Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery

by   Rachel Guo, et al.

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.


page 1

page 2

page 3

page 4


Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery

Wilderness areas offer important ecological and social benefits, and the...

A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features

The necessity of sustainable development for landscapes has emerged as a...

TorchGeo: deep learning with geospatial data

Remotely sensed geospatial data are critical for applications including ...

Using maps to predict economic activity

We introduce a novel machine learning approach to leverage historical an...

Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics

The shear volumes of data generated from earth observation and remote se...

Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas

Satellite Remote Sensing Technology is becoming a major milestone in the...

Towards Sustainable Census Independent Population Estimation in Mozambique

Reliable and frequent population estimation is key for making policies a...