GeoLab solutions have been successfully applied in diverse sectors, demonstrating impact in global development. Below are representative use cases:
- Environment:
- The Environmental and Social Framework Risk Assessment Toolkit enhanced the efficiency and quality of the Mindanao Transport Connectivity Improvement Project by aiding specialists with accessible, comprehensive geospatial and historical data. It quickly flagged environmental and social risks, generating relevant ESF reports for project sites, and providing valuable preliminary insights into issues by integrating hundreds of public and World Bank data sources, including climate, biodiversity, and conflict information for each Environmental and Social Standard (ESS).
- Agriculture:
- By leveraging object detection with drone imagery, a cocoa farm in Ghana was able to calculate zonal statistics using the NDVI (Normalized Difference Vegetation Index) for each individual tree and conduct statistical analyses across the farm’s geometry. The outcomes included:
- Environmental Monitoring: Object detection enabled ongoing assessment of farm and forest health, early detection of tree diseases, and evaluation of how environmental changes impact tree populations.
- Agriculture and Forestry Management: Farmers and foresters used these insights to monitor tree growth and health, optimize harvest strategies, and manage resources more effectively.
- Disaster Management: Technology helped evaluate post-disaster damage to forests and farmlands from events like wildfires or floods, supporting swift recovery efforts.
- Biodiversity Conservation: The ability to identify different tree species contributed to preserving biodiversity and protecting endangered species.
- Urban Planning: In urban settings, object detection assisted in managing and maintaining healthy green spaces and urban forests.
- Disaster Risk Management:
- By using GeoAI - Change Detection tool with satellite imagery users can receive rapid, evidence-based analysis of flood impacts on people, cropland, urban areas, etc. The key priorities that this technology supports are rapid disaster risk management in the case of flooding, impact monitoring, poverty reduction by enabling faster disaster response to protect vulnerable communities, social protection through data-driven targeting of post-flood assistance, etc.