Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++

Shin, Young‐Ha; Son, Kyung-Wahn; Lee, Dong-Cheon

doi:10.3390/app12041975

Cited by 13 publications

(2 citation statements)

References 20 publications

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“…Hyeongmo Gu and Seungyeon Choo used façade datasets and deep learning to automatically mark façade data and effectively generate large-scale data sets to understand the characteristics of the street [12]. Young-ha Shin et al used PointNet++ to extract and segment buildings and found that the two echoes of the laser pulse could be fully identified and removed in said buildings [13]. The systems proposed in the thesis of Shi-Jinn Horng and Pin-siang Huang can identify the different states of a product with effective identification of the products [14].…”

Section: Background and Related Workmentioning

confidence: 99%

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

Zhuang,

Guo

2023

Applied Sciences

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The collection of information about buildings and their colors is an important aspect of urban planning. The intelligent recognition of buildings using image information plays a significant role in the development of smart cities and urban planning. This thesis proposes a building color-recognition technique based on morphological features utilizing convolutional neural networks and the K-means clustering algorithm of image-recognition technology. The proposed method can identify buildings in images and classify them into two categories, buildings and panoramas, for color extraction and matching. This method involves training convolutional neural networks on deep learning so that the buildings can be differentiated and segmented. Subsequently, the K-means algorithm extracts colors from the segmented building images. The extracted building category, color, and text information were analyzed to obtain a comparison and analysis results of buildings and panoramas. The results demonstrated that the system is capable of accurately segmenting buildings, as well as extracting colors from both buildings and panoramas separately. It can also contribute to the extraction and presentation of color schemes in smart city planning and provide valuable insights for the future development of urban colors.

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Section: Background and Related Workmentioning

confidence: 99%

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

Zhuang,

Guo

2023

Applied Sciences

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“…To verify the accuracy of 3D segmentation process, we employed global accuracy metric [24], which is defined as below:…”

Section: Feature Extractionmentioning

confidence: 99%

A Korean Cattle Weight Prediction Approach Using 3D Segmentation-Based Feature Extraction and Regression Machine Learning from Incomplete 3D Shapes Acquired from Real Farm Environments

Dang,

Lee,

Alam

et al. 2023

Agriculture

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Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model.

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Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas

Aslani,

Seipel

2023

Communications in Computer and Information Science

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Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++

Cited by 13 publications

References 20 publications

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

A Korean Cattle Weight Prediction Approach Using 3D Segmentation-Based Feature Extraction and Regression Machine Learning from Incomplete 3D Shapes Acquired from Real Farm Environments

Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas

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