Helping organizations restore nature and reduce poverty by using geospatial data, AI, and earth observation data.
UNICEF Innovation Fund, UNICEF Venture Fund, Conservation International
Conventional data collection methods - like household surveys and on-ground site assessments - are expensive, slow, and limited in scale. Government statistics are often outdated or too coarse to be actionable. As a result, resources and time are wasted on programs poorly matched to their locations.
Thinking Machines helps organizations working to restore nature and reduce poverty to more effectively target and monitor their programs by providing them with more novel, up-to-date, comprehensive, and reliable geospatial data derived with AI from the latest earth observation data.
Thinking Machines Data Science in mapping socioeconomic data helps to address the data invisibility of vulnerable communities by providing finer resolution data, revealing differences between small areas. They have also done several projects with telecommunications companies to use more up-to-date and detailed socioeconomic maps to identify locations of emerging demand for improved digital connectivity. Their clients in this industry use this information to make decisions about where to improve and expand digital infrastructure, giving more people access to higher-quality internet service.
Thinking Machines is working with UNICEF to develop poverty estimation models for nine countries across Southeast Asia including the Philippines, Cambodia, Myanmar, Timor Leste, Malaysia, Thailand, Vietnam, Indonesia, and Laos and AI-generated maps of air quality in Thailand. They are also working with a conservation non-profit to use AI to map aquaculture and mangrove restoration sites in Indonesia and the Philippines.