph and spatial metadata capture using smartphone sensors.
  • To facilitate the creation of ready-to-use spatial data for grassroots mapping applications.
  • To demonstrate the integration of the application with open-source geospatial toolkits for generating GIS-ready products.

Main Methods:

  • Development of an Android application using a visual coding 'scheme blocks' framework for user customization.
  • Integration of smartphone sensors (GPS, accelerometer, compass, camera) for automated data acquisition.
  • Testing the application on lightweight aerial platforms (kites, drones) with varying smartphone hardware.
  • Utilizing open-source geospatial toolkits for processing captured metadata into GeoTIFF images.

Main Results:

  • Successful capture of aerial photographs and spatial metadata using an Android application on drones and kites.
  • Demonstration of the app's functionality on different Android smartphones, highlighting sensor set variations.
  • Identification that stable platforms (single-line kites, gliding drones) yield optimal results, while engine vibrations require dampening.
  • Generation of GIS-ready GeoTIFF images from the app's metadata using open-source processing workflows.

Conclusions:

  • The developed Android application effectively leverages smartphone sensors for accessible aerial data capture.
  • The application, coupled with open-source tools, provides a viable workflow for generating GIS-ready data for grassroots mapping.
  • The tool offers potential applications in disaster relief, education, and democratic mapping initiatives.