pwernette/MLP_veg_seg
Python programs for filtering/segmenting vegetation from bare-Earth points in point clouds with RGB colour. This repo is supplementary to my AGU presentation in December 2021 and my manuscript published in Remote Sensing in June 2024.
This tool helps geologists, surveyors, and environmental scientists efficiently separate vegetation from bare earth in 3D point cloud data. You input a dense point cloud with RGB color information, and it outputs a reclassified point cloud where each point is labeled as either 'vegetation' or 'bare earth'. This is especially useful for analyzing landscapes with significant relief or dense plant cover.
No commits in the last 6 months.
Use this if you need to accurately and efficiently segment large, dense point clouds to distinguish between vegetation and bare-earth surfaces for geological, environmental, or land management analyses.
Not ideal if your point clouds lack RGB color information or if you need to classify more than two distinct visual categories beyond just vegetation and bare earth.
Stars
8
Forks
1
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Oct 03, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pwernette/MLP_veg_seg"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
satellite-image-deep-learning/techniques
Techniques for deep learning with satellite & aerial imagery
DPIRD-DMA/OmniCloudMask
OmniCloudMask is a Python library for fast, accurate cloud and cloud shadow segmentation in...
developmentseed/label-maker
Data Preparation for Satellite Machine Learning
NRCan/geo-deep-learning
Deep learning applied to georeferenced datasets
satellite-image-deep-learning/software
Software for working with satellite & aerial imagery ML datasets