nico's repositories
awesome-data-labeling
A curated list of awesome data labeling tools
CloudComPy
Python wrapper for CloudCompare
Colorize-SwissSURFACE3D-Lidar
Instructions to colorize SwissSURFACE3D Lidar using SwissIMAGE10 orthoimages, and split the point cloud for later deep learning training.
cvat
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
DeprivedAreasMapping
Mapping of deprived areas from satellite imagery using case studies of data from sub-Saharan Africa
f4g-oceania-pdal
A workshop on PDAL for FOSS4G SotM Oceania 2018
gdal-docker
GDAL Dockerfile (with ECW format and Python support)
GIS-Processing
Processing GIS polygons
HyperspectralAnalysisIntroduction
Brief Introduction to Hyperspectral Image Analysis - Jupyter Notebook
labelme
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Learn-C-Programming-Second-Edition
Learn C Programming, Second Edition, published by Packt
myria3d
Semantic segmentation deep learning for aerial, high density Lidar point clouds.
pandas_exercises
Practice your pandas skills!
PointCNN
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
Pointnet2.PyTorch
A PyTorch Implementation of Pointnet++.
Pointnet_Pointnet2_pytorch
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
PyTorch-Stuff
Scripts using pytorch
QGIS-STUFF
Scripts for processing in QGIS using Python
railroad
Robust Railroad Infrastructure Detection Framework
RandLA-Net
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
RF4PCC
RF4PCC: Random Forest 4 Point Cloud Classification, 3DOM FBK
segment-geospatial
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
semantic-segmentation-editor
Web labeling tool for bitmap images and point clouds
training-resources
Training resources for geospatial computing
ZRect3D
Command-Line Interface (CLI) application to process and correct buildings height in CityJSON files using ground points from LiDAR data.