### Access Global Energy Infrastructure Data Catalogs

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

A list of primary global platforms for tracking transmission lines, power plants, and substations, providing links to datasets and source code repositories.

```bash
# Open Infrastructure Map - Real-time visualization of power infrastructure from OpenStreetMap
# URL: https://openinframap.org/
# Source code: https://github.com/openinframap/openinframap

# GridFinder - Predictive mapping of global power systems
# URL: https://gridfinder.rdrn.me/
# Dataset: https://zenodo.org/records/3628142
# Source code: https://github.com/carderne/gridfinder

# Global Energy Monitor - Comprehensive energy infrastructure tracking
# URL: https://globalenergymonitor.org/
# License: CC-BY-4.0

# ENERGYDATA.INFO - World Bank open data platform for energy
# URL: https://energydata.info/

# Global Transmission Database
# URL: https://github.com/Electricity-Transmission-Database/electricity-transmission-database
# License: MIT
```

--------------------------------

### Machine Learning for Grid Detection Tools

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

This section lists various machine learning projects and datasets for automatically detecting power infrastructure from satellite imagery. These tools aid in mapping power grids and substations.

```bash
# GridTracer - Deep learning for power grid mapping
# Paper: https://arxiv.org/abs/2101.06390
# Repository: https://github.com/wangzhecheng/GridMapping
# Training Dataset: https://figshare.com/articles/dataset/Electric_Transmission_and_Distribution_Infrastructure_Imagery_Dataset/6931088

# ml-hv-grid - World Bank high voltage grid mapping
# Documentation: https://developmentseed.org/ml-grid-docs/
# Repository: https://github.com/developmentseed/ml-hv-grid-pub

# Substation Detection
# Repository: https://github.com/Lindsay-Lab/substation-seg
# Dataset: https://huggingface.co/datasets/neurograce/SubstationDataset

# TTPLA - Transmission Tower and Power Line Dataset
# Paper: https://arxiv.org/abs/2010.10032
# Dataset: https://github.com/r3ab/ttpla_dataset

# Poles-on-Earth - AI utility pole detection
# Repository: https://github.com/TA-Geoforce/Poles-on-Earth
```

--------------------------------

### Reference OpenStreetMap Power Tagging Conventions

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

Provides a reference for standard OpenStreetMap tags used to identify power infrastructure components such as lines, substations, and towers.

```bash
# Common power tags:
# power=line          - High voltage transmission line
# power=minor_line    - Distribution line
# power=substation    - Electrical substation
# power=generator     - Power generation facility
# power=tower         - Transmission tower
# power=pole          - Distribution pole
```

--------------------------------

### Extract Power Grid Data using Python

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

Demonstrates the use of Python libraries like earth-osm and OSMnx to programmatically extract and analyze power infrastructure from OpenStreetMap.

```python
from earth_osm import eo

# Get power infrastructure for Kenya
power_data = eo.get_power(
    region="KE",  # ISO country code
    features=["line", "substation", "generator"]
)

import osmnx as ox

# Download power infrastructure for a city
G = ox.graph_from_place("Nairobi, Kenya", custom_filter='["power"]')
```

--------------------------------

### Resource Format Reference in Markdown

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

This markdown section details the tagging conventions and format used for resource entries in the collection. It explains how to interpret country, title, URL, year, license, and tags for each resource.

```markdown
# Resource Entry Format:
# (Country) [Title](URL) (Year) (License) (Tags)

# Tags indicate resource type:
# (map)          - Interactive or static map visualization
# (dataset)      - Downloadable data files (shapefiles, GeoJSON, CSV)
# (report)       - PDF reports and planning documents
# (capacitydata) - Includes electrical capacity information

# Common Licenses:
# CC-BY-4.0      - Creative Commons Attribution
# CC-BY-SA 4.0   - Creative Commons Attribution ShareAlike
# CC0            - Public Domain
# ODbL           - Open Database License
# MIT            - MIT Open Source License
# proprietary    - Restricted use, check source for terms

# Example entries:
# (Kenya) [Energydata Kenya Electricity Network](https://energydata.info/dataset/kenya-kenya-electricity-network) (2021) (cc0) (dataset)
# (Germany) [ENTSO-E Grid Map](https://eepublicdownloads.entsoe.eu/clean-documents/Publications/maps/2024/ENTSOE_Grid_Map_Continental_Europe.pdf) (map)
```

--------------------------------

### Generate Country Coverage Map with Python

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

A Python script utilizing geopandas and matplotlib to generate a world map visualizing countries with available public grid data. It requires geopandas, matplotlib, and PIL libraries.

```python
# scripts/createmap.py - Generate world map showing grid data availability
# Requires: geopandas, matplotlib, PIL

import geopandas as gpd
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from PIL import Image

# Define countries with available grid data
provided_countries = {
    "Nigeria", "Kenya", "South Africa", "India", "Germany",
    "United States", "Brazil", "Australia", # ... and many more
}

# Load world boundaries
world = gpd.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")

# Color countries based on data availability
brand_colors = ["#1abc9c", "#2c3e50"]
world["Status"] = world["NAME"].apply(
    lambda x: "Present" if x in provided_countries else "Missing"
)

# Generate map
fig, ax = plt.subplots(figsize=(19, 11))
world.plot(ax=ax, color=world["Status"].map({"Present": "#1abc9c", "Missing": "#2c3e50"}))
plt.savefig("countries_map.png", dpi=300, bbox_inches="tight", transparent=True)
```

--------------------------------

### Access Regional Transmission Grid Datasets

Source: https://context7.com/open-energy-transition/awesome-electrical-grid-mapping/llms.txt

A collection of regional and country-specific links to transmission grid data including shapefiles, GeoJSON, and interactive maps.

```bash
# Africa - Kenya Electricity Network (CC0)
# URL: https://energydata.info/dataset/kenya-kenya-electricity-network

# Asia - India Energy Dashboard with transmission lines, substations, power plants
# URL: https://iced.niti.gov.in/

# Europe - ENTSO-E Transmission System Map (interactive)
# URL: https://www.entsoe.eu/data/map/

# North America - US Infrastructure Foundation-Level Data
# URL: https://resilience.climate.gov/datasets/d4090758322c4d32a4cd002ffaa0aa12_0/explore
# Substations Zenodo backup (CC-BY-4.0): https://zenodo.org/records/5514010

# South America - Brazil ONS Open Data - Substations and Transmission Lines (CC-BY-4.0)
# Substations: https://dados.ons.org.br/dataset/subestacao
# Transmission Lines: https://dados.ons.org.br/dataset/linha-transmissao

# Oceania - Australia Digital Atlas Transmission Lines (CC-BY-4.0)
# URL: https://digital.atlas.gov.au/datasets/digitalatlas::electricity-transmission-lines/explore
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.