12 videos showcasing the wildfire intelligence platform — from real-time satellite fire detection to AI-powered risk prediction, built to protect communities and accelerate climate resilience.
End-to-end walkthrough of the ClimaIQ wildfire intelligence platform and its 9-service microservices architecture.
Full tour of the ClimaIQ 9-service microservices platform. Demonstrates the Data Ingestion Service (26 live connectors including NASA FIRMS, NOAA HRRR, EPA AirNow), Data Processing Service (real-time normalization and enrichment), Hybrid Data Storage (4-tier HOT/WARM/COLD/ARCHIVE with sub-100ms hot-tier queries), Streaming Service (Apache Kafka with 50K events/sec throughput), Query Service (intelligent query routing), Cache Layer (Redis with 94% hit rate), and the Ensemble AI Engine combining CNN satellite analysis, LSTM temporal prediction, Random Forest classification, and FireSat physics-informed models at 92% ensemble accuracy.
Self-service data exploration portal providing unified access to all wildfire data sources with ML analytics integration.
Walkthrough of the self-service data exploration portal providing unified access to all 26 wildfire data sources. Features visual query builder with geographic, temporal, and source-type filters; 45 REST API endpoints for programmatic access; and integrated AI/ML analytics including a Random Forest classifier for fire risk prediction, Gradient Boosting with physics-informed fire behavior modeling, and multi-objective optimization for resource deployment. Demonstrates ML-powered 72-hour risk forecasting, data export in multiple formats (CSV, GeoJSON, NetCDF), built-in data quality scoring, and a comprehensive security framework with role-based access control, API key management, and audit logging for NIST compliance.
Role-based interfaces demonstrating multi-stakeholder value — from fire chiefs to data scientists to platform administrators.
Real-time command dashboard built for fire agency leadership. Interactive California wildfire map with live satellite fire detections (NASA FIRMS MODIS/VIIRS), current conditions panel showing temperature, humidity, and wind data from nearest weather stations, Fire Weather Index calculation, ML-powered fire risk assessments (low/medium/high/extreme) driven by the ensemble AI engine, resource deployment tracker, and incident management workflow. All data refreshes every 30 seconds for real-time situational awareness.
AI/ML development environment achieving 94.7% model accuracy across wildfire prediction tasks. Features a full ML pipeline with Random Forest, XGBoost, Neural Network, and SVM algorithms for fire risk prediction, trained on 2.3M samples with 47 active experiments. Includes Python-based model training with automated hyperparameter optimization (n_estimators=150, max_depth=12), Jupyter notebook environment for fire season pattern analysis and fire spread simulation, TensorBoard integration for training visualization, and Google Research FireBench for physics-informed fire behavior modeling. Provides access to 7 curated AI-ready datasets spanning 1970–2024 including MODIS/Landsat satellite imagery, NDVI vegetation health indices, live fuel moisture, and socioeconomic risk factors.
Business intelligence portal for wildfire risk analysis with 94.2% AI prediction accuracy. Demonstrates access to 2.4M+ processed data points, predictive analytics and statistical analysis powered by the ensemble ML engine, customizable filters by region/time/risk-level, geospatial intelligence with interactive trend visualization, automated report generation with export capabilities, and cross-source correlation analysis for insurance risk assessment and utility infrastructure planning.
System administration and compliance dashboard. Shows management of 127 platform users across 5 roles, granular permission controls, real-time service health monitoring (99.7% uptime across 9 services), comprehensive audit logging for regulatory compliance, security policy configuration, and API usage tracking — demonstrating enterprise-grade platform governance.
Operational dashboards proving the platform processes live wildfire data at scale with sub-second latency.
Deep dive into the AI-driven fire detection pipeline. Shows live processing of satellite data from 5 NASA instruments (MODIS Terra/Aqua, VIIRS SNPP/NOAA-20/NOAA-21), intelligent spatial-temporal deduplication algorithm that consolidates overlapping detections into unique fire events, ML-based confidence scoring and filtering (80%+ threshold), geographic clustering algorithm, real-time fire activity map with time-series analysis, and metrics proving detection-to-dashboard latency under 30 seconds. Demonstrates how the system reduces raw satellite noise by 97% to surface only confirmed fire incidents.
Platform usage analytics showing how different stakeholders access wildfire data and AI model outputs. Tracks query patterns across 5 roles (fire chiefs, scientists, analysts, emergency managers, administrators), monitors sub-200ms average query response time, tracks ML model training workloads and data scientist experiment activity, identifies most-requested data sources and time periods, measures self-service adoption rates, and demonstrates the query routing optimization that maintains performance under concurrent multi-tenant load.
Live monitoring dashboard for the data ingestion infrastructure. Visualizes the real-time status of all 26 data source connectors, per-connector latency and throughput metrics, data fidelity scoring, automatic retry and failover handling, ingestion queue depth monitoring, and the validation pipeline that ensures data quality before storage — demonstrating the reliability needed for mission-critical wildfire operations.
Monitoring dashboard for the 4-tier storage system (HOT/WARM/COLD/ARCHIVE). Demonstrates sub-100ms query performance on the PostgreSQL hot tier, automatic lifecycle management that migrates aging data through tiers, 97.5% cost reduction from hot to archive storage, real-time capacity tracking and growth projections, cross-tier query federation, and the replication strategy ensuring zero data loss.
Comprehensive technical presentations covering the complete architecture and implementation of ClimaIQ's data ingestion and storage systems.
Complete technical presentation covering the architecture and implementation of all 26 data source connectors. Walks through each data source category (satellite/remote sensing, weather/atmospheric, ground-based sensors, geospatial/terrain, air quality, historical records), the connector framework design, data normalization pipeline, quality validation, error handling, and scalability considerations for national-scale wildfire intelligence operations.
Comprehensive technical presentation of the hybrid storage architecture. Covers the 4-tier design rationale, PostgreSQL (hot) to TimescaleDB (warm) to Parquet/S3 (cold) to Glacier (archive) pipeline, automatic lifecycle policies, cross-tier query optimization, disaster recovery with 3x replication, capacity planning models, and cost analysis showing 97.5% storage cost reduction — validated for climate data platforms requiring long-term retention and real-time access.