A public available dataset for road boundary detection in aerial images

Overview

Topo-boundary

This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving.

Project page.

Topo-boundary is a publicly available benchmark dataset for topological road-boundary detection in aerial images. With an aerial image as the input, the evaluated method should predict the topological structure of road boundaries in the form of a graph.

This dataset is based on NYC Planimetric Database. Topo-boundary consists of 25,297 4-channel aerial images, and each aerial image has eight labels for different deep-learning tasks. More details about the dataset structure can be found in our paper. Follow the steps in the ./dataset to prepare the dataset.

We also provide the implementation code (including training and inference) based on PyTorch of 9 methods. Go to the Implementation section for details.

Update

  • May/22/2021 Topo_boundary is released. More time is needed to prepare ConvBoundary, DAGMapper and Enhanced-iCurb, thus currently these models are not open-sourced.

Platform information

Hardware info

GPU: one RTX3090 and one GTX1080Ti
CPU: i7-8700K
RAM: 32G
SSD: 256G + 1T

Software info

Ubuntu 18.04
CUDA 11.2
Docker 20.10.1

Make sure you have Docker installed.

File structure

Topo-Boundary
|
├── dataset
|   ├── data_split.json
|   ├── config_dir.yml
|   ├── get_data.bash
|   ├── get_checkpoints.bash
│   ├── cropped_tiff
│   ├── labels
|   ├── pretrain_checkpoints
│   └── scripts
|   
├── docker 
|
├── graph_based_baselines
|   ├── ConvBoundary
|   ├── DAGMApper
|   ├── Enhanced-iCurb
|   ├── iCurb
|   ├── RoadTracer
|   └── VecRoad 
|
├── segmentation_based_baselines
|   ├── DeepRoadMapper
|   ├── OrientationRefine
|   └── naive_baseline
|

Environment and Docker

Docker is used to set up the environment. If you are not familiar with Docker, refer to install Docker and Docker beginner tutorial for more information.

To build the docker image, run:

# go to the directory
cd ./docker
# optional
chmod +x ./build_image.sh
# build the docker image
./build_image.sh

Data and pretrain checkpoints preparation

Follow the steps in ./dataset to prepare the dataset and checkpoints trained by us.

Implementations

We provide the implementation code of 9 methods, including 3 segmentation-based baseline models, 5 graph-based baseline models, and an improved method based on our previous work iCurb. All methods are implemented with PyTorch by ourselves.

Note that the evaluation results of baselines may change after some modifications being made.

Evaluation metrics

We evaluate our implementations by 3 relaxed-pixel-level metrics, the self-defined Entropy Connectivity Metric (ECM), naive connectivity metric (proposed in ConvBoundary) and Average Path Length Similarity (APLS). For more details, refer to the supplementary document.

Related topics

Other research topics about line-shaped object detection could be inspiring to our task. Line-shaped object indicts target objects that have long but thin shapes, and the topology correctness of them also matters a lot. They usually have an irregular shape. E.g., road-network detection, road-lane detection, road-curb detection, line-segment detection, etc. The method to detect one line-shaped object could be adapted to another category without much modification.

To do

  • Acceleration
  • Fix bugs

Contact

For any questions, please send email to zxubg at connect dot ust dot hk.

Citation

@article{xu2021topo,
  title={Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving},
  author={Xu, Zhenhua and Sun, Yuxiang and Liu, Ming},
  journal={arXiv preprint arXiv:2103.17119},
  year={2021}
}

@article{xu2021icurb,
  title={iCurb: Imitation Learning-Based Detection of Road Curbs Using Aerial Images for Autonomous Driving},
  author={Xu, Zhenhua and Sun, Yuxiang and Liu, Ming},
  journal={IEEE Robotics and Automation Letters},
  volume={6},
  number={2},
  pages={1097--1104},
  year={2021},
  publisher={IEEE}
}
Owner
Zhenhua Xu
HKUST Ph.D. Candidate
Zhenhua Xu
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Yu Meng 60 Dec 30, 2022
This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer This repo is the official implementation for TransBTS: Multimodal Brain Tumor Segmenta

Raymond 247 Dec 28, 2022
Toolkit for collecting and applying prompts

PromptSource Promptsource is a toolkit for collecting and applying prompts to NLP datasets. Promptsource uses a simple templating language to programa

BigScience Workshop 998 Jan 03, 2023
DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:

Kuang-Jui Hsu 139 Dec 22, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

28 Dec 02, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Jan 02, 2023
Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning

Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning Kajetan Schweighofer1, Markus Hofmarcher1, Marius-Constantin D

Institute for Machine Learning, Johannes Kepler University Linz 17 Dec 28, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
Measuring Coding Challenge Competence With APPS

Measuring Coding Challenge Competence With APPS This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks*, Steven B

Dan Hendrycks 218 Dec 27, 2022
Barbershop: GAN-based Image Compositing using Segmentation Masks (SIGGRAPH Asia 2021)

Barbershop: GAN-based Image Compositing using Segmentation Masks Barbershop: GAN-based Image Compositing using Segmentation Masks Peihao Zhu, Rameen A

Peihao Zhu 928 Dec 30, 2022
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

DLR-RM 4.7k Jan 01, 2023
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT ***************New March 28, 2020 *************** Add a colab tutorial to run fine-tuning for GLUE datasets. ***************New January 7, 2020

Google Research 3k Jan 01, 2023
Object detection GUI based on PaddleDetection

PP-Tracking GUI界面测试版 本项目是基于飞桨开源的实时跟踪系统PP-Tracking开发的可视化界面 在PaddlePaddle中加入pyqt进行GUI页面研发,可使得整个训练过程可视化,并通过GUI界面进行调参,模型预测,视频输出等,通过多种类型的识别,简化整体预测流程。 GUI界面

杨毓栋 68 Jan 02, 2023
Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Dongkyu Lee 4 Sep 18, 2022
Code for "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds", CVPR 2021

PV-RAFT This repository contains the PyTorch implementation for paper "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clou

Yi Wei 43 Dec 05, 2022
Wikidated : An Evolving Knowledge Graph Dataset of Wikidata’s Revision History

Wikidated Wikidated 1.0 is a dataset of Wikidata’s full revision history, which encodes changes between Wikidata revisions as sets of deletions and ad

Lukas Schmelzeisen 11 Aug 16, 2022
Deep metric learning methods implemented in Chainer

Deep Metric Learning Implementation of several methods for deep metric learning in Chainer v4.2.0. Proxy-NCA: No Fuss Distance Metric Learning using P

ronekko 156 Nov 28, 2022
Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash through feeding it pictures or videos.

Trash-Sorter-Extraordinaire Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash

Rameen Mahmood 1 Nov 07, 2021
This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''.

Sparse VAE This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''. Data Sources The datasets used in this paper wer

Gemma Moran 17 Dec 12, 2022
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022