The Multimodal Transformer Neural Network is a sophisticated machine learning model designed to predict the occurrence of wildfires in real-time. This model integrates various advanced AI techniques and statistical methods to analyze large-scale data, such as hourly weather forecasts, and small-scale topographical data, including local terrain and vegetation conditions. By leveraging data from sources like Google Earth images, the model can determine the probability of wildfire occurrences at precise locations and times. The model was trained using historical wildfire data from the United States spanning from 1992 to 2015, enabling it to predict wildfire probabilities within a 24-hour timeframe for specific areas as small as 100 square meters.
Transformer Neural Networks
Multimodal Transformer
US Wildfire Data (1992-2015), Google Earth Images
Prediction accuracy, real-time processing capability
Cloud-based, On-premises
Yes
Yes
Real-time prediction, multimodal data integration, high spatial resolution
Yes
High-performance computing resources
Linux, Windows
Compatible with GIS systems
Data encryption, secure data transmission
ISO 27001
None
No
Limited
Research team from arXiv publication
Large-scale historical data
Low latency for real-time applications
Optimized for high-performance computing
Limited
Data privacy, environmental impact
Dependent on data quality and availability
Environmental science, public safety
Wildfire prediction, emergency response planning
Government agencies, environmental organizations
API integration with existing systems
Highly scalable
Technical support available
Service Level Agreement available upon request
Web-based dashboard
No
Limited
Subscription-based
No
Collaborations with environmental agencies
Pending
Compliant with environmental data regulations
1.0
SaaS
Yes
RESTful API for data integration
B2B
0.00
USD
Proprietary
01/03/2023
01/03/2023
+1-800-555-0199
Integration with weather forecasting systems
Yes