Anh Vu Vo

Dr. Anh Vu Vo

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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2015 in ISPRS Journal of Photogrammetry and Remote Sensing
Verified
Journal Article
Closed access
AVAnh Vu Vo
Anh Vu Vo
Linh TRUONG-HONGLinh TRUONG-HONG
Linh TRUONG-HONG
Debra LaeferDebra Laefer
Debra Laefer
MBMichela Bertolotto
Michela Bertolotto
11 August 2016 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Verified
Journal Article
Open access
AVAnh Vu Vo
Anh Vu Vo
LTL. Truong-Hong
L. Truong-Hong
Debra LaeferDebra Laefer
Debra Laefer
Dirk TiedeDirk Tiede
Dirk Tiede
SDSebastian D'Oleire-Oltmanns
Sebastian D'Oleire-Oltmanns
DTD. Tuia
D. Tuia
In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m 2 ). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identification, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A.
1 January 2016 in MethodsX
Verified
Journal Article
Open access
Pirouz NourianPirouz Nourian
Pirouz Nourian
RGRomulo Gonçalves
Romulo Gonçalves
Sisi ZlatanovaSisi Zlatanova
Sisi Zlatanova
KOKen Arroyo Ohori
Ken Arroyo Ohori
AVAnh Vu Vo
Anh Vu Vo
Voxel representations have been used for years in scientific computation and medical imaging. The main focus of our research is to provide easy access to methods for making large-scale voxel models of built environment for environmental modelling studies while ensuring they are spatially correct, meaning they correctly represent topological and semantic relations among objects. In this article, we present algorithms that generate voxels (volumetric pixels) out of point cloud, curve, or surface objects. The algorithms for voxelization of surfaces and curves are a customization of the topological voxelization approach [1]; we additionally provide an extension of this method for voxelization of point clouds. The developed software has the following advantages:•It provides easy management of connectivity levels in the resulting voxels.•It is not dependant on any external library except for primitive types and constructs; therefore, it is easy to integrate them in any application.•One of the algorithms is implemented in C++ and C for platform independence and efficiency.
20 November 2016 in International Journal of Remote Sensing
Verified
Journal Article
Closed access
AVAnh Vu Vo
Anh Vu Vo
Debra LaeferDebra Laefer
Debra Laefer
MBMichela Bertolotto
Michela Bertolotto
While the rapid growth in the availability and quality of airborne laser scanning data offers unprecedented information, it challenges the existing data management and dissemination solutions. Data management has become a bottleneck to effective laser scanning data exploration. This paper documents the state-of-the-art techniques for aerial laser scanning (ALS) data storage and indexing during the post-processing stage. A brief history of laser scanning technology is presented with the intention to offer an overview about the evolution of the technology from the data point of view inclusive of full waveform data management. The key strategies for data handling, including data modelling and indexing, are extensively discussed.
1 September 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
Verified
Journal Article
Closed access
AVAnh Vu Vo
Anh Vu Vo
DLDebra F. Laefer
Debra F. Laefer
ASAljosa Smolic
Aljosa Smolic
SZS.M. Iman Zolanvari
S.M. Iman Zolanvari
21 October 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
CHChamin Nalinda Lokugam Hewage
Chamin Nalinda Lokugam Hewage
Debra LaeferDebra Laefer
Debra Laefer
AVAnh Vu Vo
Anh Vu Vo
NLNhien-An Le-Khac
Nhien-An Le-Khac
MBMichela Bertolotto
Michela Bertolotto
Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and novel data models (such as wide-column models) are considered. New structured query language (NewSQL) models are considered. The impacts of parallel architectures and data models are discussed with respect to theoretical perspectives and in the context of existing PCDM implementations. Based on the review, a methodical approach for the selection of parallel architectures and data models for highly scalable and performance-efficient PCDM system development is proposed. Finally, notable research gaps in the PCDM literature are presented as possible directions for future research.