Datasets:

SafeVixAI
/

SafeVixAI-Dataset-Hub

	---
	license: apache-2.0
	task_categories:
	- object-detection
	- question-answering
	language:
	- en
	- hi
	tags:
	- road-safety
	- india
	- emergency-services
	- traffic-law
	- geospatial
	- rag
	pretty_name: SafeVixAI Dataset Hub
	size_categories:
	- 1B<n<10B
	---

	# SafeVixAI Dataset Hub 🛡️

	> The Intelligence Layer for the SafeVixAI platform — IIT Madras Road Safety Hackathon 2026

	This repository hosts all datasets, pre-trained models, notebooks, and reproducible data acquisition scripts that power the SafeVixAI application. It is designed to be cloned directly into Google Colab or any research environment.

	Main Application Repo: [SafeVixAI/SafeVixAI](https://github.com/SafeVixAI/SafeVixAI)

	---

	## ⚡ Quickstart (Google Colab)

	```python
	# Clone the entire intelligence layer
	!git clone https://huggingface.co/datasets/SafeVixAI/SafeVixAI-Dataset-Hub /content/data

	# Symlink into the app structure
	import os
	os.makedirs("/content/SafeVixAI/chatbot_service", exist_ok=True)
	!ln -sfn /content/data/data/chatbot_service/data /content/SafeVixAI/chatbot_service/data
	```

	---

	## 📦 Repository Structure

	```
	SafeVixAI-Dataset-Hub/
	├── data/ ← 4.2 GB of raw intelligence data (11,008 files)
	│ ├── backend/
	│ │ ├── data/ ← Civic intel (OSM data, toll plazas, ward boundaries)
	│ │ └── datasets/ ← Raw database assets (blackspots, violations CSVs)
	│ ├── chatbot_service/data/ ← Chatbot reference directories & built vector stores
	│ └── frontend/offline-data/ ← Regional translation matrices & offline PWA bundles
	│
	└── scripts/ ← Complete data acquisition & processing pipelines
	├── backend/ ← Core backend and database migration scripts
	├── chatbot_service/ ← Chatbot agent and QA validation scripts
	└── scripts/ ← Legacy scrapers, downloaders, and seeders
	```

	---

	## 🗂️ Data Contents

	\| Category \| Files \| Size \| Source \|
	\|---\|---\|---\|---\|
	\| Emergency Services GIS \| `hospitals.csv`, `police_stations.csv`, `fire_stations.csv`, `ambulance.csv` \| ~150 MB \| OpenStreetMap Overpass API \|
	\| Legal Documents \| `motor_vehicles_act_1988.pdf`, `mv_amendment_act_2019.pdf` \| ~30 MB \| MoRTH / Indian Kanoon \|
	\| Accident Statistics \| `kaggle_india_accidents.csv`, `morth_2022/*.csv` \| ~250 MB \| MoRTH / Kaggle \|
	\| Road Infrastructure \| `pmgsy_roads.geojson`, `toll_plazas.csv` \| ~900 MB \| PMGSY GeoSadak / NHAI \|
	\| Hospital Directory \| `hospital_directory.csv`, `nin_facilities.csv` \| ~1.2 GB \| NHP / NIN \|
	\| Traffic Violations \| `violations_seed.csv`, `state_overrides.csv` \| ~5 MB \| MVA 2019 \|
	\| PWA Translations \| `translations/*.json` (11 regional Indian languages) \| ~6.4 MB \| Automated DeepL/Google Sync \|
	\| Road Damage Model \| `road_damage_2025/` \| ~800 MB \| ONNX + Training Data \|
	\| QA Pairs \| `qa_pairs/` \| ~50 MB \| Custom RAG Training \|

	---

	## 🔬 Scripts — Reproducible Data Pipeline

	The `scripts/` folder mirrors the main application's data acquisition pipeline.
	All scripts here are pure Python — they run without any database or backend stack.

	### Structure

	```
	scripts/
	├── scripts/data/ ← from SafeVixAI/scripts/data/
	│ ├── _overpass_utils.py ← Core GIS utility (basic version)
	│ ├── fetch_hospitals.py ← Hospital data from OpenStreetMap
	│ ├── fetch_police.py ← Police station data
	│ ├── fetch_ambulance.py ← Ambulance service data
	│ ├── fetch_blood_banks.py ← Blood bank data
	│ ├── fetch_fire.py ← Fire station data
	│ ├── download_legal_pdfs.py ← MV Act PDF downloader
	│ ├── extract_morth2022_tables.py ← PDF → CSV extractor
	│ ├── seed_blackspots.py ← Accident blackspot normalizer
	│ ├── bootstrap_local_data.py ← Master data orchestrator
	│ ├── verify_data.py ← Data integrity checker
	│ ├── inspect_zips.py ← Dataset zip validator
	│ ├── audit_env.py ← Environment configuration checker
	│ ├── check_all_scripts.py ← Script syntax validator
	│ └── setup_kaggle.ps1 ← Kaggle API auth setup
	│
	├── backend/data/ ← from SafeVixAI/backend/scripts/data/
	│ ├── seed_violations.py ← Traffic fine normalizer (MVA 2019)
	│ ├── prepare_road_sources.py ← CSV/GeoJSON → LineString converter
	│ ├── sample_pmgsy.py ← PMGSY 867K roads sampler
	│ ├── road_sources.example.json ← Manifest template
	│ └── road_sources.json ← Active road source manifest
	│
	└── chatbot_service/data/ ← from SafeVixAI/chatbot_service/scripts/data/
	├── _overpass_utils.py ← Pro GIS utility (with retries + backoff)
	├── fetch_hospitals.py ← Pro hospital fetcher
	├── fetch_police.py ← Pro police fetcher
	├── fetch_ambulance.py ← Pro ambulance fetcher
	├── fetch_blood_banks.py ← Pro blood bank fetcher
	└── fetch_fire.py ← Pro fire station fetcher
	```

	### Running the Scripts

	```bash
	# 1. Setup Kaggle auth (one-time)
	pwsh scripts/scripts/data/setup_kaggle.ps1

	# 2. Fetch all emergency service GIS data (Pro versions recommended)
	python scripts/chatbot_service/data/fetch_hospitals.py
	python scripts/chatbot_service/data/fetch_police.py
	python scripts/chatbot_service/data/fetch_ambulance.py
	python scripts/chatbot_service/data/fetch_blood_banks.py
	python scripts/chatbot_service/data/fetch_fire.py

	# 3. Download legal documents
	python scripts/scripts/data/download_legal_pdfs.py

	# 4. Extract MoRTH accident tables
	python scripts/scripts/data/extract_morth2022_tables.py

	# 5. Verify everything
	python scripts/scripts/data/verify_data.py
	```

	> Note: `chatbot_service/data/` versions of the fetchers are the Pro versions — they include retry logic, exponential backoff, and more precise Indian GIS selectors.

	---

	## 📓 Research Notebooks — Open in Colab

	> We advise running all notebooks through Google Colab for the easiest setup. Colab gives you a free T4 GPU with 16 GB of VRAM. All notebooks were built and tested on Colab, so it is the most stable platform. Any other cloud provider should work too.

	\| # \| Notebook \| What It Produces \| Open in Colab \|
	\|---\|---\|---\|---\|
	\| 1 \| YOLOv8 Pothole Detector Training \| ONNX road damage model \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1oe4Gk899lFB_vbRMUuOh4dqpsa3bbycI) \|
	\| 2 \| ChromaDB RAG Vectorstore Build \| ChromaDB index for legal RAG \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1AzPdN9xjcjW20ko0shTYn0mvbxTUw57Q) \|
	\| 3 \| Accident EDA & Hotspot Generator \| Blackspot seed CSV + heatmap \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xh_lwv_B_jc0_83dvuNppWQRtVhqExTS) \|
	\| 4 \| Roads Data Processing \| Sampled PMGSY GeoJSON \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10_WfTlbbxW9A7ceQBZGaKF5UkydlUDin#scrollTo=z4XxGZmx0ymX) \|
	\| 5 \| Risk Model ONNX Training \| Risk scoring ONNX model \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16IH-rn3CedYtfIpJP4iLa_KUjvfy8hAY) \|

	Recommended run order: `1 → 2 → 3 → 4 → 5`

	> Each notebook auto-clones this Hub at the start — no manual data setup needed.

	---

	## 📜 License

	Apache 2.0 — See [LICENSE](LICENSE)

	Data sourced from OpenStreetMap (ODbL), MoRTH (Government of India Open Data), Kaggle, PMGSY GeoSadak, and National Health Portal.