AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON  MULTIPLE FEATURES FROM LiDAR DATA

Li, Y.; Hu, Xuehai; Guan, Haiyan; Liu, P.

doi:10.5194/isprs-archives-xli-b3-289-2016

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…A low computational cost is obtained, and the necessity to improve the quality of road extraction is emphasized as a future enhancement. 7 Lu et al presented DL techniques of the richer convolutional features network, which is used to detect the edge model. Also, this method helps to extract the building edges from highresolution spatial imagery.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic building footprint extraction and road detection from hyperspectral imagery

2022

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.Hyperspectral imagery gives details of spectral information through hundreds of spectral bands also known as dimensionality. The bands with continuous spectral information model are capable of classifying various materials of interest. The enhanced dimensionality of such data allows for major changes in data relevant information, but it also presents a challenge to traditional methods for accurate hyperspectral image analysis so-called “curse of dimensionality.” The hyperspectral images are used to identify objects on the earth’s surface. Due to a large number of spectral bands, classifying objects using hyperspectral imagery has become more challenging. Feature reduction and extraction techniques are used to address these high-dimensionality issues. However, there are various challenges dealing with data classification with performance and computational time. As a result, a technique for hyperspectral image classification based on a convolutional neural network (CNN) along with geometric transformation and polygons segmentation process has been proposed. A CNN is used to convert compressed features into high-level features that were utilized to classify objects into buildings and road networks. The main objective of this paper is to design an automated building footprint extraction and road detection from hyperspectral imagery using CNN. The polygons segmentation is used for the extraction and detection of spectral features in hyperspectral data. CNN is used to classify these extracted spectral features, such as building footprints and road detection, using different kernels. When comparing the proposed techniques with other support vector machine and fully convolutional network methods, the experimental results show that CNN provides 97% classification accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic building footprint extraction and road detection from hyperspectral imagery

2022

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“…Clode, Rottensteiner have extracted and vectorized roads from LiDAR data using attributes such as intensity and local point density of point clouds near the digital terrain model (DTM) [4,5]. Li, Hu, have described a road extraction method using multiple features and using hierarchical primitive groupings to connect road segments for form networks [6]. Liu, Zhang applied the generalized Hough Transform for road detection [7].…”

Section: Literature Review and Prior Artmentioning

confidence: 99%

To Expedite Roadway Identification and Damage Assessment in LiDAR 3D Imagery for Disaster Relief Public Assistance

2022

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Aerial surveys using LiDAR systems can play a vital role in the quantitative assessment of infrastructure damage caused by hurricanes, floods, and other natural disasters. GmAPD LiDAR provides high-resolution 3D point-cloud data which enables the surveyor to take accurate measurements of damages to roads, buildings, communication towers, power lines, etc. Due to the high point cloud density, a very large volume of data is generated during an aerial survey. The data collected during the airborne imaging is post-processed with calibration, geo-registration, and segmentation. Albeit very accurate, extracting useful information from this data is a slow and laborious process. For disaster response, methods of automating this process have spurred the development of simple, fast algorithms that can be used to recognize physical structures from the point-cloud data that can later be assessed for structural damage. In this paper, we describe an efficient algorithm to extract roadways from a massive Lidar data-set to assist the Federal Emergency Management Agency (FEMA) in assessing road conditions as a step toward helping surveyors expedite a quantitative assessment of road damages for providing and distributing public assistance for disaster relief.

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“…The vibration of the vehicle during driving will have a greater impact on the image information collected by the camera and affect the accuracy of the image. Most of the research on road recognition [17][18] [19]mainly provides safety services such as obstacle avoidance for vehicles, without considering the aspect of improving car ride comfort.…”

Section: Introductionmentioning

confidence: 99%

Road Roughness Recognition Based on Lidar

Chen

2022

J. Phys.: Conf. Ser.

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The ups and downs of the road will have a great impact on the driving performance of the vehicle. Identify the road surface information in front of the vehicle in advance, and transmit this information to the vehicle’s control system. The control system can carry out targeted control according to the road conditions. Since the information on the road surface of the vehicle is mastered, the driving performance of the vehicle can be greatly improved. Firstly, a vehicle road recognition system is constructed using lidar as an environmental perception sensor to scan the road information ahead of the vehicle. Secondly, use GPS and inertial navigation system to monitor the vehicle’s driving position and attitude information, perform motion compensation on the lidar point cloud data, and extract the road point cloud. Thirdly, the elevation information of the road in front of the vehicle is obtained through filtering and interpolation of the point cloud. Finally, the error analysis of this method is carried out. The results show that the road elevation recognition error is within an acceptable range.

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AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

Cited by 10 publications

References 9 publications

Automatic building footprint extraction and road detection from hyperspectral imagery

Automatic building footprint extraction and road detection from hyperspectral imagery

To Expedite Roadway Identification and Damage Assessment in LiDAR 3D Imagery for Disaster Relief Public Assistance

Road Roughness Recognition Based on Lidar

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