@article{vivek nanda_baran_tateosian_nelson_hu_2023, title={Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling}, volume={44}, ISSN={["1366-5901"]}, DOI={10.1080/01431161.2023.2282405}, abstractNote={ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.}, number={22}, journal={INTERNATIONAL JOURNAL OF REMOTE SENSING}, author={Vivek Nanda, Vishnu Mahesh and Baran, Perver and Tateosian, Laura and Nelson, Stacy A. C. and Hu, Jianxin}, year={2023}, month={Nov}, pages={7156–7186} }
 @book{hu_2022, place={San Diego, CA}, title={Building Environmental Control Systems Illustrated}, ISBN={9781793575883}, publisher={Cognella Academic Publishing}, author={Hu, Jianxin}, year={2022} }
 @article{beasley_monsur_hu_dunn_madden_2022, title={The bacterial community of childcare centers: potential implications for microbial dispersal and child exposure}, volume={17}, ISSN={["2524-6372"]}, DOI={10.1186/s40793-022-00404-6}, abstractNote={Abstract}, number={1}, journal={ENVIRONMENTAL MICROBIOME}, author={Beasley, D. E. and Monsur, M. and Hu, J. and Dunn, R. R. and Madden, A. A.}, year={2022}, month={Mar} }
 @article{mohsenin_hu_2017, title={DAYLIGHT PREDICTION IN INDIVIDUAL FLOORS USING WELL INDEX}, volume={10}, ISSN={["1899-0142"]}, DOI={10.21307/acee-2017-024}, abstractNote={Abstract}, number={2}, journal={ARCHITECTURE CIVIL ENGINEERING ENVIRONMENT}, author={Mohsenin, Mahsan and Hu, Jianxin}, year={2017}, month={Jun}, pages={109–114} }
 @article{mohsenin_hu_2015, title={Assessing daylight performance in atrium buildings by using Climate Based Daylight Modeling}, volume={119}, ISSN={0038-092X}, url={http://dx.doi.org/10.1016/J.SOLENER.2015.05.011}, DOI={10.1016/J.SOLENER.2015.05.011}, abstractNote={This research focuses on daylight assessment in office buildings with different atrium types, proportions and roof aperture designs. The goal is to assess and optimize atrium type and proportions to improve energy efficiency of atrium buildings. This paper investigates daylight metrics in central, attached and semi-enclosed atrium types with different proportions and roof aperture designs, such as monitor and horizontal skylight. Daylight performance is measured based on the proportions of an atrium that are defined by Well Index (WI), used to characterize atria. Climate-Based Daylight Modeling (CBDM) is applied as the assessment strategy with U.S Climate Zone 3 as the climatic setting. Spatial Daylight Autonomy (sDA) and Annual Solar Exposure (ASE) are adopted as the dynamic daylight metrics to compare the results. This study also validates DIVA for Rhino as the simulation tool by comparing daylight results of the computer simulation with the same scale-model. This research applies both scale-model and computer simulation methods to assess daylight and energy performance in atrium buildings based on Well Index. This paper then employs DIVA simulation tool to assess daylight performance based on the Well Index. The results demonstrate that Well Index is a reliable indicator to characterize atrium proportion and confirm that Well Index works with (CBDM). Having assessed the impact of design parameters, such as climate, building thickness, material reflectance, material transmittance, furniture and monitor roof glazing height, the study potentially provides architects with an atrium design database for U.S. Climate Zone 3. This database compares daylight metrics for Well Index of 0.5, 1 and 2 in central, attached and semi-enclosed atrium types using different roof aperture designs.}, journal={Solar Energy}, publisher={Elsevier BV}, author={Mohsenin, Mahsan and Hu, Jianxin}, year={2015}, month={Sep}, pages={553–560} }
 @inproceedings{malekafzali ardakan_ghobad_hu_place_2013, title={Comparison of climate-based daylighting in two integrated simulation tools: DIVA and OpenStudio}, booktitle={PLEA2013 - 29th Conference, Sustainable Architecture for a Renewable Future}, author={Malekafzali Ardakan, A. and Ghobad, L. and Hu, Jianxin and Place, Wayne}, year={2013}, pages={1–7} }