Geospatial Hazard Intelligence for VFR Aviation in Germany
🚀 Live Demo: https://aerovert-de.vercel.app/
Aerovert-DE is a geospatial intelligence platform designed to analyze and visualize vertical obstacle risk in German airspace using archived NOTAM data from 2025.
The platform focuses on obstacles that directly impact VFR aviation safety, including wind turbines, construction cranes, masts, and obstruction lighting. By transforming unstructured aviation notices into structured geospatial intelligence, Aerovert-DE enables both operational awareness and strategic analysis of airspace saturation.
This project is intentionally built as a production-grade demonstration system, reflecting how such a platform would be engineered in a professional aviation, infrastructure, or geospatial analytics environment.
Germany’s rapid expansion of renewable energy infrastructure has introduced thousands of new vertical structures into low-altitude airspace.
Key challenges include:
- Wind turbines exceeding 200 meters in height
- Temporary construction cranes appearing with high frequency
- NOTAM data published primarily as unstructured free text
- Limited tools for visualizing obstacle density and vertical congestion
- Increased night-flight risk from unlit obstacles (NVFR hazards)
The central question this project addresses is:
Where and how is vertical obstacle saturation creating elevated risk for VFR operations in German airspace?
Aerovert-DE provides three tightly integrated capabilities:
- Operational Hazard Mapping
- Vertical Risk & Saturation Analytics
- Strategic Airspace Intelligence
These are not mockups — the screenshots represent a fully functioning system.
The core interface is an interactive map of Germany displaying thousands of classified obstacles derived from archived NOTAMs.
- High-density geospatial rendering with clustering
- Aviation-optimized dark basemap for low-contrast environments
- Real-time filtering by:
- Obstacle type (wind, crane, mast, lights)
- FIR (EDWW, EDGG, EDMM)
- Vertical limits (Flight Level ranges)
- NVFR-only hazards (unlit obstacles)
Each map marker opens a structured hazard card, showing:
- FIR and NOTAM reference ID
- Obstacle classification
- Maximum vertical extent expressed in Flight Levels (FL)
- Horizontal influence radius (NM)
- Active date range
- Original NOTAM text for traceability
This preserves a verifiable link between raw aviation data and derived intelligence.
Beyond simple visualization, Aerovert-DE models vertical airspace congestion, a critical factor for VFR safety.
The platform enables:
- Identification of VFR conflict bands (e.g., 500–2000 ft)
- Visualization of vertical obstacle concentration (“the wall” effect)
- Differentiation between compliant obstacles and NVFR hazards
- Analysis of height distributions by obstacle type
This vertical perspective transforms flat obstacle data into risk-aware intelligence.
The analytics dashboard provides decision-oriented metrics rather than cosmetic charts.
Key outputs include:
- Total active obstacles
- NVFR hazard count (unlit structures)
- Regional risk comparison by FIR
- Monthly construction and hazard activity trends
- Obstacle composition breakdown
- Ranked list of critical hazards combining:
- Height
- Region
- Obstacle type
- Original NOTAM context
This supports both operational scanning and strategic airspace analysis.
Using the processed 2025 dataset, Aerovert-DE makes the following analyses possible:
- Northern Germany (EDWW) exhibits significantly higher obstacle density than southern regions
- Wind farm construction contributes to persistent vertical congestion in VFR-relevant flight levels
- Unlit obstacles represent a disproportionate night-flight risk
- Construction activity follows clear seasonal patterns
- Some obstacles remain active longer than initially published, creating extended exposure windows
These insights are driven by data engineering and domain logic, not by visualization alone.
Aerovert-DE follows a clean, serverless Next.js architecture hosted entirely on Vercel:
┌─────────────────────────┐
│ Next.js Web Application │
│ (UI, Context, React) │
└───────────┬─────────────┘
│ React Server Components / API Routes
┌───────────▼─────────────┐
│ Next.js API Routes │
│ (/api/obstacles, stats) │
└───────────┬─────────────┘
│ File System Reads
┌───────────▼─────────────┐
│ Static JSON Data Files │
│ (Fast, Cached) │
└─────────────────────────┘
This ensures:
- Zero external dependencies: No dedicated backend or database service required.
- Extremely fast load times: The structured JSON data is bundled directly or served lightning fast from Vercel's edge network.
- Cost-effective: A pure static/serverless deployment topology.
- Next.js 14 (App Router & API Routes)
- TypeScript (strict mode)
- Tailwind CSS
- Leaflet & React-Leaflet
- Recharts
- date-fns
- Lucide React
Designed for high-density geospatial rendering and analytical dashboards.
- Python 3.12+
- Pandas
- openpyxl / xlrd
Responsibilities include ingestion, parsing, classification, risk derivation, and analytics directly to static JSON.
aerovert-de/
├── apps/
│ └── web/
│ ├── src/
│ │ ├── app/ (Pages and API Routes)
│ │ ├── components/
│ │ ├── context/
│ │ └── data/ (obstacles.json, stats.json)
│ ├── public/
│ └── next.config.mjs
├── data/
│ └── raw/2025/ (Archived NOTAM Excel files)
├── scripts/
│ └── convert-excel-to-json.py (Data generation pipeline)
└── README.md
-
Raw Input Monthly archived NOTAM Excel files for 2025, organized by FIR.
-
Parsing & Conversion Run the Python conversion script to extract coordinated, classify hazards, normalize FL, and derive aggregated risk statistics:
python scripts/convert-excel-to-json.py
-
Output Files The script generates
apps/web/src/data/obstacles.json(GeoJSON FeatureCollection) andapps/web/src/data/stats.json. -
Serving Served locally via Next.js API Routes to the React frontend mapping components.
- Node.js 18+
- npm or pnpm
Install dependencies and run the Next.js development server:
cd apps/web
npm install
npm run devVisit http://localhost:3000 to view the application locally.
This platform is a demonstration and research system only.
- Uses archived 2025 NOTAM data
- Does not provide real-time information
- Must not be used for flight planning or navigation
- Always consult official AIS and aviation authorities
This disclaimer is intentionally visible in both documentation and UI.
Aerovert-DE demonstrates:
- Real-world unstructured data engineering
- Serverless geospatial architecture
- Vertical (altitude-based) risk modeling
- Domain-aware UX for aviation safety
- Clean full-stack architecture
- High-performance static data generation strategies
It reflects how a geospatial safety intelligence platform would be built in a professional setting.