ERGIN C CANKAYA's Projects
Implementations of popular CNNs in 3D for developing forest inventories from LiDAR
An R package for biomass estimation at extratropical forest plots.
π Awesome R packages that offer extended UI or server components for the R web framework Shiny
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
A repository of custom scripts to be used with Sentinel Hub
LIDAR and RGB Deep Learning Model for Individual Tree Segmentation
Introduction to convolutional neural networks (CNNs) with a remote sensing example.
Code (mostly R) to work with drone lidar data for forestry applications
R interface to fast.ai
official source code for paper entitled "Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning"
Remote Sensing Tools for Forest Monitoring
Automatic Processing of TLS Point Cloud Data for Forestry Purposes
This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".
An R package for NASAβs GEDI Level 4A data download, processing and visualization.
Processing pipeline for downloading, analyzing, and gridding GEDI data
Open source book: Geocomputation with R
Material for the course on machine-learning based environmental monitoring at geostat 2018
Rapid and Pretty Things in R : A shiny graphical user interface for your favourite ggplot graphics in R
Global Sampling Grid simulation framework
Implementation of the LiDAR tree segmentation algorithm, Layer Stacking.
The lidR package tutorials book :book:
Documentos do Livro Explorando o QGIS 3.x
Modeling animal movement in forest
R package for evaluating individual tree crown predictions against a diverse benchmark dataset
Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information.
Material for the session "Introduction to Deep Learning in R for the analysis of UAV-based remote sensing data"
ORMAN GENEL MUDURLUGU