We are demonstrating the tight connection between ending slavery and reducing environmental destruction. Our preliminary research shows that if slavery were a country, it would have a population of some 46 million people and the gross domestic product of Angola (in global terms a small and poor nation), yet would be the third largest emitter of CO2 (2.54 billion tons per year) in the world after China (7.39 billion tons) and the United States (5.58 billion tons). Responding to this, we are completing the world’s largest study on the relationship between slavery and ecosystems. This work will: Compile, synthesise and integrate spatial data on the landscape changes that result from slavery activities and calculate the environmental costs of these activities and the potential gains that stem from curtailing slavery, with a focus on carbon sequestration and other ecosystem services. Explore the values associated with environmental gains, their capacity to be captured in environmental markets, and their ability to help fund slavery prevention and abolition efforts. Explore links between ecological resilience and human vulnerability as a precondition to enslavement.
We are home to the world’s first Geospatial Slavery Observatory. The majority of today’s slaves live in developing countries where many slave-based activities are visible in satellite imagery (for example brick kilns, mines, fisheries and farms). By identifying slavery locations using geospatial intelligence, we answer the demand within the development and human rights communities for scientific data that can underpin policy formation and humanitarian operations. Our process is to: • Compile, synthesise and integrate spatial data to detect and eventually prevent slavery. • Develop (automated) methods with as much data as possible at as low as possible cost, with known levels of uncertainty. • Act as a conduit for all observations of slavery activity. Employing state-of-the-art techniques from geoinformatics and data modelling, geospatial intelligence, and location-based services, we conduct comparison and cross-corroboration of archival satellite imagery with various open source data, and use imagery analysis methodologies. We also use our expertise on volunteered geographic information to enable quality crowd-sourcing, and are applying machine-learning techniques that automate identification via a prototype feature extraction algorithm.Our work was featured in a Telegraph article in October 2016. We are now developing new pilots in Ghana (fishing), India, Nepal and Pakistan (brick kilns), Thailand (fishing), Brazil (charcoal camps) and the Democratic Republic of the Congo (mining).