Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation

TL;DR

This paper introduces a novel generative modeling approach using latent composite flow matching to improve poaching behavior prediction, addressing data scarcity and detection issues for wildlife conservation.

Contribution

It proposes a new flow matching method that incorporates occupancy detection and prior knowledge, enhancing prediction accuracy in poaching risk modeling.

Findings

Improved predictive accuracy on real-world datasets

Effective handling of imperfect detection in poaching data

Enhanced generalization with composite flow initialization

Abstract

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.