GridMapping
By wangzhecheng
earth-osm
Python tool to extract large-amounts of OpenStreetMap data (by pypsa-meets-earth)
GridMapping | earth-osm | |
---|---|---|
1 | 1 | |
13 | 18 | |
- | - | |
3.8 | 7.9 | |
about 1 year ago | 13 days ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GridMapping
Posts with mentions or reviews of GridMapping.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-18.
-
New to OpenStreetMaps - Power Layer - Educational Purposes - Any tips on style format?
GridMapping: https://github.com/wangzhecheng/GridMappinghttps://www.nature.com/articles/s41467-023-39647-3Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.
earth-osm
Posts with mentions or reviews of earth-osm.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-18.
-
New to OpenStreetMaps - Power Layer - Educational Purposes - Any tips on style format?
earth-osm - https://github.com/pypsa-meets-earth/earth-osmearth-osm is a python package that provides an end-to-end solution to extract & standardize power infrastructure data from OpenStreetmap (OSM).
What are some alternatives?
When comparing GridMapping and earth-osm you can also consider the following projects:
SOFTX-D-22-00275 - Convert OpenStreetMap grid data
GridTool - Convert OpenStreetMap grid data
gridfinder - Algorithm for guessing MV grid location based on NTL
transnet - Transmission System Network Inference
GridKit - GridKit is an power grid extraction toolkit
PyPSA - PyPSA: Python for Power System Analysis