20 June 2014 THELMA: a mobile app for crowdsourcing environmental data
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Abstract
The collection of environmental light pollution data related to sea turtle nesting sites is a laborious and time consuming effort entailing the use of several pieces of measurement equipment, their transportation and calibration, the manual logging of results in the field, and subsequent transfer of the data to a computer for post-collection analysis. Serendipitously, the current generation of mobile smart phones (e.g., iPhone® 5) contains the requisite measurement capability, namely location data in aided GPS coordinates, magnetic compass heading, and elevation at the time an image is taken, image parameter data, and the image itself. The Turtle Habitat Environmental Light Measurement App (THELMA) is a mobile phone app whose graphical user interface (GUI) guides an untrained user through the image acquisition process in order to capture 360° of images with pointing guidance. It subsequently uploads the user-tagged images, all of the associated image parameters, and position, azimuth, elevation metadata to a central internet repository. Provision is also made for the capture of calibration images and the review of images before upload. THELMA allows for inexpensive, highly-efficient, worldwide crowdsourcing of calibratable beachfront lighting/light pollution data collected by untrained volunteers. This data can be later processed, analyzed, and used by scientists conducting sea turtle conservation in order to identify beach locations with hazardous levels of light pollution that may alter sea turtle behavior and necessitate human intervention after hatchling emergence.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenneth J. Hintz, Kenneth J. Hintz, Christopher J. Hintz, Christopher J. Hintz, Faris Almomen, Faris Almomen, Christian Adounvo, Christian Adounvo, Michael D' Amato, Michael D' Amato, } "THELMA: a mobile app for crowdsourcing environmental data", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911V (20 June 2014); doi: 10.1117/12.2058690; https://doi.org/10.1117/12.2058690
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