Semantic Data Integration For Forest Observatory
Project Overview
Building a linked-data Forest Observatory that unifies heterogeneous bioscience and environmental datasets to help predict poaching activity in Sabah, Malaysia.
Poaching and animal trafficking are significant challenges around the world. Anti-poaching efforts are always underfunded and under-resourced. Law enforcement officers cannot keep up with the large number of poachers trying to kill and capture animals. Due to limited manpower, they cannot patrol and protect vast areas of land. We will semantically integrate data gathered by bio-science researchers and environmental scientists to predict where the poaching activities will occur in the future. Our data-driven prediction models will tell areas and time frames that are highly likely to have poaching incidents. Therefore, law enforcement agencies can deploy their limited resources into those areas. This project will focus on the Lower Kinabatangan Wildlife Sanctuary, Sabah, Malaysia. This project collaborates between the School of Computer Science and the School of Biosciences (and its Danau Girang Field Centre; DGFC) at Cardiff University.
Our approach is to develop a Forest Observatory and develop data-driven predictive analytics to predict poaching incidents. Forest Observatory is a Linked Datastore that integrates heterogeneous data. Collecting data in forests is much more challenging than in cities due to the lack of infrastructure. However, while we expect to deploy an Internet of Things (IoT) infrastructure to enable poaching monitoring, we aim to utilise already collected data sets to develop predictive poaching models. For example, DGFC has data sets collected by researchers for wildlife species monitoring over the last decade, such as animal collar data, camera traps, satellite imagery, LiDAR and environmental data, with each data set generated using different time frames durations, geographic areas etc.
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