Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats
Lucia Gordon, Nikhil Behari, Samuel Collier, Elizabeth Bondi-Kelly,, Jackson A. Killian, Catherine Ressijac, Peter Boucher, Andrew Davies, Milind, Tambe

TL;DR
This paper introduces a novel active learning approach using multimodal imagery to efficiently map rhino middens, providing valuable insights for conservation efforts while significantly reducing labeling effort.
Contribution
The paper presents MultimodAL, an active learning system that effectively detects rhino middens using multimodal data, overcoming class imbalance issues and reducing labeling time.
Findings
Achieved 94% reduction in labeling effort.
Mapped rhino midden distribution, revealing clustering patterns.
Enabled targeted anti-poaching strategies.
Abstract
Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhinos' movement is crucial to their protection but has unfortunately proven difficult because rhinos are elusive. Therefore, instead of tracking rhinos, we propose the novel approach of mapping communal defecation sites, called middens, which give information about rhinos' spatial behavior valuable to anti-poaching, management, and reintroduction efforts. This paper provides the first-ever mapping of rhino midden locations by building classifiers to detect them using remotely sensed thermal, RGB, and LiDAR imagery in passive and active learning settings. As existing active learning methods perform poorly due to the extreme class imbalance in our dataset, we design MultimodAL, an active learning system employing…
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