  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.
