This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
Commercial navigation platforms (Google Maps, Waze, Apple Maps) answer only one question: "What is the fastest route for an ordinary passenger car?"
In an emergency, this assumption is dangerous and often fatal. In dense urban quarters, historic wards, and rapidly growing urban fringes across India and the developing world:
- A 16-tonne Type 1 Fire Tender (requiring $\ge 3.5\text{m}$ clearance) gets physically wedged in 2.2m-wide gullies or snagged by low-hanging electrical wire bundles.
- An ALS Ambulance (requiring $\ge 3.0\text{m}$) meets unmapped bollards, narrow gate curfews, or construction trenches.
- Up to 70% of municipal GIS and OpenStreetMap records lack road width tags (
width=null), leading conventional mapping apps to assume roads are passable simply because a centerline vector exists.
UNREACHABLE is a deterministic emergency accessibility intelligence platform and side-hustle micro-SaaS built for municipal corporations, emergency dispatch centers (112/101/108), urban planning consultancies, and private industrial complexes.
The Side Hustle Product & Who Pays For It:
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Municipal Urban Councils & Smart City Missions ($2,500 – $10,000 / ward audit):
- Uses the platform's Single Point of Failure (SPOF) Sensitivity Engine to rank choke points and identify which single road bottlenecks cut off the most homes.
- Generates automated Field Audit Work Orders (
.csvexport & print-ready clipboard checklists) prioritizing which unmeasured roads surveyors must measure first.
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Emergency Dispatch Operations & Fire Brigades ($499 / station / month):
- Evaluates apparatus clearance across 3 emergency vehicle profiles before dispatch.
- Live route recalculation with dynamic in-memory incident obstruction overlays and Server-Sent Events (SSE) telemetry.
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Simulation Bench for Capital Works:
- Urban engineers toggle hypothetical street interventions (e.g. wire bundle clearing, bollard removal) to preview the exact number of buildings "rescued" before committing municipal budget.
Demo
- Video Walkthrough Demo: Watch the Video Demo on Google Drive
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Local / Single-Command Interactive Console: Can be run locally via
scripts/start_demo.shor in dual mode (demosynthetic campus vsreal497-facility Punjab regional dataset).
Code
Garg-Pankaj29
/
UNREACHABLE
AI-powered emergency accessibility mapping that identifies unreachable buildings, detects road bottlenecks, and helps emergency services plan feasible routes and prioritize safety improvements.
UNREACHABLE
An AI-powered, deterministic emergency accessibility intelligence platform designed for dense urban environments and Indian cities. Evaluates physical road geometry, bottleneck pinch-points, overhead utility wire bundles, and apparatus clearances before an emergency unfolds.
Concept
In dense urban environments—from historic core wards in Indian cities to rapidly densifying metropolitan settlements—emergency response times are frequently defeated not by highway traffic, but by the "last 500 meters":
- Narrow residential gullies under 2.5 meters wide where heavy fire water tenders become physically wedged.
- Low-hanging overhead electrical and telecommunication wire bundles snagging apparatus ladders.
- Unmapped bollards, gate curfews, street market encroachments, and construction trenches.
- Municipal GIS datasets that mark a building as "reachable" simply because a centerline road vector exists in OpenStreetMap or cadastre records.
UNREACHABLE changes how cities prepare for emergencies:
"Know the access. Find the constraint. Fix it before an emergency."
UNREACHABLE runs on local loopback (127.0.0.1). It ingests road corridor…
- Repository: https://github.com/Garg-Pankaj29/UNREACHABLE
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Stack:
- Backend: Python 3.10+, FastAPI, Pydantic v2, SQLite spatial indexing, Server-Sent Events (SSE).
- Frontend: React 18, TypeScript 5, Vite, Tailwind CSS 3.4, Leaflet 1.9 vector mapping, Lucide Icons.
- AI / LLM: Local inference via LM Studio running open-weight Gemma 3 4B (OpenAI-compatible local endpoint).
How I Built It
1. The Core Architectural Philosophy: Deterministic Physics Decides, AI Explains
In life-safety systems, probabilistic AI models must never make access or routing decisions. An LLM hallucinating that a 3.8-meter-wide fire truck can squeeze through a 2.1-meter alley can cost lives.
UNREACHABLE enforces an unshakeable boundary:
- Routing & Feasibility are 100% Deterministic Python: Pure graph theory via a custom Modified Penalty Dijkstra algorithm, Great-Circle Haversine calculations, and vehicle clearance metrics evaluate whether an apparatus fits.
- Open-Source AI (Gemma 3 4B): Translates complex mathematical routes, bottleneck IDs, clearance margins, and penalty scores into concise, plain-language tactical briefings for dispatchers and incident commanders.
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Automated Numeric & Fact Guardrail Verifier: Before any AI-generated explanation reaches the user, it is intercepted and audited by
ExplainService._validate_llm_response(). It cross-checks all extracted numbers against mathematical ground truth. If the model hallucinates a number or alters road IDs, the response is instantly rejected and safely served by a pre-validated deterministic template.
2. Dual Dataset Architecture
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Synthetic Demo Mode (
data/sample/): High-fidelity model of Chandigarh University (CU) Gharuan campus & surrounding rural-urban fringe (NH-05, Punjab) for instant testing. -
Real Pilot Mode (
data/real/): Full OpenStreetMap ingestion covering all 23 districts of Punjab & Chandigarh, indexing 497 emergency facilities (fire hubs, trauma centers, dispensaries) and over 41,300 road centerlines with 453,812 graph edges.
3. Dynamic In-Memory Obstructions & Real-Time SSE
- When a road is blocked during an incident, an in-memory obstruction overlay updates the active graph without touching disk files.
- Real-time Server-Sent Events (
/api/stream) broadcast unit coordinates and dynamic detours to connected dispatchers with sub-second latency.
Why Does Open Innovation Matter?
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Air-Gapped Disaster Resilience: During catastrophic monsoons, floods, or network blackouts, cloud APIs like proprietary closed models become unreachable. Open-source AI (Gemma 3 4B running locally via LM Studio or Ollama) enables the entire system to run 100% locally on an air-gapped laptop on
127.0.0.1with zero external cloud dependencies. - Sovereignty over Sensitive Spatial Infrastructure: Municipalities, military bases, and fire brigades cannot leak security footprints, gate curfews, or road vulnerability data to closed third-party cloud servers. Open innovation keeps sensitive data local and private.
- No Per-Token API Costs for Municipalities: Budget-constrained civic bodies cannot afford unpredictably billed proprietary tokens for 24/7 emergency dispatch engines. Open-weight models democratize emergency intelligence for developing cities.
My Agent Session
Developed and tested with local agentic pair-programming workflows, ensuring strict separation between deterministic routing physics and open-source generative briefings.
My First User
During development and field validation around the Kharar and Gharuan urban fringes (Punjab), we piloted the spatial engine against real-world choke points:
- User Profile: A local logistics and private security coordinator managing transport access around high-density housing sectors and university feeder corridors.
- Feedback: "Standard GPS constantly routed our supply trucks down internal village link roads where low electrical wiring and open drains forced drivers to reverse hundreds of meters. UNREACHABLE's clearance classification instantly exposed the two narrow pinch points responsible for 90% of our access blocks."
- Outcome: Validated the commercial value of the Intervention Directory and the Field Audit Work Order export, proving civic administrators and private enclaves are willing to pay for bottleneck remediation reports.
Prize Categories
- Open-Source AI Challenge Week 2: Side Hustle
- Sponsor Tracks / Open Innovation Track (Deployable, Local-first Open-Weight AI with Strict Guardrails)
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