1 research outputs found
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery
Illegal wildlife poaching is driving the loss of biodiversity. To combat
poaching, rangers patrol expansive protected areas for illegal poaching
activity. However, rangers often cannot comprehensively search such large
parks. Thus, the Protection Assistant for Wildlife Security (PAWS) was
introduced as a machine learning approach to help identify the areas with
highest poaching risk. As PAWS is deployed to parks around the world, we
recognized that many parks have limited resources for data collection and
therefore have scarce feature sets. To ensure under-resourced parks have access
to meaningful poaching predictions, we introduce the use of publicly available
remote sensing data to extract features for parks. By employing this data from
Google Earth Engine, we also incorporate previously unavailable dynamic data to
enrich predictions with seasonal trends. We automate the entire
data-to-deployment pipeline and find that, with only using publicly available
data, we recuperate prediction performance comparable to predictions made using
features manually computed by park specialists. We conclude that the inclusion
of satellite imagery creates a robust system through which parks of any
resource level can benefit from poaching risks for years to come.Comment: Presented at NeurIPS 2020 Workshop on Machine Learning for the
Developing World. 4 pages, 1 page references. 4 figures, 1 tabl