fatiando/verde
Processing and gridding spatial data, machine-learning style
This tool helps you transform scattered measurements of spatial data, like topography, ocean depth, or geophysical readings, into a smooth, regularly spaced grid or map. You input raw point data with coordinates and associated values, and it outputs an interpolated grid ready for visualization or further analysis. Geoscientists, surveyors, and environmental modelers who work with spatial measurements would find this useful.
651 stars. Actively maintained with 15 commits in the last 30 days. Available on PyPI.
Use this if you need to create continuous surfaces or maps from irregularly spaced point observations of natural phenomena, for either Cartesian or geographic coordinates.
Not ideal if your primary goal is general-purpose machine learning or if you don't work with spatial or geophysical data.
Stars
651
Forks
73
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 02, 2026
Commits (30d)
15
Dependencies
7
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