angus-spence/loc2vec
Learning semantic embeddings from OSM data: A Pytorch implementation of the loc2vec general method outlined in: https://sentiance.com/loc2vec-learning-location-embeddings-w-triplet-loss-networks.
This tool helps urban planners, real estate analysts, or logistics experts understand the unique characteristics and similarities between different geographic areas. By taking raster data representing OpenStreetMap features like roads, parks, and commercial zones, it generates a unique 'fingerprint' for each location. These 'fingerprints' can then be used to compare locations, find similar areas, or predict behavior based on geographical context.
No commits in the last 6 months.
Use this if you need to quantitatively understand the 'personality' of a location based on its built environment and natural features, especially for tasks like urban planning, site selection, or regional comparison.
Not ideal if your primary goal is simple mapping visualization or if you don't have existing geographic feature data in a raster format.
Stars
11
Forks
1
Language
Python
License
MIT
Category
Last pushed
Oct 07, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/angus-spence/loc2vec"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
shibing624/similarities
Similarities: a toolkit for similarity calculation and semantic search....
explosion/sense2vec
🦆 Contextually-keyed word vectors
chakki-works/chakin
Simple downloader for pre-trained word vectors
sebischair/Lbl2Vec
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with...
pdrm83/sent2vec
How to encode sentences in a high-dimensional vector space, a.k.a., sentence embedding.