Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

Overview

Created by Olga Sutyrina, Sarah Elemili, Abduragim Shtanchaev and Artur Bille

Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

We analyzed sparse and convolutational autoencoders (SAE, CAE) with appended "classical" classifiers (AdaBoost, CatBoost, XGB, SVC, RFC, Log. Regr.), as well two convolutional neural networks (CNN) with different structures as end-to-end classifiers. Details and explicit architectures of the considered models can found in the report.

Dependencies

Requirement:

  • Python 3 with:
    • Numpy
    • Matplotlib
    • rasterio
    • fiona
    • xgboost
    • torch
    • sklearn

To extract the pixels around a given tree, use our extractor-module.

Our code has been tested with Python 3 on GoogleColab.

Installation

To run our codes you have to install the following modules: fiona, rasterio and catboost.

Data set

Our data set consists of three parts. The first one contains the whole satellite picture with 4561 x 6559 pixels and 8 channels. The second one includes 1012 labeled trees positions. Possible labels are: 0 = 'Spruce', 1 = 'Birch', 2 = 'Fir', 3 = 'Pine'. In the third part one can find positions of 10492 unlabeled tree crown positions within the satellite picture. The provided extractor module includes a function which extracts the pixels in the surrounding of every tree crown.

To download the data set:

cd ML-project-Group9-WeakLearners/

./download_data.sh

Usage

In this project we set up four experiments. To find all needed implemenations and results of each of them, follow this structure:

  1. Autoencoders (AE) + 'classical' classifiers:
  • Convolutional autoencoder (CAE) -> here

  • Sparse autoencoder (SAE) -> here

  1. Convolutional neural network (CNN):
[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

Ayan Kumar Bhunia 44 Dec 12, 2022
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation This is a demo implementation of BYOL for Audio (BYOL-A), a self-sup

NTT Communication Science Laboratories 160 Jan 04, 2023
RL agent to play μRTS with Stable-Baselines3

Gym-μRTS with Stable-Baselines3/PyTorch This repo contains an attempt to reproduce Gridnet PPO with invalid action masking algorithm to play μRTS usin

Oleksii Kachaiev 24 Nov 11, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
Spectral Tensor Train Parameterization of Deep Learning Layers

Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr

Anton Obukhov 12 Oct 23, 2022
Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Introduction This repository contains research code for the ACL 2021 paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual

AdapterHub 20 Aug 04, 2022
[TIP2020] Adaptive Graph Representation Learning for Video Person Re-identification

Introduction This is the PyTorch implementation for Adaptive Graph Representation Learning for Video Person Re-identification. Get started git clone h

WuYiming 41 Dec 12, 2022
Coursera - Quiz & Assignment of Coursera

Coursera Assignments This repository is aimed to help Coursera learners who have difficulties in their learning process. The quiz and programming home

浅梦 828 Jan 04, 2023
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Rishik Mourya 48 Dec 20, 2022
Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.

Knowledge Graph Embeddings and Chemical Effect Prediction, 2020. Scripts and outputs related to the paper Prediction of Adverse Biological Effects of

Knowledge Graphs at the Norwegian Institute for Water Research 1 Nov 01, 2021
Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

mqtt-camera-streamer Summary: Publish frames from a connected camera or MJPEG/RTSP stream to an MQTT topic, and view the feed in a browser on another

Robin Cole 183 Dec 16, 2022
This repository contains PyTorch models for SpecTr (Spectral Transformer).

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation This repository contains PyTorch models for SpecTr (Spectral Transformer).

Boxiang Yun 45 Dec 13, 2022
Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

546 Final Project: Masked Autoencoder Haoran Tang, Qirui Wu 1. Training To train the network, please run mae_pretraining.py. Please modify folder path

Haoran Tang 0 Apr 22, 2022
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

Katsuya Hyodo 16 Dec 22, 2022
Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation"

Designing an Encoder for StyleGAN Image Manipulation (SIGGRAPH 2021) Recently, there has been a surge of diverse methods for performing image editing

749 Jan 09, 2023
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
Yoga - Yoga asana classifier for python

Yoga Asana Classifier Description Hi welcome to my new deep learning project "Yo

Programminghut 35 Dec 12, 2022
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
Tree Nested PyTorch Tensor Lib

DI-treetensor treetensor is a generalized tree-based tensor structure mainly developed by OpenDILab Contributors. Almost all the operation can be supp

OpenDILab 167 Dec 29, 2022
Prototype-based Incremental Few-Shot Semantic Segmentation

Prototype-based Incremental Few-Shot Semantic Segmentation Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo -- BMVC 20

Fabio Cermelli 21 Dec 29, 2022