SYNERGISTIC USE OF LIDAR DATA AND AERIAL IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BUILDING MODEL RECOGNITION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ALIDOOST FATEME; AREFI HOSSEIN

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2017 | Volume:4 | Issue:4 Page(s): 103-121

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

819

Download:

Cites:

Information Journal Paper

Title

SYNERGISTIC USE OF LIDAR DATA AND AERIAL IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BUILDING MODEL RECOGNITION

Author(s)

ALIDOOST FATEME | AREFI HOSSEIN | Issue Writer Certificate

Keywords

PATTERN RECOGNITIONQ1

DEEP LEARNINGQ1

3D MODELLINGQ2

CONVOLUTIONAL NEURAL NETWORKQ2

LIDARQ2

Abstract

Buildings are one of the most important urban structures that are used for various applications and urban mapping. In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LIDAR and aerial orthophotos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labelling in a supervised pre-trained CONVOLUTIONAL NEURAL NETWORK (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for CNN network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept that consists of a number of convolutional and sub-sampling layers in an adaptable structure which is widely used in PATTERN RECOGNITION and object detection applications. Since the training dataset is a small library of labelled models for different shapes of roofs, via using the pretrained models, the computation time of learning can decrease significantly. The experimental results highlight the effectiveness of the DEEP LEARNING approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information. Based on the training results, the top 1 error and accuracy of training are about 0.05 and 95 %, respectively. Moreover, the average of correctness and completeness rates are about 97% and 69%, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

ALIDOOST, FATEME, & AREFI, HOSSEIN. (2017). SYNERGISTIC USE OF LIDAR DATA AND AERIAL IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BUILDING MODEL RECOGNITION. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 4(4), 103-121. SID. https://sid.ir/paper/230096/en

Vancouver: Copy

ALIDOOST FATEME, AREFI HOSSEIN. SYNERGISTIC USE OF LIDAR DATA AND AERIAL IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BUILDING MODEL RECOGNITION. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2017;4(4):103-121. Available from: https://sid.ir/paper/230096/en

IEEE: Copy

FATEME ALIDOOST, and HOSSEIN AREFI, “SYNERGISTIC USE OF LIDAR DATA AND AERIAL IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BUILDING MODEL RECOGNITION,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 4, no. 4, pp. 103–121, 2017, [Online]. Available: https://sid.ir/paper/230096/en

Related Journal Papers