Proactive Geohazard Monitoring
**GeoVisual-Detect** is an intelligent, end-to-end software engine that ingests visual data from cameras and produces actionable geohazard alerts. It works by establishing a high-fidelity visual baseline of a slope and then continuously watching for change.
The Problem: A Critical "Data Gap"
Landslides and avalanches pose significant threats, but traditional monitoring methods fall short, leaving a dangerous gap in our ability to detect disasters before they happen.
Traditional Methods
- In-Ground Sensors: Expensive and difficult to scale across large areas.
- Satellite Monitoring: Periodic, not continuous. Rapid-onset events can be missed entirely.
- Manual Inspection: Relies on subjective human observation, which isn't scalable and is prone to error.
The "Data Gap" We're Solving
The most critical, pre-failure warning signs—such as new cracks, minor rockfalls, or slow soil creep—are often too subtle for a human to notice until it's too late.
This project was inspired by the need for an automated, cost-effective, and continuous monitoring system that can see and quantify these subtle visual pre-cursors.
Our Solution: The Dual-Analysis Engine
GeoVisual-Detect provides the "best of both worlds" by fusing two types of analysis: high-frequency 2D analysis for rapid events and high-precision 3D analysis for slow, cumulative changes.
Detecting Rapid Change
Every 1-5 minutes, the system analyzes new images. It uses a deep learning model (semantic segmentation) to classify every pixel (e.g., `stable rock`, `loose rock`, `soil`, `snow`).
It then compares this to the baseline to detect rapid, localized changes, such as new cracks, boulder movement, or small rockfalls. This provides an immediate warning for fast-developing events.
Key Project Features
Proactive Monitoring
Moves monitoring from a reactive to a proactive posture, catching pre-cursors to failure.
Cost-Effective & Scalable
Leverages COTS cameras, making the solution far more scalable than in-ground sensors.
High-Fidelity Detection
Detects and *quantifies* change (e.g., "2 cm of creep") not just simple "motion detected" alerts.
Intelligent Filtering
Uses semantic segmentation to understand *what* is changing (rock vs. animal), reducing false positives.
How It Works: System Architecture
The system is designed as an end-to-end pipeline, from data capture on-site to intelligent processing in the cloud, resulting in actionable alerts.
1. Input Module (On-Site)
Fixed, high-resolution cameras (including IR) capture continuous imagery. An on-site edge device (e.g., NVIDIA Jetson) handles pre-processing.
2. Processing Module (Cloud)
The "brain" of the system. Ingests data, aligns images, and runs the 2D segmentation and 3D reconstruction models to detect and fuse changes.
3. Decision & Alert Core
An AI module classifies the severity of the change and issues tiered alerts to stakeholders via a dashboard, API, or SMS.
Tiered Alert System
Level 1 (Watch)
Minor, slow change detected (e.g., 2 cm of soil creep). Notifies monitoring team.
Level 2 (Alert)
Moderate, localized change (e.g., new rockfall cluster). Issues a stakeholder alert.
Level 3 (Critical)
Large-scale, rapid change. An immediate, critical alert is sent to all stakeholders.
Implementation Plan
The project will be rolled out in four distinct phases, moving from initial data collection to full field validation.
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Phase 1: Data Collection
Identify pilot sites and install hardware to collect a diverse baseline dataset across all weather and lighting conditions.
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Phase 2: Model Development
Manually annotate thousands of images to train the semantic segmentation model and benchmark the 3D differencing algorithms.
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Phase 3: System Integration
Build the end-to-end software pipeline, connecting the on-site ingestion, cloud processing, and user dashboard.
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Phase 4: Field Deployment & Validation
Deploy the prototype, monitor its performance, and validate its alerts against ground-truth data (e.g., drone surveys).
Risks & Key Takeaways
Challenges We Might Run Into
What We Learned
Data Pre-processing is Everything
Image registration and illumination normalization are just as important as the deep learning model itself. Garbage in, garbage out.
No Single "Silver Bullet"
Relying on only 2D analysis misses slow creep. Relying on only 3D misses rapid events. The fusion of both data types is essential.
Understand the "Noise"
Characterizing "normal" changes (lighting, animals, vegetation) as part of the baseline is critical for reducing false alarms.
Edge Computing is a Necessity
For real-world remote deployments, processing data at the source is a core requirement for managing data costs and power.