Siamese Learning-based Monarch Butterfly Localization

TL;DR

This paper introduces a Siamese learning-based GPS-less localization method for Monarch butterflies using light and temperature sensors, significantly improving accuracy over previous techniques especially around equinoxes.

Contribution

It presents a novel deep learning sensor fusion model that enhances Monarch butterfly localization accuracy without GPS, addressing limitations of prior methods during equinox periods.

Findings

Achieved mean absolute error of 1.416° in latitude

Achieved mean absolute error of 0.393° in longitude

Outperformed prior localization methods

Abstract

A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking. Prior methods suffer from the location-independent day length during the equinox, resulting in high localization errors around that date. This work proposes a new Siamese learning-based localization model that improves the accuracy and reduces the bias of daily Monarch butterfly localization using light and temperature measurements. To train and test the proposed algorithm, we use $5658$ daily measurement records collected through a data measurement campaign involving 306 volunteers across the U.S., Canada, and Mexico from 2018 to 2020. This model achieves a mean absolute error of $1.416^\circ$ in latitude and $0.393^\circ$ in longitude coordinates outperforming the prior method.