EBPS - Electronic Building Permit System

AI-Powered Building Bylaws Verification for Kathmandu Metropolitan City

Automating civil engineering compliance verification for Nepal's capital city

Overview

EBPS is an intelligent document processing system that automates building permit verification for Kathmandu Metropolitan City (KMC), Nepal. It replaces hours of manual calculation with instant, accurate compliance checking against the Kathmandu Building Standards 2080 and historical bylaws (2050, 2060, 2072).

The Problem

Civil engineers at KMC manually verify hundreds of building permit applications against complex zoning regulations. Each verification requires:

Reading multiple PDF documents in Nepali and English
Cross-referencing 5 different sub-zone categories
Calculating Floor Area Ratio (FAR), ground coverage, parking requirements
Applying Right of Way (ROW) deductions based on road widening plans
Checking setback distances and height restrictions
Applying grandfather clauses for pre-existing buildings

This process takes 30-60 minutes per application and is prone to human error.

The Solution

EBPS reduces verification time to under 30 seconds with:

Intelligent PDF extraction using PyMuPDF with Google Gemini AI fallback for complex Nepali text
Smart file detection that analyzes PDF content to select architectural drawings over structural ones
Multi-year bylaws support for historical compliance (grandfather clause implementation)
Automated calculations with full formula transparency for auditing
Professional reports with floor-by-floor breakdowns

Key Features

Feature	Description
One-Command Verification	Single command extracts data, verifies compliance, generates report
Bilingual PDF Processing	Handles Nepali (देवनागरी) and English documents with AI-powered OCR
Smart File Detection	Content-based scoring algorithm selects correct DPC file from multiple candidates
Multi-Year Bylaws	Supports 2050, 2060, 2072, and 2080 building standards with automatic year detection
Complete Compliance Suite	FAR, Ground Coverage, Parking, Setbacks, Height verification
ROW Calculation	Automatic Right of Way deductions based on KMC road widening plans
Data Validation	Flags suspicious extraction results (zero values, impossible ratios)
Detailed Reporting	Generates audit-ready markdown reports with calculation transparency

Technical Architecture

┌─────────────────────────────────────────────────────────────────────┐
│ EBPS Architecture │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ PDF Input │───►│ Extractor │───►│ Structured Data │ │
│ │ (Nepali/EN) │ │ (PyMuPDF + │ │ (Pydantic Models) │ │
│ │ │ │ Gemini AI) │ │ │ │
│ └──────────────┘ └──────────────┘ └──────────┬───────────┘ │
│ │ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────▼───────────┐ │
│ │ Report │◄───│ Verifier │◄───│ Standards Lookup │ │
│ │ Generator │ │ Engine │ │ (5 Sub-zones × │ │
│ │ (Markdown) │ │ │ │ 4 Bylaw Years) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘

Core Components

Component	Technology	Purpose
PDF Extraction	PyMuPDF, Google Gemini Pro	Text extraction with AI fallback for scanned documents
Data Models	Pydantic v2	Type-safe building data validation
Smart Detection	Custom scoring algorithm	Selects architectural vs structural drawings
Calculations	Pure Python	FAR, coverage, ROW, parking formulas
Standards Engine	JSON configuration	20 zone/year combinations
Report Generation	Markdown templating	Professional audit-ready output

Quick Start

Installation

# Clone repository
git clone https://github.com/ajjucoder/ebps.git
cd ebps
# Create virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate# Install dependencies
pip install -r requirements.txt
# Configure API key (for AI extraction)echo"GEMINI_API_KEY=your_key_here"> .env

Basic Usage

# Verify a building permit
./venv/bin/python verify_compliance.py "clients/client_name"# With vision OCR for scanned documents
./venv/bin/python verify_compliance.py "clients/client_name" --vision
# For older buildings (apply historical bylaws)
./venv/bin/python verify_compliance.py "clients/client_name" --construction-year 2065

Example Output

============================================================
COMPLIANCE VERIFICATION REPORT
============================================================
Client: Niroj Dangol
Ward: 15, Kitta: 164
Sub-Zone: Other Residential Sub-Zone
Land Area: 200.81 sqm (6.31 Aana)
APPLICABLE STANDARDS (Kathmandu Building Standards 2080):
Maximum FAR: 3.5
Maximum Coverage: 70% (plot < 8 Aana)
Maximum Height: 5 stories / 17m
Required Setback: 1.524m
VERIFICATION RESULTS:
✅ FAR: 0.62 / 3.5 max (PASS)
✅ Ground Coverage: 59.87% / 70.0% max (PASS)
✅ Parking: 588 sft / 432 sft required (PASS)
✅ Height: 4.5 / 5 stories max (PASS)
✅ Setback (West): 10.414m / 1.524m min (PASS)
OVERALL: ✅ COMPLIANT
============================================================

Compliance Checks Explained

Floor Area Ratio (FAR)

FAR = Total FAR Countable Area / Land Area
Example:
Total FAR Countable: 1,338.52 sft
Land Area: 2,161.70 sft
FAR = 1,338.52 / 2,161.70 = 0.62
Allowed FAR: 3.5 (Other Residential)
Result: 0.62 ≤ 3.5 → PASS

Ground Coverage

Coverage % = (Ground Floor Area / Land Area) × 100
Example:
Ground Floor: 1,294.46 sft
Land Area: 2,161.70 sft
Coverage = (1,294.46 / 2,161.70) × 100 = 59.87%
Allowed: 70% (plot < 8 Aana)
Result: 59.87% ≤ 70% → PASS

Right of Way (ROW) Deduction

Government road widening affects parking calculations:

Current Road Width	Future Road Width	Deduction per Side
< 8 ft	13 ft	6.5 ft
8-12 ft	20 ft	10 ft
12-20 ft	26 ft	13 ft
≥ 20 ft	No change	0 ft

Net Parking = Gross Parking Area - (ROW Deduction × Frontage)
Required Parking = Land Area × 20% (for plots ≥ 4 Aana)

Sub-Zone Standards (2080)

Sub-Zone	FAR Limit	Coverage (< 8 Aana)	Coverage (≥ 8 Aana)	Max Height	Setback
Other Residential	3.5	70%	60%	5 stories / 17m	1.524m
Planned Residential	3.5	70%	60%	7 stories / 22.86m	2.0m
Dense Mixed	4.0	70%	60%	6 stories / 20m	1.524m
Main Residential	3.0	60%	50%	4 stories / 14m	1.524m
Commercial	4.5	70%	50%	7 stories / 22.86m	2.0m

Smart File Detection Algorithm

When a client folder contains multiple DPC files, EBPS uses content analysis to select the correct architectural drawing:

Positive Indicators (Architectural):

Floor plan keywords (+15 points)
Area tables with sq.ft/sqm (+15 points)
"FAR countable" mentions (+20 points)
Ground floor, first floor labels (+12 points)

Negative Indicators (Structural):

Trench plan (-25 points)
Tie beam details (-20 points)
Foundation drawings (-15 points)
Structural detail (-30 points)

This solves the critical bug where structural drawings (with zero floor areas) were incorrectly selected over architectural drawings.

Historical Bylaws Support

EBPS implements the grandfather clause for buildings constructed under previous regulations:

Construction Year (BS)	Applicable Bylaws	Folder
2080 onwards	Standards 2080	`BYLAWS_2080/`
2072-2079	Bylaws 2072	`BYLAWS_2072/`
2060-2071	Bylaws 2060	`BYLAWS_2060/`
Before 2060	Bylaws 2050	`BYLAWS_2050/`

# Apply 2065 construction year (uses 2060 bylaws)
./venv/bin/python verify_compliance.py "clients/old_building" --construction-year 2065

Project Structure

ebps/
├── verify_compliance.py # Main CLI entry point
├── smart_extract.py # PDF extraction utility
├── src/
│ ├── verifier.py # Compliance verification engine
│ ├── calculations.py # FAR, coverage, parking formulas
│ ├── standards.py # Sub-zone standards (20 combinations)
│ ├── report_generator.py # Markdown report generation
│ ├── models.py # Pydantic data models
│ ├── extractor.py # Smart PDF extraction + file detection
│ ├── pdf_reader.py # PyMuPDF text extraction
│ └── gemini_parser.py # Gemini AI parsing fallback
├── Building Bylaws/ # Regulatory documents (4 versions)
│ ├── BYLAWS_2080/ # Current standards
│ ├── BYLAWS_2072/ # 2072-2079 buildings
│ ├── BYLAWS_2060/ # 2060-2071 buildings
│ └── BYLAWS_2050/ # Pre-2060 buildings
├── clients/ # Client data (gitignored)
├── docs/ # Technical documentation
├── CLAUDE.md # AI assistant instructions
└── CHANGELOG.md # Version history

CLI Reference

# Basic verification
./venv/bin/python verify_compliance.py "clients/CLIENT" [OPTIONS]
Options:
--bylaws-year YEAR Specify bylaws version (2050, 2060, 2072, 2080)
--construction-year YEAR Auto-select bylaws from construction year (BS)
--force-extract Re-extract PDFs even if cached data exists
--vision Use vision OCR for scanned/blurry documents
--json Output results as JSON
--dpc-file PATH Override DPC file auto-detection
--compliance-file PATH Override compliance file auto-detection
--verbose Show detailed extraction logs

Real-World Impact

This system was developed for practical use at Kathmandu Metropolitan City, Nepal's largest municipality:

Time Savings: Reduces verification from 30-60 minutes to under 30 seconds
Accuracy: Eliminates calculation errors in FAR and coverage percentages
Consistency: Applies regulations uniformly across all applications
Transparency: Full calculation breakdown for audit trails
Historical Compliance: Properly handles grandfather clause for older buildings

Technologies Used

Category	Technologies
Language	Python 3.11+
AI/ML	Google Gemini Pro (multimodal LLM)
PDF Processing	PyMuPDF (fitz), pdf2image
Data Validation	Pydantic v2
Configuration	python-dotenv
Testing	Manual verification with real KMC documents

Skills Demonstrated

Software Engineering: Clean architecture, modular design, CLI development
AI Integration: LLM-powered document extraction with fallback strategies
Domain Expertise: Civil engineering regulations, urban planning concepts
Data Processing: PDF parsing, bilingual text extraction (Nepali/English)
Algorithm Design: Content-based file scoring, multi-criteria validation
Government Tech: Real-world municipal compliance automation

Privacy & Security

Client documents remain local in clients/ (gitignored)
API keys stored in .env (gitignored)
No client data is transmitted except for AI extraction via Gemini API
All processing can be done offline with --vision flag (local OCR)

Future Roadmap

Web interface for non-technical users
Batch processing for multiple clients
Integration with KMC document management system
Mobile app for field verification
Support for additional municipalities

Contributing

Contributions are welcome. Please read the contribution guidelines before submitting pull requests.

License

MIT License - See LICENSE for details.

Acknowledgments

Kathmandu Metropolitan City for the building bylaws documentation
Google AI for the Gemini Pro API

EBPS v5.1.0 | Built for real-world government use in Nepal

Transforming how Kathmandu Metropolitan City verifies building permits

Name		Name	Last commit message	Last commit date
Building Bylaws		Building Bylaws
Kathmandu Building Standards 2080		Kathmandu Building Standards 2080
docs		docs
src		src
.env.example		.env.example
.gitignore		.gitignore
AI_WORKFLOW.md		AI_WORKFLOW.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
IMPLEMENTATION_PLAN.md		IMPLEMENTATION_PLAN.md
PROMPT_TEMPLATE.md		PROMPT_TEMPLATE.md
RALPH_LOOP_PROMPT.md		RALPH_LOOP_PROMPT.md
README.md		README.md
WARP.md		WARP.md
extract_pdf.py		extract_pdf.py
requirements.txt		requirements.txt
smart_extract.py		smart_extract.py
verify_compliance.py		verify_compliance.py

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EBPS - Electronic Building Permit System

Overview

The Problem

The Solution

Key Features

Technical Architecture

Core Components

Quick Start

Installation

Basic Usage

Example Output

Compliance Checks Explained

Floor Area Ratio (FAR)

Ground Coverage

Right of Way (ROW) Deduction

Sub-Zone Standards (2080)

Smart File Detection Algorithm

Historical Bylaws Support

Project Structure

CLI Reference

Real-World Impact

Technologies Used

Skills Demonstrated

Privacy & Security

Future Roadmap

Contributing

License

Acknowledgments

About

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Repository files navigation

EBPS - Electronic Building Permit System

Overview

The Problem

The Solution

Key Features

Technical Architecture

Core Components

Quick Start

Installation

Basic Usage

Example Output

Compliance Checks Explained

Floor Area Ratio (FAR)

Ground Coverage

Right of Way (ROW) Deduction

Sub-Zone Standards (2080)

Smart File Detection Algorithm

Historical Bylaws Support

Project Structure

CLI Reference

Real-World Impact

Technologies Used

Skills Demonstrated

Privacy & Security

Future Roadmap

Contributing

License

Acknowledgments

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