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 introduce the Overhead Wildlife Locator (OWL), a weakly supervised density-estimation framework with three variants: OWL-C, a fully convolutional model for high-throughput screening; OWL-T, a Swin-augmented hybrid for heterogeneous, cluttered scenes; and OWL-D, built on a frozen DINOv3 ViT-H+/16 encoder with a DPT-style fusion decoder. We benchmark all three against POLO, YOLOv11n, and YOLOv11l across five public aerial datasets, from sparse fixed-wing savanna surveys to dense UAV paddock imagery, and against the published HerdNet baseline on its native Delplanque split. OWL-D sets a new state of the art on Delplanque (0.934 AP vs. HerdNet's 0.840) and records the highest AP on four of the five datasets. Performance is regime-dependent: on the extreme-density SheepCounter UAV dataset the hybrid OWL-T leads (0.978 AP) and the convolutional variants attain the lowest counting error, whereas the foundation-based OWL-D degrades, indicating which variant suits which survey type. We further validate operational readiness on the Alaska Department of Fish and Game's 2022 Central Arctic Caribou census: under cross-herd and cross-temporal transfer, OWL-C fine-tuned on the 2017 Porcupine Caribou Herd split attains F1 = 0.965 on a held-out patch test set, with a signed count error of +3.1% aggregated across the released test patches. We release the OWL code, model weights, and the annotated Porcupine Caribou Herd 2017 (PCH) and Central Arctic Herd 2022 (CAH) patches, the first open patch-level datasets for large-scale caribou aerial surveys, at https://github.com/microsoft/MegaDetector-Overhead.

翻译：自动化航空野生动物调查日益依赖深度学习，然而标准目标检测器需要边界框标注，据报道其生成速度比点级标签慢七倍、成本高三倍。为突破这一瓶颈，我们提出天基野生动物定位器（OWL）——一种弱监督密度估计框架，包含三个变体：OWL-C，一种适用于高通量筛选的全卷积模型；OWL-T，一种面向异质杂乱场景的Swin增强混合模型；以及OWL-D，基于冻结的DINOv3 ViT-H+/16编码器与DPT风格融合解码器构建。我们针对所有三个变体，在五个公开航空数据集（从稀疏固定翼稀树草原调查到密集无人机围场影像）上，与POLO、YOLOv11n和YOLOv11l进行基准测试，并在其原生Delplanque分割上与已发表的HerdNet基线对比。OWL-D在Delplanque数据集上创下新最优结果（AP 0.934，相较HerdNet的0.840），并在五个数据集的四个中取得最高AP。性能呈现机制依赖性：在极端密集的SheepCounter无人机数据集上，混合模型OWL-T表现最优（AP 0.978），卷积变体取得最低计数误差，而基于基础模型的OWL-D性能下降，表明不同变体适用于不同调查类型。我们进一步在阿拉斯加渔猎局2022年中央北极驯鹿普查中验证操作准备性：经过2017年波丘派恩驯鹿群分割微调的OWL-C，在跨兽群与跨时域迁移下，于保留测试斑块集上达到F1=0.965，测试斑块聚合后的符号计数误差为+3.1%。我们开源OWL代码、模型权重，以及首批面向大规模驯鹿航空调查的公开斑块级数据集——2017年波丘派恩驯鹿群（PCH）与2022年中央北极兽群（CAH）斑块，详见https://github.com/microsoft/MegaDetector-Overhead。