Modeling Economic Well-Being Through Machine Learning and Satellite Imagery
In this two-month project, a global team of 50 changemakers built a model on economic well-being in India. The project has been hosted by the World Resources Institute and the team used publicly available satellite imagery and other ground data. Technical support has been given by Activeloop through a fast and simple framework for building and scaling data pipelines.
Economic well-being is a broad concept that goes beyond statistical metrics. When you plan on moving to another place, do you primarily check complex economic measures like the GDP of that region? When making such decisions, generally what matters to most people, in layman’s terms, is the standard of living. The standard of living refers to the necessities, comforts, and luxuries that a person is likely to enjoy. It refers to the quantity and quality of their consumption. The fundamental reason for differences in the standards of living between regions is the difference in their levels of economic productivity. Hence, it is important for nations to record a source of primary data. This provides valuable information for planning and formulating policies by governments, international agencies, scholars, business people, industrialists, and many more.
This data is usually collected through on-site surveys that need to be performed across vast areas. A list of questions is asked from families and individuals which leads to a huge database.
There are several problems in using old methods like surveys to measure changes in economic outcomes over time. For example, they do not repeatedly measure outcomes at the same locations and temporal changes over a few-year time span are likely to be small relative to cross-sectional differences. Also, any random noise in each year’s survey will diminish the signal in these changes [Yeh et al.]. There are also risks of abuse of data and corruption. The temporal variation of factors affecting economic well-being makes it even more difficult to compare the progress of regions.
To cope with these issues, the team trained AI models to learn features related to the changing agricultural and urban landscape. The approach provided a more accurate understanding of economic well-being in India (check an example visualizations below).
Distribution of Districts by the Overall Development Index for each indicator of economic well-being.
The project falls under the UN´s Sustainable Development Goal 8, which is to “promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all and all” by the year 2030.
This project has been hosted with our friends at
WRI is a global research organization that spans more than 60 countries and more than 1,000 experts and staff working closely with leaders to turn big ideas into action to sustain our natural resources—the foundation of economic opportunity and human well-being. Their work focuses on seven critical issues at the intersection of environment and development: climate, energy, food, forests, water, cities, and the ocean.
Activeloop (www.activeloop.ai), is a company backed by Y Combinator and a member of NVIDIA’s prestigious Inception program. Activeloop streamlines data scientists’ data aggregation, preparation, and automating, as well as optimizes the training of machine learning models. The company’s open-source Hub package is the fastest way to access and manage datasets for PyTorch and TensorFlow. Thanks to the package, you can build scalable data pipelines in no time.