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Mengchi Ai
Basic Info . 0000-0001-5300-4409 Honors and Awards . The user has no records in this section Career Timeline . The user has no records in this section. Short Biography . The user biography is not available. Following Followers Co-Authors The list of users this user is following is empty. Following: 0 users The user has no followers. Followers: 0 users Yuan Zhou Tongfan Surveying Engineerin... Jie Shan Lyles School of Civil Engine... Chun Liu Tongji University Hangbin Wu College of Surveying and Geo... Chen Yang Department of Landscape Arch... Zhuo Chen College of Surveying and Geo... Zhixin Li Lyles School of Civil Engine... Chun Liu College of Surveying and Geo... Yujie Cao College of Surveying and Geo... Co-Authors: 9 users View all Feed . Journal article Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds Mengchi Ai Zhixin Li Jie Shan https://doi.org/10.3390/rs13193844 Published: 26 September 2021 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments. ACS Style Mengchi Ai; Zhixin Li; Jie Shan. Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds. Remote Sensing 2021 , 13 , 3844 . AMA Style Mengchi Ai, Zhixin Li, Jie Shan. Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds. Remote Sensing. 2021; 13 (19):3844. Chicago/Turabian Style Mengchi Ai; Zhixin Li; Jie Shan. 2021. "Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds." Remote Sensing 13, no. 19: 3844. Journal article Pattern identification and analysis for the traditional village using low altitude UAV-borne remote sensing: multifeatured geospatial data to support rural landscape investigation, documentation and management Chun Liu Yujie Cao Chen Yang Yuan Zhou Mengchi Ai https://doi.org/10.1016/j.culher.2019.12.013 Published: 07 February 2020 in Journal of Cultural Heritage . Reads? 0 Downloads? 0 Abstract Cite All recommendations Spatial pattern of landscapes is viewed as the fabric and structure of traditional village. Accurate detection and analysis for the landscapes pattern plays key role in understanding the sociocultural milieu and human-natural relations. Current methods perceive their problems. Pedestrian survey is labor and time consuming. Meanwhile, the derived data tend to be subjective, qualitative and monotonous, which can hardly be used for further analysis. Remote sensing technique has been successfully applied in the field of heritage protection for its ability in object detection. But these methods are limited by visiting circle, spatial resolution and data richness. Therefore, the scientific methods of landscape pattern detection, documentation and analysis for the traditional village has long been under discussion. By taking Baojiatun castle village as a case study, the present paper aims to detect and analyze spatial pattern of traditional village by the geospatial data from a low altitude UAV-borne remote sensing. A four-leveled hierarchical landscape recognition scheme and the corresponding landscape category regulation were established. Based on the derived data and the established scheme, a three-level classification model was construct by using Object-Oriented Image Analysis method (OBIA) method and machine learning classifiers (Random Forest classifier and SVM classifier). The model was proven to be accurate and stable by ten-fold cross validation, and five major heritage landscape elements of the village were finally extracted. Furthermore, spatial pattern characteristics and distribution differences of targeted landscapes were unveiled based on distance statistics and clustering analysis. Lastly, further discussion is fostered, which focuses on the usefulness of remote sensing technique in the field of heritage landscape investigation, documentation and management. ACS Style Chun Liu; Yujie Cao; Chen Yang; Yuan Zhou; Mengchi Ai. Pattern identification and analysis for the traditional village using low altitude UAV-borne remote sensing: multifeatured geospatial data to support rural landscape investigation, documentation and management. Journal of Cultural Heritage 2020 , 44 , 185 -195. AMA Style Chun Liu, Yujie Cao, Chen Yang, Yuan Zhou, Mengchi Ai. Pattern identification and analysis for the traditional village using low altitude UAV-borne remote sensing: multifeatured geospatial data to support rural landscape investigation, documentation and management. Journal of Cultural Heritage. 2020; 44 ():185-195. Chicago/Turabian Style Chun Liu; Yujie Cao; Chen Yang; Yuan Zhou; Mengchi Ai. 2020. "Pattern identification and analysis for the traditional village using low altitude UAV-borne remote sensing: multifeatured geospatial data to support rural landscape investigation, documentation and management." Journal of Cultural Heritage 44, no. : 185-195. Research article Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform Chun Liu Mengchi Ai Zhuo Chen Yuan Zhou Hangbin Wu https://doi.org/10.1155/2018/6869807 Published: 27 May 2018 in Journal of Sensors . Reads? 0 Downloads? 0 Abstract Cite All recommendations The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments. ACS Style Chun Liu; Mengchi Ai; Zhuo Chen; Yuan Zhou; Hangbin Wu. Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform. Journal of Sensors 2018 , 2018 , 1 -12. AMA Style Chun Liu, Mengchi Ai, Zhuo Chen, Yuan Zhou, Hangbin Wu. Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform. Journal of Sensors. 2018; 2018 ():1-12. Chicago/Turabian Style Chun Liu; Mengchi Ai; Zhuo Chen; Yuan Zhou; Hangbin Wu. 2018. "Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform." Journal of Sensors 2018, no. : 1-12. .
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