This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning

Overview

Deep learning for Earth Observation

http://www.onera.fr/en/dtim https://www-obelix.irisa.fr/

This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning.

We build on the SegNet architecture (Badrinarayanan et al., 2015) to provide a semantic labeling network able to perform dense prediction on remote sensing data. The implementation uses the PyTorch framework.

Motivation

Earth Observation consists in visualizing and understanding our planet thanks to airborne and satellite data. Thanks to the release of large amounts of both satellite (e.g. Sentinel and Landsat) and airborne images, Earth Observation entered into the Big Data era. Many applications could benefit from automatic analysis of those datasets : cartography, urban planning, traffic analysis, biomass estimation and so on. Therefore, lots of progresses have been made to use machine learning to help us have a better understanding of our Earth Observation data.

In this work, we show that deep learning allows a computer to parse and classify objects in an image and can be used for automatical cartography from remote sensing data. Especially, we provide examples of deep fully convolutional networks that can be trained for semantic labeling for airborne pictures of urban areas.

Content

Deep networks

We provide a deep neural network based on the SegNet architecture for semantic labeling of Earth Observation images.

All the pre-trained weights can be found on the OBELIX team website (backup link.

Data

Our example models are trained on the ISPRS Vaihingen dataset and ISPRS Potsdam dataset. We use the IRRG tiles (8bit format) and we build 8bit composite images using the DSM, NDSM and NDVI.

You can either use our script from the OSM folder (based on the Maperitive software) to generate OpenStreetMap rasters from the images, or download the OSM tiles from Potsdam here.

The nDSM for the Vaihingen dataset is available here (courtesy of Markus Gerke, see also his webpage). The nDSM for the Potsdam dataset is available here.

How to start

Just run the SegNet_PyTorch_v2.ipynb notebook using Jupyter!

Requirements

Find the right version for your setup and install PyTorch.

Then, you can use pip or any package manager to install the packages listed in requirements.txt, e.g. by using:

pip install -r requirements.txt

References

If you use this work for your projects, please take the time to cite our ISPRS Journal paper :

https://arxiv.org/abs/1711.08681 Nicolas Audebert, Bertrand Le Saux and Sébastien Lefèvre, Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks, ISPRS Journal of Photogrammetry and Remote Sensing, 2017.

@article{audebert_beyond_2017,
title = "Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2017",
issn = "0924-2716",
doi = "https://doi.org/10.1016/j.isprsjprs.2017.11.011",
author = "Nicolas Audebert and Bertrand Le Saux and Sébastien Lefèvre",
keywords = "Deep learning, Remote sensing, Semantic mapping, Data fusion"
}

License

Code (scripts and Jupyter notebooks) are released under the GPLv3 license for non-commercial and research purposes only. For commercial purposes, please contact the authors.

https://creativecommons.org/licenses/by-nc-sa/3.0/ The network weights are released under Creative-Commons BY-NC-SA. For commercial purposes, please contact the authors.

See LICENSE.md for more details.

Acknowledgements

This work has been conducted at ONERA (DTIM) and IRISA (OBELIX team), with the support of the joint Total-ONERA research project NAOMI.

The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).

Say Thanks!

Owner
Nicolas Audebert
Assistant professor in Computer Science. Resarcher on computer vision and deep learning.
Nicolas Audebert
This project deploys a yolo fastest model in the form of tflite on raspberry 3b+. The model is from another repository of mine called -Trash-Classification-Car

Deploy-yolo-fastest-tflite-on-raspberry 觉得有用的话可以顺手点个star嗷 这个项目将垃圾分类小车中的tflite模型移植到了树莓派3b+上面。 该项目主要是为了记录在树莓派部署yolo fastest tflite的流程 (之后有时间会尝试用C++部署来提升

7 Aug 16, 2022
Meta graph convolutional neural network-assisted resilient swarm communications

Resilient UAV Swarm Communications with Graph Convolutional Neural Network This repository contains the source codes of Resilient UAV Swarm Communicat

62 Dec 06, 2022
Pipeline code for Sequential-GAM(Genome Architecture Mapping).

Sequential-GAM Pipeline code for Sequential-GAM(Genome Architecture Mapping). mapping whole_preprocess.sh include the whole processing of mapping. usa

3 Nov 03, 2022
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

Chloe 10 Nov 14, 2022
This is the code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields This is the code for "HyperNeRF: A Higher-Dimensional

Google 702 Jan 02, 2023
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 01, 2022
The Face Mask recognition system uses AI technology to detect the person with or without a mask.

Face Mask Detection Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect

Rohan Kasabe 4 Apr 05, 2022
This is an open source library implementing hyperbox-based machine learning algorithms

hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed

Complex Adaptive Systems (CAS) Lab - University of Technology Sydney 21 Dec 14, 2022
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL labels about 43 hours of mocap sequences from AMASS [1] with action labels.

113 Dec 28, 2022
Code for a real-time distributed cooperative slam(RDC-SLAM) system for ROS compatible platforms.

RDC-SLAM This repository contains code for a real-time distributed cooperative slam(RDC-SLAM) system for ROS compatible platforms. The system takes in

40 Nov 19, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

11 May 19, 2022
Differentiable scientific computing library

xitorch: differentiable scientific computing library xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely

98 Dec 26, 2022
Loopy belief propagation for factor graphs on discrete variables, in JAX!

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

Vicarious 62 Dec 23, 2022
Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project

Space Invaders This is a simple SPACE INVADER game create using PYGAME whihc hav

Gaurav Pandey 2 Jan 08, 2022
Code release of paper "Deep Multi-View Stereo gone wild"

Deep MVS gone wild Pytorch implementation of "Deep MVS gone wild" (Paper | website) This repository provides the code to reproduce the experiments of

François Darmon 53 Dec 24, 2022
Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow

AutoAugment - Learning Augmentation Policies from Data Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by Au

Philip Popien 1.3k Jan 02, 2023
A fast python implementation of Ray Tracing in One Weekend using python and Taichi

ray-tracing-one-weekend-taichi A fast python implementation of Ray Tracing in One Weekend using python and Taichi. Taichi is a simple "Domain specific

157 Dec 26, 2022
SVG Icon processing tool for C++

BAWR This is a tool to automate the icons generation from sets of svg files into fonts and atlases. The main purpose of this tool is to add it to the

Frank David Martínez M 66 Dec 14, 2022
🇰🇷 Text to Image in Korean

KoDALLE Utilizing pretrained language model’s token embedding layer and position embedding layer as DALLE’s text encoder. Background Training DALLE mo

HappyFace 74 Sep 22, 2022