WildBox provides over 237k 3D wildlife annotations from drone video and benchmarks reveal zero-shot 3D detection at 0 AP but fine-tuned performance of 8.68 AP-BEV and 13.17 AP3D, with depth estimation causing most errors.
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FAIR^2 Drones is a proposed standard that adds platform metadata and annotation specifications to existing FAIR and AI-ready frameworks so wildlife drone datasets can support ecological analysis, robotics development, and computer vision benchmarking simultaneously.
DeepForestVisionV2 expands camera-trap classification to 64 classes and reports improved accuracy and taxon coverage on held-out benchmarks spanning forest interiors, riverbanks, and park edges.
citing papers explorer
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WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of African Savanna Wildlife
WildBox provides over 237k 3D wildlife annotations from drone video and benchmarks reveal zero-shot 3D detection at 0 AP but fine-tuned performance of 8.68 AP-BEV and 13.17 AP3D, with depth estimation causing most errors.
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FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
FAIR^2 Drones is a proposed standard that adds platform metadata and annotation specifications to existing FAIR and AI-ready frameworks so wildlife drone datasets can support ecological analysis, robotics development, and computer vision benchmarking simultaneously.
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DeepForestVisionV2: Ecology-Driven Taxonomy Expansion for Camera-Trap Monitoring in African Tropical Forests
DeepForestVisionV2 expands camera-trap classification to 64 classes and reports improved accuracy and taxon coverage on held-out benchmarks spanning forest interiors, riverbanks, and park edges.