tiagodc

tiagodc's Stars

• OSGeo/gdal

  GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.

  Language:C++4.9k2.5k

• iterative/PyDrive2

  Google Drive API Python wrapper library. Maintained fork of PyDrive.

  Language:Python58369

• facebookresearch/convit

  Code for the Convolutional Vision Transformer (ConViT)

  Language:Python46253

• Nyandwi/ModernConvNets

  Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras

  Language:Jupyter Notebook34240

• langnico/global-canopy-height-model

  This repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.

  Language:Python13729

• simard-landscape-lab/kapok

  Kapok: Python Library for PolInSAR Forest Height Estimation Using UAVSAR Data

  Language:Python3913

• facebookresearch/dinov2

  PyTorch code and models for the DINOv2 self-supervised learning method.

  Language:Jupyter Notebook9.1k814

• nasa/GEDI-Data-Resources

  This repository provides guides, short how-tos, and tutorials to help users access and work with data from the Global Ecosystem Dynamics Investigation (GEDI) mission.

  Language:Jupyter Notebook5624

• r-lidar/lasR

  Fast and Pipable Airborne LiDAR Data Tools

  Language:C++621

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook22.8k3.3k

• nasa/LPDAAC-Data-Resources

  This repository is a place to find data user resources that demonstrate how to use LP DAAC tools, services, and data.

  Language:Jupyter Notebook2810

• microsoft/torchgeo

  TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

  Language:Python2.7k339

• CASIA-IVA-Lab/FastSAM

  Fast Segment Anything

  Language:Python7.5k707

• csiro-robotics/raycloudtools

  Language:C++7016

• henrikbostrom/crepes

  Python package for conformal prediction

  Language:Python44935

• langnico/GEDI-BDL

  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".

  Language:Python6127

• google-research/tuning_playbook

  A playbook for systematically maximizing the performance of deep learning models.

  27.1k2.2k

• LidarSu/Canopy_entropy

  Language:Python123

• google/earthengine-community

  Tutorials and content created by Earth Engine users, for Earth Engine users

  Language:Jupyter Notebook583746

• philwilkes/TLS2trees

  Language:Python6120

• r-barnes/richdem

  High-performance Terrain and Hydrology Analysis

  Language:C++26868

• julianoscabral/MoF3D

  Modelling Forests in 3 Dimensions

  1

• armstonj/librat_in_jupyter

  Language:Jupyter Notebook1

• raphaelquast/EOmaps

  A library to create interactive maps of geographical datasets

  Language:Python33925

• 3dgeo-heidelberg/helios

  Language:Jupyter Notebook19244

• HenrikBengtsson/future

  :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone

  Language:R95684

• DiskFrame/disk.frame

  Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data

  Language:R59540

• BlasBenito/spatialRF

  R package to fit spatial models with Random Forest

  Language:R10916

• Antguz/rTLS

  rTLS: Tools to Process Point Clouds Derived from Terrestrial Laser Scanning

  Language:R246

• makepath/xarray-spatial

  Raster-based Spatial Analytics for Python

  Language:Python84185