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Operational
Global Landslide Detection for Rapid response (GLiDEr)
Foundation models and High-resolution commercial imagery enable near-real-time detection of new landslides following extreme events.
Organizations
- Launch Date
- --
- Class
- Data/Image Computer Models
- Website
- --
- Program Lead
- Software Developer
- Project Scientist
Key Staffs
- Goal: Rapidly map new landslides after disasters to support emergency response, replacing slow manual mapping and overcoming the limits of coarse public imagery.
- Why a Foundation Model: Landslide labels are expensive and human-error prone (lots of interpretability challenges by SME, see example on the right where SME miss several landslides).
- What’s New: A change-detection deep learning framework using Planet high-resolution imagery identifies event-induced landslides only, separating new damage from older terrain scars.
- Technical Innovation: Hybrid architecture combining DinoV3 + diffusion-based terrain reconstruction to improve change detection and reduce false positives. Enables near-real-time disaster mapping, supports rapid response decisions, and delivers an open, globally transferable system for landslide hazards and other change-detection applications.