1. Formulate predictive algorithms to understand climate change effects on agriculture better.

2. Rejuvenate land, soil and increase crop yield by curbing operational inefficiencies in existing practices with historical soil data & NDVI.

1. Identify best timelines for increasing revenue generation with historical weather data.

2. Plan and execute the most effective marketing channels in terms of conversion with weather-based sales pattern insights.

1. Design solutions compliant to net-zero emissions with hyper- local air quality data.

2. Train models with historical intelligence to increase energy efficiency

1. High-resolution weather and hyper-local air quality data to understand climate-specific historical events

2. Improve performance efficiency with behavior-based evaluation.

1. Predict & design safety measures from historical case-studies

2. Utilize historical geospatial data to plan flight routes, delays, and on-route climatic conditions.

1. Prepare to cater to varying customer demands by correlating to historical weather patterns

2. Manage business performance and aptly allocate workforce by identifying off-days and seasonal sales

1. Determine on-field analytics and player performance depending on historical weather conditions.

2. Use historical geospatial data to plan an uninterrupted sports season.

1. Increase accuracy of AI models by training with large historical datasets.

2. Innovate transcendental tech solutions with historical insights to better tackle future problems.

1. Implement construction site discipline and safety hazard measures with high-resolution weather insights.

2. Fully understand the topography of the construction site to better plan super-structures with satellite imagery.

1. Identify targeted groups of potential customers at every stage in the trip planning process.

2. Curate experiences focused on the customers and their needs with historical climate patterns.

1. Track students’ success rates based on location and ambient environmental insights.

2. Facilitate new testing processes for teaching techniques, enable detailed research projects backed with accurate historical insights.

1. Mitigate key risk factors related to sustainable investing by demonstrating results based on historical geospatial intelligence.

2. Use geospatial historical data to develop data visualization that reveals S&P 500 companies prioritize finances in environmental sustainability.

1. Address the impact of changing climate risks on underwriting, pricing, and the insurers’ bottom lines with historical geospatial data.

2. Geospatial intelligence encourages preparedness against climate-related losses that threaten the viability of insurers’ books of businesses and investment portfolios.