paper.bib

@INPROCEEDINGS{9267879-1,
  author={Ivanov, Sergey and Nikolskaya, Ksenia and Radchenko, Gleb and Sokolinsky, Leonid and Zymbler, Mikhail},
  booktitle={2020 Global Smart Industry Conference (GloSIC)}, 
  title={Digital Twin of City: Concept Overview}, 
  year={2020},
  volume={},
  number={},
  pages={178-186},
  doi={10.1109/GloSIC50886.2020.9267879}}

@INPROCEEDINGS{9254288-2,
  author={Erol, Tolga and Mendi, Arif Furkan and Doğan, Dilara},
  booktitle={2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)}, 
  title={Digital Transformation Revolution with Digital Twin Technology}, 
  year={2020},
  volume={},
  number={},
  pages={1-7},
  doi={10.1109/ISMSIT50672.2020.9254288}}
  
@article{DAI201977-3,
title = {Thermal impacts of greenery, water, and impervious structures in Beijing’s Olympic area: A spatial regression approach},
journal = {Ecological Indicators},
volume = {97},
pages = {77-88},
year = {2019},
issn = {1470-160X},
doi = {10.1016/j.ecolind.2018.09.041},
url = {https://www.sciencedirect.com/science/article/pii/S1470160X18307386},
author = {Zhaoxin Dai and Jean-Michel Guldmann and Yunfeng Hu},
keywords = {Urban heat island, Land uses, Trees, Grass, Spatial autocorrelation},
abstract = {This paper explores the urban land-use determinants of the urban heat island (UHI) in Beijing’s Olympic Area, using different statistical models, land surface temperatures (LST) derived from Landsat 8 remote sensing, and land-use data derived from 1-m high-resolution imagery. Data are captured over grids of different sizes. Spatial regressions are necessary to capture neighboring effects, particularly when the grid unit is small. Grass, trees, water bodies, and shades have all significant and negative effects on LST, whereas buildings, roads and other impervious surfaces have all significant and positive effects. The results also point to significant nonlinear and interaction effects of grass, trees and water, particularly when the grid cell size is small (60 m-90 m). Trees are found to be the most important predictor of LST. When the grids are smaller than 180 m, the indirect impacts are larger than the direct ones, whereas, the opposite takes place for larger grids. Because of their strong performance (R2 ranging from 0.839 to 0.970), the models can be used for predicting the impacts of land-use changes on the UHI and as tools for urban planning. Finally, extensive uncertainty and sensitivity analyses show that the models are very reliable in terms of both input data accuracy and estimated coefficients precision.}
}

@article{PARK2021101655-4,
title = {Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches},
journal = {Computers, Environment and Urban Systems},
volume = {88},
pages = {101655},
year = {2021},
issn = {0198-9715},
doi = {10.1016/j.compenvurbsys.2021.101655},
url = {https://www.sciencedirect.com/science/article/pii/S0198971521000624},
author = {Yujin Park and Jean-Michel Guldmann and Desheng Liu},
keywords = {3D city model, Tree shade, Shade location, Urban heat mitigation, Greening scenario, Spatial regression},
abstract = {The continued increase in average and extreme temperatures around the globe is expected to strike urban communities more harshly because of the urban heat island (UHI). Devising natural and design-based solutions to stem the rising heat has become an important urban planning issue. Recent studies have examined the impacts of 2D/3D urban land-use structures on land surface temperature (LST), but with little attention to the shades cast by 3D objects, such as buildings and trees. It is, however, known that shades are particularly relevant for controlling summertime temperatures. This study examines the role of urban shades created by trees and buildings, focusing on the effects of shade extent and location on LST mitigation. A realistic 3D digital representation of urban and suburban landscapes, combined with detailed 2D land cover information, is developed. Shadows projected on horizontal and vertical surfaces are obtained through GIS analysis, and then quantified as independent variables explaining LST variations over grids of varying sizes with spatial regression models. The estimation results show that the shades on different 3D surfaces, including building rooftops, sun-facing façades, not-sun-facing façades, and on 2D surfaces including roadways, other paved covers, and grass, have cooling effects of varying impact, showing that shades clearly modify the thermal effects of urban built-up surfaces. Tree canopy volume has distinct effects on LST via evapotranspiration. One of the estimated models is used, after validation, to simulate the LST impacts of neighborhood scenarios involving additional greening. The findings illustrate how urban planners can use the proposed methodology to design 3D land-use solutions for effective heat mitigation.}
}

@article{WU2021116884-5,
title = {Coupled optical-electrical-thermal analysis of a semi-transparent photovoltaic glazing façade under building shadow},
journal = {Applied Energy},
volume = {292},
pages = {116884},
year = {2021},
issn = {0306-2619},
doi = {10.1016/j.apenergy.2021.116884},
url = {https://www.sciencedirect.com/science/article/pii/S030626192100372X},
author = {Jing Wu and Ling Zhang and Zhongbing Liu and Zhenghong Wu},
keywords = {Semi-transparent glazing façade, Building shadow, Three-dimensional heat transfer, Implicit finite difference, Optical-electrical-thermal simulation},
abstract = {The semi-transparent photovoltaic glazing (STPVG) façade can introduce comfortable daylight into the indoor space and achieve energy efficiency, which is a promising PV glazing façade system. However, it is susceptible to building shadow, reducing power generation efficiency. This paper established a coupled optical-electrical-thermal model under dynamic changing building eave shadow of the STPVG façade and built a full-scale experiment platform to test and verify the coupled model. The model was then used to simulate and analyze the electrical performance and the temperature distribution of the STPVG under different eave shadow. The results show that the I/V curve appears multi-knee shape and the P/V curve appears multi-peak shape due to the different shadow coefficient in each PV string. Furthermore, the annual overall energy performance of STPVG in Changsha, China was compared with different eave width. The transmitted solar radiation, the energy generation and energy conversion efficiency, and the total heat gain decrease with the eave width increases in the months when the shadow appears. When the eave width is 0.29 m, the monthly largest transmission loss rate is in May at 3.86%; the largest energy generation loss rate is in April at 15.3%; and the largest indoor heat gain reduction rate is in August at 3.28%. This study can provide theoretical guidance for the system optimization and engineering application of the STPVG in building energy conservation.}
}

@article{YADAV201811-6,
title = {Performance of building integrated photovoltaic thermal system with PV module installed at optimum tilt angle and influenced by shadow},
journal = {Renewable Energy},
volume = {127},
pages = {11-23},
year = {2018},
issn = {0960-1481},
doi = {10.1016/j.renene.2018.04.030},
url = {https://www.sciencedirect.com/science/article/pii/S0960148118304373},
author = {S. Yadav and S.K. Panda and M. Tripathy},
keywords = {BIPV thermal system, Optimum tilt angle, HDKR model, Energy equilibrium equation, Sky view factor, Shading coefficient},
abstract = {Building integrated photovoltaic (BIPV) thermal system is an efficient system for urban applications to convert a building to net zero energy buildings by utilizing solar insolation. In this study, HDKR/S (Hay, Davies, Klucher, Reindl/shadow) model is developed which is a modified HDKR model where influence of shadow is incorporated in the mathematical model. Four discrete rectangular buildings situated in four directions (North, South, East and West) around a BIPV thermal system are considered for creating adverse effect of shadow. Variation of width (B), storey height (H) and horizontal distance (D) of these surrounded buildings are taken into account for evaluating optimum tilt angle, insolation and performance of BIPV thermal system by introducing corresponding shadow effects. The performance of the system is adversely affected because of the presence of surrounded building located at close proximity i.e., due to higher influence of shading and sky view blocking effects.}
}


@Article{rs13152862-7,
AUTHOR = {Xie, Yakun and Feng, Dejun and Xiong, Sifan and Zhu, Jun and Liu, Yangge},
TITLE = {Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery},
JOURNAL = {Remote Sensing},
VOLUME = {13},
YEAR = {2021},
NUMBER = {15},
ARTICLE-NUMBER = {2862},
URL = {https://www.mdpi.com/2072-4292/13/15/2862},
ISSN = {2072-4292},
ABSTRACT = {Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m–3.76 m, which can achieve high-precision estimation of building height.},
DOI = {10.3390/rs13152862}
}


@article{CHEN2020114-8,
title = {An end-to-end shape modeling framework for vectorized building outline generation from aerial images},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {170},
pages = {114-126},
year = {2020},
issn = {0924-2716},
doi = {10.1016/j.isprsjprs.2020.10.008},
url = {https://www.sciencedirect.com/science/article/pii/S092427162030280X},
author = {Qi Chen and Lei Wang and Steven L. Waslander and Xiuguo Liu},
keywords = {Building segmentation, Boundary optimization, Automatic mapping, Deep learning, Shape modeling},
abstract = {The identification and annotation of buildings has long been a tedious and expensive part of high-precision vector map production. The deep learning techniques such as fully convolution network (FCN) have largely promoted the accuracy of automatic building segmentation from remote sensing images. However, compared with the deep-learning-based building segmentation methods that greatly benefit from data-driven feature learning, the building boundary vector representation generation techniques mainly rely on handcrafted features and high human intervention. These techniques continue to employ manual design and ignore the opportunity of using the rich feature information that can be learned from training data to directly generate vectorized boundary descriptions. Aiming to address this problem, we introduce PolygonCNN, a learnable end-to-end vector shape modeling framework for generating building outlines from aerial images. The framework first performs an FCN-like segmentation to extract initial building contours. Then, by encoding the vertices of the building polygons along with the pooled image features extracted from segmentation step, a modified PointNet is proposed to learn shape priors and predict a polygon vertex deformation to generate refined building vector results. Additionally, we propose 1) a simplify-and-densify sampling strategy to generate homogeneously sampled polygon with well-kept geometric signals for shape prior learning; and 2) a novel loss function for estimating shape similarity between building polygons with vastly different vertex numbers. The experiments on over 10,000 building samples verify that PolygonCNN can generate building vectors with higher vertex-based F1-score than the state-of-the-art method, and simultaneously well maintains the building segmentation accuracy achieved by the FCN-like model.}
}


@article{bolin2020investigation-9,
  title={An investigation of the influence of the refractive shadow zone on wind turbine noise},
  author={Bolin, Karl and Conrady, Kristina and Karasalo, Ilkka and Sj{\"o}blom, Anna},
  journal={The Journal of the Acoustical Society of America},
  volume={148},
  number={2},
  pages={EL166--EL171},
  year={2020},
  publisher={Acoustical Society of America},
  doi={10.1121/10.0001589}
}

@inproceedings{zhou2015integrated-10,
  title={An integrated approach for shadow detection of the building in urban areas},
  author={Zhou, Guoqing and Han, Caiyun and Ye, Siqi and Wang, Yuefeng and Wang, Chenxi},
  booktitle={International Conference on Intelligent Earth Observing and Applications 2015},
  volume={9808},
  pages={98082W},
  year={2015},
  doi = {10.1117/12.2207632},
  organization={International Society for Optics and Photonics}
}


@Article{rs12040679-11,
AUTHOR = {Zhou, Guoqing and Sha, Hongjun},
TITLE = {Building Shadow Detection on Ghost Images},
JOURNAL = {Remote Sensing},
VOLUME = {12},
YEAR = {2020},
NUMBER = {4},
ARTICLE-NUMBER = {679},
URL = {https://www.mdpi.com/2072-4292/12/4/679},
ISSN = {2072-4292},
ABSTRACT = {Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.},
DOI = {10.3390/rs12040679}
}

@article{RAFIEE2014397-12,
title = {From BIM to Geo-analysis: View Coverage and Shadow Analysis by BIM/GIS Integration},
journal = {Procedia Environmental Sciences},
volume = {22},
pages = {397-402},
year = {2014},
note = {12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014},
issn = {1878-0296},
doi = {10.1016/j.proenv.2014.11.037},
url = {https://www.sciencedirect.com/science/article/pii/S1878029614001844},
author = {Azarakhsh Rafiee and Eduardo Dias and Steven Fruijtier and Henk Scholten},
keywords = {BIM, GIS, Shadow, Analysis},
abstract = {Data collection is moving towards more details and larger scales and efficient ways of interpreting the data and analysing it is of great importance. A Building Information Model (BIM) includes very detailed and accurate information of a construction. However, this information model is not necessarily geo located but uses a local coordinate system hampering environmental analysis. Transforming the BIM to its corresponding geo-located model helps answering many environmental questions efficiently. In this research, we have applied methods to automatically transform the geometric and semantic information of a BIM model to a geo-referenced model. Two analyses, namely view and shadow analysis, have been performed using the geometric and semantic information within the geo-referenced BIM model and other existing geospatial elements. These analyses demonstrate the value of integrating BIM and spatial data for e.g. spatial planning.}
}

@article{HONG2016408-13,
title = {Estimation of the Available Rooftop Area for Installing the Rooftop Solar Photovoltaic (PV) System by Analyzing the Building Shadow Using Hillshade Analysis},
journal = {Energy Procedia},
volume = {88},
pages = {408-413},
year = {2016},
note = {CUE 2015 - Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems},
issn = {1876-6102},
doi = {10.1016/j.egypro.2016.06.013},
url = {https://www.sciencedirect.com/science/article/pii/S1876610216300777},
author = {Taehoon Hong and Minhyun Lee and Choongwan Koo and Jimin Kim and Kwangbok Jeong},
keywords = {Rooftop solar photovoltaic (PV) system, Hillshade analysis, Building shadow, Available rooftop area},
abstract = {For continuous promotion of the solar PV system in buildings, it is crucial to analyze the rooftop solar PV potential. However, the rooftop solar PV potential in urban areas highly varies depending on the available rooftop area due to the building shadow. In order to estimate the available rooftop area accurately by considering the building shadow, this study proposed an estimation method of the available rooftop area for installing the rooftop solar PV system by analyzing the building shadow using Hillshade Analysis. A case study of Gangnam district in Seoul, South Korea was shown by applying the proposed estimation method.}
}

@Article{ijgi7100413-13,
AUTHOR = {Agius, Tyler and Sabri, Soheil and Kalantari, Mohsen},
TITLE = {Three-Dimensional Rule-Based City Modelling to Support Urban Redevelopment Process},
JOURNAL = {ISPRS International Journal of Geo-Information},
VOLUME = {7},
YEAR = {2018},
NUMBER = {10},
ARTICLE-NUMBER = {413},
URL = {https://www.mdpi.com/2220-9964/7/10/413},
ISSN = {2220-9964},
ABSTRACT = {Multi-dimensional representation of urban settings has received a great deal of attention among urban planners, policy makers, and urban scholars. This is due to the fact that cities grow vertically and new urbanism strategies encourage higher density and compact city development. Advancements in computer technology and multi-dimensional geospatial data integration, analysis and visualisation play a pivotal role in supporting urban planning and design. However, due to the complexity of the models and technical requirements of the multi-dimensional city models, planners are yet to fully exploit such technologies in their activities. This paper proposes a workflow to support non-experts in using three-dimensional city modelling tools to carry out planning control amendments and assess their implications. The paper focuses on using a parametric three-dimensional (3D) city model to enable planners to measure the physical (e.g., building height, shadow, setback) and functional (e.g., mix of land uses) impacts of new planning controls. The workflow is then implemented in an inner suburb of Metropolitan Melbourne, where urban intensification strategies require the planners to carry out radical changes in regulations. This study demonstrates the power of the proposed 3D visualisation tool for urban planners at taking two-dimensional (2D) Geographic Information System (GIS) procedural modelling to construct a 3D model.},
DOI = {10.3390/ijgi7100413}
}

@ARTICLE{8283638-14,
  author={Miranda, Fabio and Doraiswamy, Harish and Lage, Marcos and Wilson, Luc and Hsieh, Mondrian and Silva, Cláudio T.},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time}, 
  year={2019},
  volume={25},
  number={3},
  pages={1559-1574},
  doi={10.1109/TVCG.2018.2802945}}


@article{Yu2022,
  doi = {10.21105/joss.04021},
  url = {10.21105/joss.04021},
  year = {2022},
  publisher = {The Open Journal},
  volume = {7},
  number = {71},
  pages = {4021},
  author = {Qing Yu and Jian Yuan},
  title = {TransBigData: A Python package for transportation spatio-temporal big data processing, analysis and visualization},
  journal = {Journal of Open Source Software}
}

@Article{rs13163297,
AUTHOR = {Zhang, Ying and Roffey, Matthew and Leblanc, Sylvain G.},
TITLE = {A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information},
JOURNAL = {Remote Sensing},
VOLUME = {13},
YEAR = {2021},
NUMBER = {16},
ARTICLE-NUMBER = {3297},
URL = {https://www.mdpi.com/2072-4292/13/16/3297},
ISSN = {2072-4292},
ABSTRACT = {After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection.},
DOI = {10.3390/rs13163297}
}

@article{pysal2007,
  author={Sergio Rey and Luc Anselin},
  title={{PySAL: A Python Library of Spatial Analytical Methods}},
  doi = {10.1007/978-3-642-03647-7_11},
  journal={The Review of Regional Studies},
  year=2007,
  volume={37},
  number={1},
  pages={5-27},
  keywords={Open Source; Software; Spatial},
  url={https://rrs.scholasticahq.com/article/8285.pdf}
}


@software{kelsey_jordahl_2021_5573592,
  author       = {Kelsey Jordahl and
                  Joris Van den Bossche and
                  Martin Fleischmann and
                  James McBride and
                  Jacob Wasserman and
                  Adrian Garcia Badaracco and
                  Jeffrey Gerard and
                  Alan D. Snow and
                  Jeff Tratner and
                  Matthew Perry and
                  Carson Farmer and
                  Geir Arne Hjelle and
                  Micah Cochran and
                  Sean Gillies and
                  Lucas Culbertson and
                  Matt Bartos and
                  Brendan Ward and
                  Giacomo Caria and
                  Mike Taves and
                  Nick Eubank and
                  sangarshanan and
                  John Flavin and
                  Matt Richards and
                  Sergio Rey and
                  maxalbert and
                  Aleksey Bilogur and
                  Christopher Ren and
                  Dani Arribas-Bel and
                  Daniel Mesejo-León and
                  Leah Wasser},
  title        = {geopandas/geopandas: v0.10.2},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.10.2},
  doi          = {10.5281/zenodo.5573592},
  url          = {10.5281/zenodo.5573592}
}