Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and valida...Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.展开更多
Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven a...Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.展开更多
Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain da...Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to(1) compare the observed versus expected frequency(chi-square) of permit issuance before and after the EF5 2011 tornado;(2), determine if significant space-time clusters of permits existed using the SaTScan^(TM) cluster analysis program(version 9.7);and(3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event,and one(residential) showed significance for nine of the 10years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.展开更多
The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches.This paper describes a comprehen...The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches.This paper describes a comprehensive dataset consisting of indoor environmental conditions,Wi-Fi connected devices,energy consumption of end uses(i.e.,HVAC,lighting,plug loads and fans),HVAC operations,and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment.The five rooms include two different-sized lecture rooms,an office space for administrative staff,an office space for researchers,and a library space accessible to all students.A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes.This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling,building simulation and control,energy forecasting and various building analytics.展开更多
Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data ...Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data becomes imperative.There are a few publicly available products that provide a lot of building data,such as Microsoft and Open Street Map.However,in East Asia,due to the more complex distribution of buildings and the scarcity of auxiliary data,there is a lack of building data in these regions,hindering the large-scale application in East Asia.Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression.However,the reliance on inaccurate buildings data introduces cumulative errors,rendering this simulation data highly unreliable,leading to limitations in achieving precise research in East Asian region.Therefore,we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia,and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings.The evaluation shows the validity of our building product,with an average overall accuracy of 89.63%and an F1 score of 82.55%.In addition,a comparison with existing products further shows the high quality and completeness of our building data.Finally,we conduct spatial analysis of our building data,revealing its value in supporting urban-related research.The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.展开更多
OpenStreetMap(OSM)is an essential source for acquiring building data,although such data may suffer from quality issues.Many studies have focused on assessing OSM building data quality but few have been carried out on ...OpenStreetMap(OSM)is an essential source for acquiring building data,although such data may suffer from quality issues.Many studies have focused on assessing OSM building data quality but few have been carried out on a global scale.This study aims to assess OSM building completeness(a quality measure)for 12,975 cities across the globe.This was achieved by employing population grid data as a proxy for reference building data.Not only the completeness of each city but also that of the grids within that city was assessed.The assessment results were evaluated based on calculating the overall accuracy and the r-square value between estimated and reference OSM building completeness values.Results showed that for 75%of cities,the completeness is lower than 20%;no more than 9%of cities have an estimated completeness higher than 80%.The overall accuracies of most countries were higher than 80%.The estimated completeness was also highly correlated with the reference completeness,which verifies the effectiveness of our approach.These results may be useful for acquiring and updating building data in OSM.A global and open dataset related to OSM building completeness has been made available for public use.展开更多
This paper discusses a methodology to collect building inventory data by combining image processing techniques,field work or tools such as Google Street View and applying statistical inferences.Following the methodolo...This paper discusses a methodology to collect building inventory data by combining image processing techniques,field work or tools such as Google Street View and applying statistical inferences.Following the methodology outlined in Marinescu(2002),a family of Gabor filters are first constructed,which are then applied to an optical high-resolution image.The output from the processed image is segmented using Self-Organising Maps.This paper examines the relationship between the segmented areas in the image and the building type distribution within each segmented area,by deriving the distribution from field data.The relationship between the average number of buildings in these cells against the number of grid cells allocated to each segmentation cluster is also investigated.Finally,using these results,the overall building inventory distribution for the whole of the case study site of Pylos is presented.展开更多
With the rapid development of digital earth,smart city,and digital twin technology,the demands of three-dimensional model data’s application is getting higher and higher.These data tend to be multi-objectification,mu...With the rapid development of digital earth,smart city,and digital twin technology,the demands of three-dimensional model data’s application is getting higher and higher.These data tend to be multi-objectification,multi-type,multi-scale,complex spatial relationship,and large amount,which brings great challenges to the efficient organization of them.This paper mainly studies the organization of three-dimensional model data,and the main contributions are as follows:1)A integer coding method of three dimensional multi-scale grid is proposed,which can reduce the four-dimensional(spatial dimension and scale dimension)space into one-dimensional,and has better space and scale clustering characteristics by comparing with various types of grid coding.2)The binary algebra calculation method is proposed to realize the basic spatial relationship calculation of three-dimensional grid,which has higher spatial relationship computing ability than 3D-Geohash method;3)The multi-scale integer coding method is applied to the data organization of three-dimensional city model,and the experiment results show that:it is more efficient and stable than the threedimensional R-tree index and Geohash coding method in the establishment of index and the query of three dimensional space.展开更多
Simple navigation receivers can be used for positioning with sub-centimeter accuracy in a wireless sensor network if the read-out of the carrier phase(CP)data is possible and all data are permanently broadcast to a ce...Simple navigation receivers can be used for positioning with sub-centimeter accuracy in a wireless sensor network if the read-out of the carrier phase(CP)data is possible and all data are permanently broadcast to a central processing computer.At this base station an automated near real-time processing takes place and a precise differential GNSS-based positioning of the involved sensor nodes is computed.The paper describes the technical principles of such a system with its essential demands for the sensing,the communication,and the computing components.First experiences in a research project related to landslide monitoring are depicted.Of course the developed system can also be embedded for location finding in a widespread multifunctional geo sensor network.The quality of the obtained result is restricted due to the fact that the CP measurements must be recorded over a certain time span,usually a few minutes for every independent position solution.As far as possible a modular structure with commercial off-theshelf components,e.g.standard wireless local area network for communication,and in cooperation of existing proofed and powerful program tools is chosen.Open interfaces are used as far as possible.展开更多
Albert Einstein is credited with saying, “Insanity: doing the same thing over and over again and expecting different results.” Then in the 1980s, we watched as poor Bill Murray kept waking up to the very same day, v...Albert Einstein is credited with saying, “Insanity: doing the same thing over and over again and expecting different results.” Then in the 1980s, we watched as poor Bill Murray kept waking up to the very same day, very same activities, and same exact results in the comedy “Groundhog Day”. We challenge you to look at the process of design, delivery, and operations for our buildings. Are we all stuck in “insanity” or “Groundhog Day?” We keep doing things the same way and expecting different results. This article explores how the utilization of facility data from design to operations can not only break the paradigm of “insanity,” it can make our facilities more efficient and sustainable to construct and operate. Our basic premise is that if you do not have accurate “data” about your facility it will be very difficult, if not impossible, to operate your facility effectively.展开更多
文摘Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.
基金The authors gratefully acknowledge the support of this research by the Research Grant Council of Hong Kong SAR(152075/19E)the National Natural Science Foundation of China(No.51908365)the National Natural Science Foundation of China(No.51778321).
文摘Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.
文摘Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to(1) compare the observed versus expected frequency(chi-square) of permit issuance before and after the EF5 2011 tornado;(2), determine if significant space-time clusters of permits existed using the SaTScan^(TM) cluster analysis program(version 9.7);and(3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event,and one(residential) showed significance for nine of the 10years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.
文摘The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches.This paper describes a comprehensive dataset consisting of indoor environmental conditions,Wi-Fi connected devices,energy consumption of end uses(i.e.,HVAC,lighting,plug loads and fans),HVAC operations,and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment.The five rooms include two different-sized lecture rooms,an office space for administrative staff,an office space for researchers,and a library space accessible to all students.A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes.This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling,building simulation and control,energy forecasting and various building analytics.
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3903402National Natural Science Foundation of China under Grant 42222106+1 种基金National Natural Science Foun-dation of China under Grant 61976234Fundamental Research Funds for the Central Universities,Sun Yat-sen University under Grant 22lgqb12.
文摘Building,as an integral aspect of human life,is vital in the domains of urban management and urban analysis.To facilitate large-scale urban planning applications,the acquisition of complete and reliable building data becomes imperative.There are a few publicly available products that provide a lot of building data,such as Microsoft and Open Street Map.However,in East Asia,due to the more complex distribution of buildings and the scarcity of auxiliary data,there is a lack of building data in these regions,hindering the large-scale application in East Asia.Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression.However,the reliance on inaccurate buildings data introduces cumulative errors,rendering this simulation data highly unreliable,leading to limitations in achieving precise research in East Asian region.Therefore,we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia,and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings.The evaluation shows the validity of our building product,with an average overall accuracy of 89.63%and an F1 score of 82.55%.In addition,a comparison with existing products further shows the high quality and completeness of our building data.Finally,we conduct spatial analysis of our building data,revealing its value in supporting urban-related research.The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.
基金supported by National Natural Science Foundation of China(Grant No.41771428)the Director Fund of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022DF010).
文摘OpenStreetMap(OSM)is an essential source for acquiring building data,although such data may suffer from quality issues.Many studies have focused on assessing OSM building data quality but few have been carried out on a global scale.This study aims to assess OSM building completeness(a quality measure)for 12,975 cities across the globe.This was achieved by employing population grid data as a proxy for reference building data.Not only the completeness of each city but also that of the grids within that city was assessed.The assessment results were evaluated based on calculating the overall accuracy and the r-square value between estimated and reference OSM building completeness values.Results showed that for 75%of cities,the completeness is lower than 20%;no more than 9%of cities have an estimated completeness higher than 80%.The overall accuracies of most countries were higher than 80%.The estimated completeness was also highly correlated with the reference completeness,which verifies the effectiveness of our approach.These results may be useful for acquiring and updating building data in OSM.A global and open dataset related to OSM building completeness has been made available for public use.
文摘This paper discusses a methodology to collect building inventory data by combining image processing techniques,field work or tools such as Google Street View and applying statistical inferences.Following the methodology outlined in Marinescu(2002),a family of Gabor filters are first constructed,which are then applied to an optical high-resolution image.The output from the processed image is segmented using Self-Organising Maps.This paper examines the relationship between the segmented areas in the image and the building type distribution within each segmented area,by deriving the distribution from field data.The relationship between the average number of buildings in these cells against the number of grid cells allocated to each segmentation cluster is also investigated.Finally,using these results,the overall building inventory distribution for the whole of the case study site of Pylos is presented.
基金National Key R&D Program of China[Grant Number 2018YFB0505304]National Natural Science Foundation of China[Grant Number 41671409].
文摘With the rapid development of digital earth,smart city,and digital twin technology,the demands of three-dimensional model data’s application is getting higher and higher.These data tend to be multi-objectification,multi-type,multi-scale,complex spatial relationship,and large amount,which brings great challenges to the efficient organization of them.This paper mainly studies the organization of three-dimensional model data,and the main contributions are as follows:1)A integer coding method of three dimensional multi-scale grid is proposed,which can reduce the four-dimensional(spatial dimension and scale dimension)space into one-dimensional,and has better space and scale clustering characteristics by comparing with various types of grid coding.2)The binary algebra calculation method is proposed to realize the basic spatial relationship calculation of three-dimensional grid,which has higher spatial relationship computing ability than 3D-Geohash method;3)The multi-scale integer coding method is applied to the data organization of three-dimensional city model,and the experiment results show that:it is more efficient and stable than the threedimensional R-tree index and Geohash coding method in the establishment of index and the query of three dimensional space.
文摘Simple navigation receivers can be used for positioning with sub-centimeter accuracy in a wireless sensor network if the read-out of the carrier phase(CP)data is possible and all data are permanently broadcast to a central processing computer.At this base station an automated near real-time processing takes place and a precise differential GNSS-based positioning of the involved sensor nodes is computed.The paper describes the technical principles of such a system with its essential demands for the sensing,the communication,and the computing components.First experiences in a research project related to landslide monitoring are depicted.Of course the developed system can also be embedded for location finding in a widespread multifunctional geo sensor network.The quality of the obtained result is restricted due to the fact that the CP measurements must be recorded over a certain time span,usually a few minutes for every independent position solution.As far as possible a modular structure with commercial off-theshelf components,e.g.standard wireless local area network for communication,and in cooperation of existing proofed and powerful program tools is chosen.Open interfaces are used as far as possible.
文摘Albert Einstein is credited with saying, “Insanity: doing the same thing over and over again and expecting different results.” Then in the 1980s, we watched as poor Bill Murray kept waking up to the very same day, very same activities, and same exact results in the comedy “Groundhog Day”. We challenge you to look at the process of design, delivery, and operations for our buildings. Are we all stuck in “insanity” or “Groundhog Day?” We keep doing things the same way and expecting different results. This article explores how the utilization of facility data from design to operations can not only break the paradigm of “insanity,” it can make our facilities more efficient and sustainable to construct and operate. Our basic premise is that if you do not have accurate “data” about your facility it will be very difficult, if not impossible, to operate your facility effectively.
