BTCMap to OpenStreetMap Tag Converter

A Python tool to quickly convert BTCMap data into OpenStreetMap tag format for easy tagging.

Features

• Parses BTCMap data format (Id, Origin, Name, Category, Extra fields)
• Automatically extracts and formats address components (city, housenumber, postcode, state, street)
• Adds required Bitcoin/Lightning payment tags with today's date
• Maps BTCMap categories to OSM shop/amenity tags
• Preserves opening hours and other metadata

Requirements

• Python 3.6 or higher
• No external dependencies (uses only standard library)

Usage

Interactive Mode (Recommended)

Run the program and paste your BTCMap data:

python3 btcmap_to_osm.py

Important Terminal Tips:

• To paste: Use Ctrl+Shift+V (or right-click and select paste)
• To copy output: Select the text and use Ctrl+Shift+C (or right-click and select copy)
• To finish input: Press Ctrl+D (Linux/Mac) or Ctrl+Z then Enter (Windows)

The program will wait for you to paste your data, then display the OSM format output.

File Mode

If you have your BTCMap data saved in a file:

python3 btcmap_to_osm.py input_file.txt

Input Format

Paste your BTCMap data in this format:

Id: 3901
Origin: square
Name: Dulcédo Coffee
Category: food_truck_cart

Extra fields:

{
"address": "26750 FM 1093 STE 120 Richmond TX 77406 US",
"icon_url": "https://square-web-production-f.squarecdn.com/files/107fa80f57d2db9874dda1a19683578392ed87b8/original.jpeg",
"description": "Specialty coffee shop",
"opening_hours": "Mo-Sa 07:00-19:00",
"last_updated": "2025-11-25T17:21:07.561595515Z"
}

Output Format

The program generates OpenStreetMap tags in this format:

addr:city=Richmond
addr:housenumber=26750
addr:postcode=77406
addr:state=TX
addr:street=FM 1093
addr:unit=120
check_date:currency:XBT=2025-11-25
check_date=2025-11-25
currency:XBT=yes
payment:onchain=no
payment:lightning=yes
payment:lightning_contactless=no
payment:lightning:operator=square
name=Dulcédo Coffee
cuisine=coffee
opening_hours=Mo-Sa 07:00-19:00
amenity=fast_food
street_vendor=yes

** unfortunally this example IS NOT a food truck even though the category shows it as such. it is a cafe. this is to show that not all inputs will give correct info but it will give you a quicker start. Be mindful.

Automatic Fields

The program automatically adds these fields with today's date:

• currency:XBT=yes
• check_date:currency:XBT=<today's date>
• check_date=<today's date>
• payment:onchain=no
• payment:lightning=yes
• payment:lightning_contactless=no
• payment:lightning:operator=<from Origin field>

Category Mapping

BTCMap categories are automatically mapped to OSM tags:

• professional_services → amenity=office
• restaurants → amenity=restaurant
• cafe → amenity=cafe
• confectionery → shop=confectionery
• grocery → shop=supermarket
• And more...

Tips

• The program handles various address formats, but you may need to manually verify street names (e.g., "Trl" vs "Trail")
• All dates are automatically set to today's date - no need to update manually
• The output is ready to copy directly into OpenStreetMap editors
• If you encounter parsing issues, check that your input format matches the expected structure

Troubleshooting

• Can't paste in terminal? Make sure you're using Ctrl+Shift+V (not just Ctrl+V)
• Can't copy output? Select the text and use Ctrl+Shift+C or right-click
• Address parsing looks wrong? The program does its best, but you may need to manually adjust street names or address components
• Category not mapping correctly? You can edit the map_category_to_osm() function in the script to add more mappings