  Recent advances in deep learning technology have triggered radical progress
in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles
(ASVs) that are regularly used for surveillance, monitoring and other routine
tasks can benefit from this autonomy. Long haul deep sea transportation
activities are additional opportunities. These two use cases present very
different terrains -- the first being coastal waters -- with many obstacles,
structures and human presence while the latter is mostly devoid of such
obstacles. Variations in environmental conditions are common to both terrains.
Robust labeled datasets mapping such terrains are crucial in improving the
situational awareness that can drive autonomy. However, there are only limited
such maritime datasets available and these primarily consist of optical images.
Although, Long Wave Infrared (LWIR) is a strong complement to the optical
spectrum that helps in extreme light conditions, a labeled public dataset with
LWIR images does not currently exist. In this paper, we fill this gap by
presenting a labeled dataset of over 2,900 LWIR segmented images captured in
coastal maritime environment under diverse conditions. The images are labeled
using instance segmentation and classified in seven categories -- sky, water,
obstacle, living obstacle, bridge, self and background. We also evaluate this
dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and
provide detailed analysis of its efficacy. While the dataset focuses on the
coastal terrain it can equally help deep sea use cases. Such terrain would have
less traffic, and the classifier trained on cluttered environment would be able
to handle sparse scenes effectively. We share this dataset with the research
community with the hope that it spurs new scene understanding capabilities in
the maritime environment.
