用生成模型预测偷猎行为，提升保护巡逻效率
Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation
- 在隐空间中结合占用检测模型，推断隐藏的偷猎活动状态
- 用线性模型先验初始化复合流，数据稀缺下仍保持高精度
- 适用于野生动物保护机构，尤其适合数据少的偏远地区
偷猎对野生动植物和生物多样性构成严重威胁。有效减少偷猎的关键一步是预测偷猎者行为，从而指导巡逻规划等保护行动。现有基于线性模型或决策树的预测方法难以捕捉复杂的非线性时空模式。生成建模的新进展，特别是流匹配（flow matching），提供了更灵活的替代方案。然而，在真实偷猎数据上训练此类模型面临两大挑战：偷猎事件检测不完全和数据有限。为应对检测不完全问题，我们结合流匹配与基于占用的检测模型，在隐空间中训练流以推断潜在的占用状态。为缓解数据稀缺问题，我们采用从线性模型预测结果初始化的复合流，而非扩散模型中常用的随机噪声，注入先验知识以提升泛化能力。在乌干达两个国家公园的数据集上评估显示，预测准确率持续提升。
原文摘要 · Abstract (English)
Poaching poses significant threats to wildlife and biodiversity. A valuable step in reducing poaching is to forecast poacher behavior, which can inform patrol planning and other conservation interventions. Existing poaching prediction methods based on linear models or decision trees lack the expressivity to capture complex, nonlinear spatiotemporal patterns. Recent advances in generative modeling, particularly flow matching, offer a more flexible alternative. However, training such models on real-world poaching data faces two central obstacles: imperfect detection of poaching events and limited data. To address imperfect detection, we integrate flow matching with an occupancy-based detection model and train the flow in latent space to infer the underlying occupancy state. To mitigate data scarcity, we adopt a composite flow initialized from a linear-model prediction rather than random noise which is the standard in diffusion models, injecting prior knowledge and improving generalization. Evaluations on datasets from two national parks in Uganda show consistent gains in predictive accuracy.
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