Historical spatial data by gspatial.ai is a directory of geospatial intelligence collected from hundreds of sensors, including our own, from multiple satellites all the way from 1991 to date. Geospatial insights available for every point on earth contain hourly data for weather, pollen, and air quality at high resolutions paired with satellite imagery.
Yield Management - Gain perspective of the existing issues in your farm, explore and identify where and when problems are most likely to occur, pinpoint their exact cause, and maximize the efficiency of crop yield.
Increasing Sales - Geospatial intelligence from years ago can help marketers understand climate patterns from years ago and predict their sales, manage inventory and build sustainable solutions for precision marketing campaigns.
Digital marketing - Actionable intelligence from Gspatial.ai when paired with customer behavior data can help companies personalize their content, cater to the exact requirements of their customers and implement hyper-personalization in marketing efforts.
Putting the Data to Use - Geospatial insights enable enterprises to train their models efficiently and generate AI-driven insights to predict & forecast future risks.
Gspatial’s intelligence is curated from hundreds of earth observatory satellites and sensor information from across the globe. These sources capture earth’s behavioral, topographical data and satellite imagery in its raw form. The final insights delivered to users largely involve data that is further processed with machine learning and AI algorithms built from scratch.
Users can request data from gspatial’s simplified, user-friendly console in minimum steps. The only pre-requisite for this process is to understand the kind of data you need to make your engines run. After your data requirements are defined, log in to the gspatial.ai console and make the request, or follow the simple steps.
1. Create an account in gspatial.ai
2. Go to console
3. Select your desired category, parameters, and time range for the data you need
4. Verify the order details in the cart
5. Make the payment and download geospatial intelligence.
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.