Detecting and Mapping Invasive Species Across Riparian Corridors via Object Detection Approaches in UAV Imagery: An Example of .

Riparian zones in the United Kingdom have high species diversity but are prone to anthropogenic changes and alien plant invasions, like . However, identification can be challenging due to poor accessibility or visibility via tree canopies. UAVs provide a means to access previously inaccessible areas and capture imagery of the area.

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age.

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers.

Comparison of image intensity, local, and multi-atlas priors in brain tissue classification.

Purpose: Automated and accurate tissue classification in three-dimensional brain magnetic resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity in homogeneity, and partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative study on the contributions of three commonly used image information priors for tissue classification in normal brains: image intensity, local, and multi-atlas priors.