A simple algorithm for extracting tree height in sparse scene from point cloud data.

Overview

TREE HEIGHT EXTRACTION IN SPARSE SCENES BASED ON UAV REMOTE SENSING

This is the offical python implementation of the paper "Tree Height Extraction in Sparse Scenes Based on UAV Remote Sensing," by Y. Liu, M. Xing, X. Zhou, Y. Song and D. Wang. For more information, checkout the IGARSS2020 oral paper.

Description

A simple algorithm for extracting tree height in sparse scene from point cloud data.

Requirements

  • python3
  • pip install numpy scipy matplotlib plotly
  • python-pcl

If you have python-pcl installation problems, you can try the ".whl" file in the floder "python-pcl/"

Tested Environment

I have tested the code in the following environment:

  • Windows10-64bit
  • Anaconda python3.7

Make sure anaconda has been installed on your windows computer, use the following command:

conda create -n py37 python=3.7
conda activate py37
pip install python_pcl-0.3.0rc1-cp37-cp37m-win_amd64.whl
conda install -c plotly plotly-orca==1.2.1 psutil requests
pip install numpy scipy matplotlib

ImportError: DLL load failed

If you meet "ImportError: DLL load failed" problem when you try to "import pcl", that was because "OpenNI2.dll" missing.

To fix this, I provide you with "python-pcl/OpenNI2.dll", just copy "OpenNI2.dll" into folder "YOUR_ANACONDA3_FLODER\envs\py37\lib\site-packages\pcl".

Run

python main.py

Citation

If you use this code or ideas from the paper for your research, please cite our paper:

@inproceedings{liu2020tree,
  title={Tree Height Extraction in Sparse Scenes Based on UAV Remote Sensing},
  author={Liu, Yuanzhong and Xing, Minfeng and Zhou, Xiaozhe and Song, Yang and Wang, Danyang},
  booktitle={IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium},
  pages={6499--6502},
  year={2020},
  organization={IEEE}
}
Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation The reference code of Improving Factual Completeness and C

46 Dec 15, 2022
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).

What is judgyprophet? judgyprophet is a Bayesian forecasting algorithm based on Prophet, that enables forecasting while using information known by the

AstraZeneca 56 Oct 26, 2022
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)

ICON: Implicit Clothed humans Obtained from Normals Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black CVPR 2022 News 🚩 [2022/04/26] H

Yuliang Xiu 1.1k Jan 04, 2023
D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of

Albert Pumarola 291 Jan 02, 2023
Swapping face using Face Mesh with TensorFlow Lite

Swapping face using Face Mesh with TensorFlow Lite

iwatake 17 Apr 26, 2022
An intuitive library to extract features from time series

Time Series Feature Extraction Library Intuitive time series feature extraction This repository hosts the TSFEL - Time Series Feature Extraction Libra

Associação Fraunhofer Portugal Research 589 Jan 04, 2023
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

SPLADE 🍴 + 🥄 = 🔎 This repository contains the weights for four models as well as the code for running inference for our two papers: [v1]: SPLADE: S

NAVER 170 Dec 28, 2022
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

TIA Toolbox Computational Pathology Toolbox developed at the TIA Centre Getting Started All Users This package is for those interested in digital path

Tissue Image Analytics (TIA) Centre 156 Jan 08, 2023
Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Introduction This is an official release of the paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity" (arxiv link). Abstrac

Leo 21 Nov 23, 2022
Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

Nest Protect integration for Home Assistant Custom component for Home Assistant to interact with Nest Protect devices via an undocumented and unoffici

Mick Vleeshouwer 175 Dec 29, 2022
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
Deep Learning for humans

Keras: Deep Learning for Python Under Construction In the near future, this repository will be used once again for developing the Keras codebase. For

Keras 57k Jan 09, 2023
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

Deep learning based state estimation: incorporating Transformer and LSTM to Kalman Filter with EM algorithm Overview Kalman Filter requires the true p

zshicode 57 Dec 27, 2022
git《Investigating Loss Functions for Extreme Super-Resolution》(CVPR 2020) GitHub:

Investigating Loss Functions for Extreme Super-Resolution NTIRE 2020 Perceptual Extreme Super-Resolution Submission. Our method ranked first and secon

Sejong Yang 0 Oct 17, 2022
Unsupervised phone and word segmentation using dynamic programming on self-supervised VQ features.

Unsupervised Phone and Word Segmentation using Vector-Quantized Neural Networks Overview Unsupervised phone and word segmentation on speech data is pe

Herman Kamper 13 Dec 11, 2022
Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch

CutPaste CutPaste: image from paper Unofficial implementation of Google's "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization"

Lilit Yolyan 59 Nov 27, 2022