RJ2019

3d-model-new-york-city

FME Workspaces created in the cours of generating a semantic digital 3D city model for the entire city of New York are available for download on this GitHub page.

amundsen

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

amundsendatabuilder

Data ingestion library for Amundsen to build graph and search index

amundsenfrontendlibrary

Front-end service library for Amundsen

amundsensearchlibrary

Search service library for Amundsen

Awesome-Geospatial

Long list of geospatial tools and resources

Building-Segmentation-Reference-Dataset

This is the data set used as the reference for the segmentation of LiDAR building point clouds

PointCloudSegmentation

The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection

segmenters_lib

The LiDAR segmenters library, for segmentation-based detection.

zero_to_deep_learning_udemy

Repository for the Zero to Deep Learning™ Video Course on Udemy

RJ2019/DeepLiDAR

Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image

RJ2019/neo4j-etl

Data import from relational databases to Neo4j.

RJ2019/neo4j-graphql

GraphQL bindings for Neo4j, generates and runs Cypher

RJ2019/LAStools

efficient tools for LiDAR processing

RJ2019/vision3d

Research platform for 3D object detection in PyTorch.

RJ2019/pseudo_lidar

(CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

RJ2019/whitebox-python

WhiteboxTools Python Frontend

RJ2019/BuildingFootprints

RJ2019/QT-LiDAR-Object-Detection

QT (Quad-Tree Segmentation)

RJ2019/spatial

Neo4j Spatial is a library of utilities for Neo4j that faciliates the enabling of spatial operations on data. In particular you can add spatial indexes to already located data, and perform spatial operations on the data like searching for data within specified regions or within a specified distance of a point of interest. In addition classes are provided to expose the data to geotools and thereby to geotools enabled applications like geoserver and uDig.

RJ2019/superpoint_graph

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

RJ2019/Lidar_Quadtree_Segmentation

RJ2019/spatial-algorithms

Spatial algorithms for both cartesian and geographic data

RJ2019/SqueezeSegV2

Implementation of SqueezeSegV2, Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

RJ2019/neo4j-guides

Tooling to create Neo4j Browser Guides from AsciiDoc Content

RJ2019/fmepedia-pointcloud

Demo of point cloud data distribution using FME Server

RJ2019/python-geospatial

A collection of Python packages for geospatial analysis with binder-ready notebook examples

RJ2019/LiDAR

LiDAR data processing

RJ2019/building_footprint_extraction

RJ2019/whitebox-geospatial-analysis-tools

An open-source GIS and remote sensing package

RJ2019/segmenters_lib

The LiDAR segmenters library, for segmentation-based detection.

RJ2019/ForestMetrics

Individual tree segmentation from LiDAR-derived point clouds

RJ2019/neo4j-tableau

Neo4j Tableau Integration via WDC

RJ2019/pytorch-LiLaNet

Implementation of "Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation"

RJ2019/lidar_ground_segmentation

Segmenting ground plane from LIDAR data

RJ2019/neo4j-tinkerpop-api-impl

Implementation of Apache Licensed Neo4j API for Tinkerpop3

RJ2019/plane_fit_ground_filter

3D lidar recognition and segmentation of ground

RJ2019/Lidar-object-clustering-AND-ground-remove

激光雷达障碍物检测和聚类，参考论文： Efficient Online Segmentation for Sparse 3D Laser Scans ; 其中包含地面分割代码 ransac;

RJ2019/neo4j-elasticsearch

Neo4j ElasticSearch Integration

RJ2019/SqueezeSeg

Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation