scholarly journals Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

2019 ◽  
Vol 11 (9) ◽  
pp. 1012 ◽  
Author(s):  
Prajowal Manandhar ◽  
Prashanth Reddy Marpu ◽  
Zeyar Aung ◽  
Farid Melgani
Keyword(s):  
Deep Learning ◽  
United Arab Emirates ◽  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
The Road ◽  
Second Stage ◽  
Road Map ◽  
Road Network Extraction ◽  
Multi Agent

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.

scholarly journals FPGA IMPLEMENTATION OF ROAD NETWORK EXTRACTION USING MORPHOLOGICAL OPERATOR

2016 ◽  
Vol 35 (2) ◽  
pp. 93 ◽  
Author(s):  
Sujatha Chinnathevar ◽  
Selvathi Dharmar
Keyword(s):  
Road Network ◽  
Hardware Implementation ◽  
Ground Truth ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Average Value ◽  
Road Map ◽  
Road Network Extraction ◽  
Morphological Operator

In the remote sensing analysis, automatic extraction of road network from satellite or aerial images is the most needed approach for efficient road database creation, refinement, and updating. Mathematical morphology is a tool for extracting the features of an image that are useful in the representation and description of region shape. Morphological operator plays a significant role in the extraction of road network from satellite images. Most of the image processing algorithms need to handle large amounts of data, high repeatability, and general software is relatively slow to implement, so the system cannot achieve real-time requirements. In this paper, field programmable gate array (FPGA) architecture designed for automatic extraction of road centerline using morphological operator is proposed. Based on simulation and implementation, results are discussed in terms of register transfer level (RTL) design, FPGA editor and resource estimation. For synthesis and implementation of the above architecture, Spartan 3 XC3S400TQ144-4 device is used. The hardware implementation results are compared with software implementation results. The performance of proposed method is evaluated by comparing the results with ground truth road map as reference data and performance measures such as completeness, correctness and quality are calculated. In the software imple-mentation, the average value of completeness, correctness, and quality of various images are 90%, 96%, and 87% respectively. In the hardware implementation, the average value of completeness, correctness, and quality of various images are 87%, 94%, and 85% respectively. These measures prove that the proposed work yields road network very closer to reference road map.

scholarly journals ROAD EXTRACTION AND VECTORIZATION FROM AERIAL IMAGE DATA

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1323-1327
Author(s):  
L. Zhu ◽  
Y. Li ◽  
H. Shimamura
Keyword(s):  
Road Network ◽  
Image Data ◽  
Road Surface ◽  
Aerial Image ◽  
Surface Model ◽  
Center Line ◽  
Road Extraction ◽  
The Road ◽  
Road Map ◽  
Road Network Extraction

Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was proposed to address the issues of tedious steps on segmentation, recognition and grouping, using the digital surface model (DSM) only. The key feature of the proposed approach was the cross validation of the road basic elements, which was applied all the way through the entire procedure of road extraction. In the second stage, the regularized road map was produced where center line and side lines subject to parallel and even layout rules. It gives more accurate and reliable map by utilizing both the height information of the DSM and the color information of the ortho image. Road surface was extracted from the image by region growing. Then, a regularized center line was modeled by linear regression using all the pixels of the road surface. The road width was estimated and two road side lines were modeled as the straight lines parallel with the center line. Finally, the road model was built up in terms of vectorized points and lines. The experimental results showed that the proposed approach performed satisfactorily in our test site.

scholarly journals AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 925-932
Author(s):  
Y. Wei ◽  
X. Hu ◽  
M. Zhang ◽  
Y. Xu
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Tensor Voting ◽  
Road Extraction ◽  
The Road ◽  
Road Width ◽  
Road Intersections ◽  
On The Road ◽  
Segmentation Problem

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.

scholarly journals A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

2020 ◽  
Vol 12 (5) ◽  
pp. 765 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramon Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Road Network ◽  
Ideal Solution ◽  
The Road

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

scholarly journals Road Network Extraction from High Resolution Multispectral Satellite Imagery Based on Object Oriented Techniques

2014 ◽  
Vol II-8 ◽  
pp. 107-110 ◽  
Author(s):  
M. Kumar ◽  
R. K. Singh ◽  
P. L. N. Raju ◽  
Y. V. N. Krishnamurthy
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Network ◽  
Object Oriented ◽  
Integrated Approach ◽  
Morphological Operations ◽  
Target Feature ◽  
Fuzzy Classifier ◽  
The Road ◽  
Road Network Extraction

High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resolution space imagery. The proposed approach begins with segmentation of the scene with Multi-resolution Object Oriented segmentation. This step focuses on exploiting both spatial and spectral information for the target feature extraction. The road regions are automatically identified using a soft fuzzy classifier based on a set of predefined membership functions. A number of shape descriptors are computed to reduce the misclassifications between road and other spectrally similar objects. The detected road segments are further refined using morphological operations to form final road network, which is then evaluated for its completeness, correctness and quality. The experiments were carried out of fused IKONOS 2 , Quick bird ,Worldview 2 Products with fused resolution’s ranging from 0.5m to 1 m. Results indicate that the proposed methodology is effective in extracting accurate road networks from high resolution imagery.

scholarly journals With Geospatial in Path of Smart City

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1381-1387 ◽  
Author(s):  
A. S. Homainejad
Keyword(s):  
Information And Communication Technologies ◽  
Smart City ◽  
Traffic Management ◽  
Road Network ◽  
Aerial Images ◽  
Transport Systems ◽  
City Management ◽  
Traffic Jam ◽  
The Road ◽  
On The Road

With growth of urbanisation, there is a requirement for using the leverage of smart city in city management. The core of smart city is Information and Communication Technologies (ICT), and one of its elements is smart transport which includes sustainable transport and Intelligent Transport Systems (ITS). Cities and especially megacities are facing urgent transport challenge in traffic management. Geospatial can provide reliable tools for monitoring and coordinating traffic. In this paper a method for monitoring and managing the ongoing traffic in roads using aerial images and CCTV will be addressed. In this method, the road network was initially extracted and geo-referenced and captured in a 3D model. The aim is to detect and geo-referenced any vehicles on the road from images in order to assess the density and the volume of vehicles on the roads. If a traffic jam was recognised from the images, an alternative route would be suggested for easing the traffic jam. In a separate test, a road network was replicated in the computer and a simulated traffic was implemented in order to assess the traffic management during a pick time using this method.

scholarly journals SEMI-AUTOMATIC ROAD NETWORK EXTRACTION FROM DIGITAL IMAGES USING OBJECT-BASED CLASSIFICATION AND MORPHOLOGICAL OPERATORS

2018 ◽  
Vol 24 (4) ◽  
pp. 485-502
Author(s):  
Darlan Miranda Nunes ◽  
Nilcilene das Graças Medeiros ◽  
Afonso de Paula dos Santos
Keyword(s):  
Road Network ◽  
Digital Images ◽  
Test Site ◽  
Extraction Process ◽  
Morphological Operators ◽  
The Road ◽  
Road Network Extraction ◽  
Object Based ◽  
Matching Procedure ◽  
Automated Processes

Abstract: The demand for geospatial data concerning road network is constant, due to the wide variety of application which needs this type of data. It stands out the importance of this data in cartography update cycles, that can be obtained using automated processes of feature extraction in digital images, which are more accurate, fast and less costly than the traditional methods. In this sense, this work aimed the road network extraction from RapidEye satellite imagery, by developing a hybrid methodology using techniques of object-based image classification and morphological operators. The methodology was tested in three different sites, with images acquired in distinct dates, and the extraction process was evaluated through metrics obtained from the linear matching procedure. By the proposed extraction process, were achieved in terms of correctness and completeness the values of 92.23% and 85.15% for test site 1, the values of 79.16% and 81.06% for test site 2, and the values of 82.05% and 92.22% for test site 3, respectively. The results shown that the proposed methodology presented a good performance for semi-automatic road network extraction from Rapideye images, representing an alternative to auxiliary road network database acquisition and updating.

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