Whiteshield AI Vision Navigator
How can policymakers estimate socioeconomic parameters more quickly and move to real-time, granular policy action?
This is the question the AI Vision Navigator seeks to answer. Below you will find a summary of our presentation to the European Capital of Smart Tourism upon invitation from OpenAI.
1. Process daylight satellite images
2. Analyse cell network towers
3. Score nightlight infrared radiance
Our data scientists and economists compose a formula to determine an economic activity score for each tile of geographic area, which can be visualized in a number of different ways for policymakers.
For example, below you can explore Cape Town’s gross national product breakdown by km2.
Explore Cape Town’s GDP per km2, estimated by Whiteshield’s AI Vision Navigator
Other use cases exist across a wide range of socioeconomic parameters. The AI Satellite models are adapted to the policy question at hand and can span a wide range of socioeconomic parameters.
Using academic research, benchmarks, or training generative AI models, our economists tailor each model to the specific policy use case.
- Urban density
- Industrial areas
- Commercial zones
- Transport infrastructure
- Agriculture
- Construction activity
- Education institutions
- Low and high-quality housing
- Road network complexity
- Road quality
- Transportation networks
- Office buildings
- Bar land
- Economic complexity
Challenge: Road quality improvements are constantly underway, and the government must decide which roads to prioritise for construction. How can we help governments identify which locations are most in need of improvement?
AI Vision solution: We use satellite images to triangulate information. Three key pieces of information to consistently prioritise roadworks- 1) Road quality, 2) Population density, and 3) Road usage. Roads that are poor in quality while in central locations that are frequently used by citizens are prioritized first to make the largest impact on citizen well-being and satisfaction.
Challenge: To boost local economic output, governments need to optimize productivity from agricultural land and farming. How can we support them to identify current challenges and formulate positive interventions?t
AI Vision solution: Using satellite imagery, we identified different types of agricultural activities, such as orchards, crops, and cattle farming across a large geographic area. This allowed us to understand how land is being used and develop recommendations on crop selection and farmer concentration to optimize land use and productivity.
Challenge: Severe weather events are becoming increasingly frequent. In some countries, bouts of heavy rainfall are impacting transportation systems, causing damage to commercial and residential properties, and impacting GDP across various sectors.
AI Vision solution: Using satellite imagery to identify roads that have become unusable from the rain, we estimate the impact on retail in the form of economic losses. These results can then also inform decisions on the development of rain infrastructure to limit the impact of weather events around critical economic networks.
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