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New OWL framework sets SOTA for aerial wildlife surveys

By PulseAugur Editorial · [2 sources] ·

Researchers have developed the Overhead Wildlife Locator (OWL), a new weakly supervised framework for aerial wildlife surveys. OWL offers three variants—OWL-C, OWL-T, and OWL-D—each tailored for different survey conditions, from sparse fixed-wing imagery to dense UAV data. The OWL-D variant, utilizing a frozen DINOv3 ViT-H+/16 encoder, achieves state-of-the-art performance on several datasets and has been successfully applied to a real-world caribou census, demonstrating its operational readiness. The project also releases code, model weights, and new annotated datasets for caribou surveys. AI

IMPACT This research advances weakly supervised learning for wildlife monitoring, potentially reducing costs and improving accuracy in ecological surveys.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark for computer vision tasks in aerial wildlife surveys.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New OWL framework sets SOTA for aerial wildlife surveys

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The cluster contains a research paper detailing a new framework and benchmark for computer vision tasks in aerial wildlife surveys.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Isai Daniel Chac\'on, Zhongqi Miao, Bruno Demuro, Caleb Robinson, Rahul Dodhia, Lasha Otarashvili, Jason Holmberg, Kirk Larsen, Howard Frederick, Nathan J. Pamperin, Pablo Arbel\'aez, Juan M. Lavista Ferres ·

    Overhead Wildlife Locator (OWL): Benchmarking Weakly Supervised Learning for Aerial Wildlife Surveys

    arXiv:2606.13911v1 Announce Type: new Abstract: Automated aerial wildlife surveys increasingly rely on deep learning, yet standard object detectors require bounding-box annotations, reported to be up to seven times slower and three times more expensive to produce than point-level…

  2. arXiv cs.CV TIER_1 English(EN) · Juan M. Lavista Ferres ·

    Overhead Wildlife Locator (OWL): Benchmarking Weakly Supervised Learning for Aerial Wildlife Surveys

    Automated aerial wildlife surveys increasingly rely on deep learning, yet standard object detectors require bounding-box annotations, reported to be up to seven times slower and three times more expensive to produce than point-level labels. To address this bottleneck, we introduc…

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