langnico

belongielab.github.io

Lab Website

DL_tutorial

Introduction to CNNs

DL_tutorial_RS

Introduction to convolutional neural networks (CNNs) with a remote sensing example.

fine-grained-osr

Project webpage for "From Coarse to Fine-Grained Open-Set Recognition"

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".

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.

globalcanopyheight

GRAINet

MLEG_tutorial

Tutorial: Machine Learning for Environmental and Geosciences (MLEG)

osr-coarse-to-fine

This repository contains the code used to create the results presented in the paper: "From Coarse to Fine-Grained Open-Set Recognition". We investigate the role of label granularity, semantic similarity, and hierarchical representations in open-set recognition (OSR) with an OSR-benchmark based on iNat2021.

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.

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".

langnico/DL_tutorial_RS

Introduction to convolutional neural networks (CNNs) with a remote sensing example.

langnico/GRAINet

langnico/MLEG_tutorial

Tutorial: Machine Learning for Environmental and Geosciences (MLEG)

langnico/osr-coarse-to-fine

This repository contains the code used to create the results presented in the paper: "From Coarse to Fine-Grained Open-Set Recognition". We investigate the role of label granularity, semantic similarity, and hierarchical representations in open-set recognition (OSR) with an OSR-benchmark based on iNat2021.

langnico/DL_tutorial

Introduction to CNNs

langnico/globalcanopyheight

langnico/belongielab.github.io

Lab Website

langnico/fine-grained-osr

Project webpage for "From Coarse to Fine-Grained Open-Set Recognition"

langnico/langnico.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

langnico/MultiMAE

MultiMAE: Multi-modal Multi-task Masked Autoencoders, ECCV 2022

langnico/MMEarth-data

This repository contains code to download data for the paper "MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning"

langnico/MMEarth-train

This repository contains code to reproduce the experiments in the paper "MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning"