Outline

Implement change detection and early-warning models for deforestation and wildfires. • Apply techniques for wildfire detection: TIR/VIIRS anomaly flags, smoke segmentation, and alert thresholds. • Analyze deforestation using time-series change (BFAST/Delta), semantic segmentation, and accuracy assessment.

Outline

Fuse EO (AOD) with ground air quality data, perform bias correction, and nowcast under missing data scenarios. • Estimate carbon and emissions using multispectral+SAR biomass and FRP→emissions relationships, including uncertainty bands. • Explore the Silvanet & Silvaguard case study to understand real-world application of early warning and integration.

Outline

Fuse multi-resolution data (UAV + EO + IoT) for robust signal detection in environmental monitoring. • Address data gaps, accuracy limits, and scaling issues in low-resource contexts effectively. • Implement deployment strategies at scale including robustness, drift monitoring, human-on-the-loop, and low-bandwidth constraints.

Outline

Create a STAC-indexed Area of Interest (AOI) data lake incorporating Sentinel-2, VIIRS, and drone scene data. • Run a complete pipeline: cloud mask → wildfire/smoke flags → forest-loss polygons (with confidence). • Fuse EO + ground AQ data to produce a daily bias-corrected PM map.

Outline

Estimate stand-level carbon with basic uncertainty for environmental impact assessment. • Publish a lightweight dashboard displaying alerts, loss, AQ index, and carbon snapshots for decision support. • Prototype a minimal alerting workflow and web map for efficient communication of environmental insights.