BHU-DRISTI (भू-दृष्टि):

1. EXECUTIVE OVERVIEW 

BHU-DRISTI is an AI-powered early warning and disaster response system designed to mitigate landslide risks in vulnerable mountainous regions. The platform combines machine learning predictions from wireless sensor network (WSN) telemetry, real-time crowdsourced hazard reporting, emergency SOS dispatch, and offline-first progressive web application (PWA) architecture to ensure uninterrupted field utility during severe network disruptions.

2. Core Problem & Objectives

• Lack of Real-Time Ground Warnings: Conventional landslide monitoring relies heavily on satellite updates that can face latency during storm conditions.

• Network Vulnerability in Disaster Zones: Mountainous and landslide-prone regions often suffer severe communication cutoffs, rendering standard cloud-only emergency portals unreachable.

• Delayed Authority Response: Citizens and first responders lack direct channels to report localized hazards with precise geolocations, photographic proof, and automated alerts to disaster management teams.

BHU-DRISTI resolves these issues by delivering continuous sensor risk evaluation, localized browser caching, and automated authority escalation.

3. Key Features & Capabilities

• AI Landslide Risk Prediction:

  • Uses an XGBoost Classifier trained on environmental telemetry (slope angle, soil moisture, rainfall intensity, vibration, etc.).

  • Calculates real-time hazard probabilities categorized into clear severity bands (Low, Medium, High) to trigger preventive actions.

• Offline-First PWA Resilience:

  • Employs Service Workers (sw.js) and IndexedDB / Web Storage (offline-sync.js) to enable full offline functionality.

  • Field responders and residents can capture reports without internet; data queues locally and automatically pushes to the central backend once connectivity is restored.

• Crowdsourced Hazard Reporting:

  • Allows users to submit active ground hazards with GPS coordinates, severity descriptions, and photographic attachments stored on the server.

• Emergency SOS Distress Trigger:

  • A dedicated single-click distress mechanism (sos.html) that extracts device geolocation and dispatches immediate high-priority alerts.

• Automated Authority Notification Engine:

  • An automated SMTP email dispatch service (authority_notification.py) that instantly alerts district disaster management authorities when high-risk hazards or SOS events are logged.

• Interactive Authority Dashboard:

  • A command-and-control portal (authority_dashboard.html) mapping active incidents, historical landslide zones (historical_landslides.geojson), and live emergency statuses for rescue prioritization.

4. Technical Architecture

• Frontend: HTML5, CSS3, JavaScript, Leaflet.js, and PWA Service Workers.

• Backend: Python Flask (app.py, platform_features.py) providing REST APIs and file upload orchestration.

• Machine Learning: XGBoost, Scikit-learn, Pandas, and NumPy (landslide_xgboost.json, train_model.py).

• Database & Storage: SQLite (bhu_dristi.db), GeoJSON vector files, and local file storage for media evidence.

• Deployment: Dockerized containerization (Dockerfile) for cross-platform portability and rapid setup.

5. Impact & Value Proposition

• Zero Downtime in Remote Terrain: Guarantees critical reporting survives network dropouts via client-side offline sync.

• Proactive Rather Than Reactive: Moves beyond post-disaster recovery to preemptive warning through sensor-driven machine learning.

• Direct Citizen-to-Authority Bridge: Connects stranded citizens directly to disaster relief response units with actionable location intelligence.