  We present MONET, a new multimodal dataset captured using a thermal camera
mounted on a drone that flew over rural areas, and recorded human and vehicle
activities. We captured MONET to study the problem of object localisation and
behaviour understanding of targets undergoing large-scale variations and being
recorded from different and moving viewpoints. Target activities occur in two
different land sites, each with unique scene structures and cluttered
backgrounds. MONET consists of approximately 53K images featuring 162K manually
annotated bounding boxes. Each image is timestamp-aligned with drone metadata
that includes information about attitudes, speed, altitude, and GPS
coordinates. MONET is different from previous thermal drone datasets because it
features multimodal data, including rural scenes captured with thermal cameras
containing both person and vehicle targets, along with trajectory information
and metadata. We assessed the difficulty of the dataset in terms of transfer
learning between the two sites and evaluated nine object detection algorithms
to identify the open challenges associated with this type of data. Project
page: https://github.com/fabiopoiesi/monet_dataset.
