Geospatial ML with Amazon SageMaker (Preview)
Build, train, and deploy ML models faster using geospatial data
Access readily available geospatial data sources, including satellite imagery, maps, and location data.
Efficiently process or enrich large-scale geospatial datasets with purpose-built operations such as resampling, mosaicking, and reverse geocoding.
Accelerate model building by using built-in, pretrained deep neural network models such as land cover segmentation and cloud masking.
Analyze geospatial data and explore model predictions on an interactive map using 3D accelerated graphics with built-in visualization tools.
How it works
Assess risk and insurance claims
Measure risk, validate claims and prevent fraud, analyze damage impact from natural disasters on local economies, and track construction projects.
Inform trading strategies
Monitor financial assets globally, forecast market commodity prices, enhance your hedging or trading strategies, and mitigate the impact of price volatility.
Monitor climate change
Track deforestation and biodiversity, measure methane gas emissions, create climate resiliency plans, and improve power grid reliability.
Support sustainable urban development
Design more sustainable and livable urban environments, identify areas for land development, track traffic trends, or evaluate the feasibility of energy projects.
Maximize harvest yield and food security
View satellite images to diagnose plant health, insure and classify crops, predict harvest yield, forecast demand for agriculture produce, or detect farm boundaries.
Predict retail demand
Track high-growth city areas to improve sales or supply distribution channels, or combine location data with competitive intelligence to choose new store locations.
How to get started
AWS News Blog
Use Amazon SageMaker to build, train, and deploy ML models using geospatial data.
Learn more about Amazon SageMaker geospatial capabilities in this step-by-step guide.
Learn how farmers can optimize crop production through advanced analytics and ML.