Spatio-temporal data from earth observation systems and models are increasing at astronomical rates in the climate domain. This results in a massive dataset that is increasingly difficult to navigate to find interesting time periods where the spatial pattern of a process changes. The ability to navigate to such areas can lead to new knowledge about the factors that contribute to a spatio-temporal process. This paper proposes a method to automatically characterize multi-variate spatio-temporal datasets using basic image processing techniques and an efficient distance measure. The approach uses a measure of local image entropy combined with edge detection to find naturally occurring boundaries in the dataset. Then a distance measure is used to track the change in these boundaries over time. The resulting measure of spatio-temporal change can be used to explore spatio-temporal datasets to find new relationships between the spatial pattern of variables over time. Experiments were performed on a real-world climate dataset and the results were promising in that new patterns emerged and interesting relationships between variables were found.
{"title":"Exploring multivariate spatio-temporal change in climate data using image analysis techniques","authors":"M. P. McGuire, A. Gangopadhyay, V. Janeja","doi":"10.1145/2345316.2345333","DOIUrl":"https://doi.org/10.1145/2345316.2345333","url":null,"abstract":"Spatio-temporal data from earth observation systems and models are increasing at astronomical rates in the climate domain. This results in a massive dataset that is increasingly difficult to navigate to find interesting time periods where the spatial pattern of a process changes. The ability to navigate to such areas can lead to new knowledge about the factors that contribute to a spatio-temporal process. This paper proposes a method to automatically characterize multi-variate spatio-temporal datasets using basic image processing techniques and an efficient distance measure. The approach uses a measure of local image entropy combined with edge detection to find naturally occurring boundaries in the dataset. Then a distance measure is used to track the change in these boundaries over time. The resulting measure of spatio-temporal change can be used to explore spatio-temporal datasets to find new relationships between the spatial pattern of variables over time. Experiments were performed on a real-world climate dataset and the results were promising in that new patterns emerged and interesting relationships between variables were found.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While the existence of very large and scalable Database Management Systems (DBMSs) is well recognized, it is the usage and extension of these technologies to managing spatial data that has seen increasing amounts of research work in recent years. A focused area of this research work involves the handling of very high resolution Light Detection and Ranging (LiDAR) data. While LiDAR has many real world applications, it is usually the purview of organizations interested in capturing and monitoring our environment where it has become pervasive. In many of these cases, it has now become the de facto minimum standard expected when a need to acquire very detailed 3D spatial data is required. However, significant challenges exist when working with these data sources, from data storage to feature extraction through to data segmentation all presenting challenges relating to the very large volumes of data that exist. In this paper, we present the complete LiDAR data pipeline as managed in our spatial database framework. This involves three distinct sections, populating the database, building a spatial hierarchy that describes the available data sources, and spatially segmenting data based on user requirements which generates a visualization of these data in a WebGL enabled web-application viewer. All work presented is in an experimental results context where we show how this approach is runtime efficient given the very large volumes of LiDAR data that are being managed.
{"title":"LiDAR data management pipeline; from spatial database population to web-application visualization","authors":"P. Lewis, C. McElhinney, T. McCarthy","doi":"10.1145/2345316.2345336","DOIUrl":"https://doi.org/10.1145/2345316.2345336","url":null,"abstract":"While the existence of very large and scalable Database Management Systems (DBMSs) is well recognized, it is the usage and extension of these technologies to managing spatial data that has seen increasing amounts of research work in recent years. A focused area of this research work involves the handling of very high resolution Light Detection and Ranging (LiDAR) data. While LiDAR has many real world applications, it is usually the purview of organizations interested in capturing and monitoring our environment where it has become pervasive. In many of these cases, it has now become the de facto minimum standard expected when a need to acquire very detailed 3D spatial data is required. However, significant challenges exist when working with these data sources, from data storage to feature extraction through to data segmentation all presenting challenges relating to the very large volumes of data that exist. In this paper, we present the complete LiDAR data pipeline as managed in our spatial database framework. This involves three distinct sections, populating the database, building a spatial hierarchy that describes the available data sources, and spatially segmenting data based on user requirements which generates a visualization of these data in a WebGL enabled web-application viewer. All work presented is in an experimental results context where we show how this approach is runtime efficient given the very large volumes of LiDAR data that are being managed.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuma Matsui, Aaron Gidding, T. Levy, F. Kuester, T. DeFanti
Modern field science, including archaeology, utilizes a massive amount of digital data captured by state-of-the-art measurement instruments. Large archaeological data sets may include images, geospatial data, analytical data, and metadata. Geospatial information plays a central role in the life cycle of those data; information is collected, organized, and published for analyses and visualization as final output using geospatial data as an index. The web is an ideal place to publish scientific data and promote diverse collaboration, and thus we need a system to publish digital archaeological data efficiently so that it is also integrated in our data management workflow. In order to realize this goal, we designed and implemented a web-based application named ArcheoSTOR Map, which visualizes and publishes raw archaeological data onto a map.
{"title":"ArchaeoSTOR map: publishing archaeological geodata on the web","authors":"Yuma Matsui, Aaron Gidding, T. Levy, F. Kuester, T. DeFanti","doi":"10.1145/2345316.2345355","DOIUrl":"https://doi.org/10.1145/2345316.2345355","url":null,"abstract":"Modern field science, including archaeology, utilizes a massive amount of digital data captured by state-of-the-art measurement instruments. Large archaeological data sets may include images, geospatial data, analytical data, and metadata. Geospatial information plays a central role in the life cycle of those data; information is collected, organized, and published for analyses and visualization as final output using geospatial data as an index. The web is an ideal place to publish scientific data and promote diverse collaboration, and thus we need a system to publish digital archaeological data efficiently so that it is also integrated in our data management workflow. In order to realize this goal, we designed and implemented a web-based application named ArcheoSTOR Map, which visualizes and publishes raw archaeological data onto a map.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129118158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blaine Hoffman, Harold R. Robinson, Keith Han, John Millar Carroll
In this paper, we introduce the design of a location-sensitive calendar as part of an ongoing community portal project. CiVicinity's Events page supports the aggregation and presentation of activities and events throughout the community in one centralized location. The integration of location-aware features, including map visuals and distances based on a user's current location, enhances the locality of the online calendar. We support the design rationale of this calendar through a brief user evaluation study focusing on the benefits and additions of the location-sensitive features.
{"title":"CiVicinity events: pairing geolocation tools with a community calendar","authors":"Blaine Hoffman, Harold R. Robinson, Keith Han, John Millar Carroll","doi":"10.1145/2345316.2345334","DOIUrl":"https://doi.org/10.1145/2345316.2345334","url":null,"abstract":"In this paper, we introduce the design of a location-sensitive calendar as part of an ongoing community portal project. CiVicinity's Events page supports the aggregation and presentation of activities and events throughout the community in one centralized location. The integration of location-aware features, including map visuals and distances based on a user's current location, enhances the locality of the online calendar. We support the design rationale of this calendar through a brief user evaluation study focusing on the benefits and additions of the location-sensitive features.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131921764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Big Data refers to the rising flood of digital data from many different sources, including the sensors, digitizers, scanners, mobile phones, cameras, software-based tools, internet, and so on. "Big" and "diverse" are two important characteristics of Big Data. The diversity of the Big Data, such as text, geometry, image, video, or sound, also increases difficulties of big data processing. Coping with the "Big Data" problems requires a radical change in the philosophy of the organization of information processing. Primarily, the Big Data approach has to modify the underlying computational model in order to manage the uncertainty in the access to information items in a huge nebulous environment. As a result, the produced outcomes are directly influenced only by some active part of all information items, while the rest of the available information items just indirectly affect the choice of the active part. An analogous functionality exhibits the organization of the brain featuring the unconsciousness, and a characteristic similarity shows the retrieval process in Google. In this talk, we introduce a novel method for on-the-fly clusterization of amorphous data from diverse sources. The devised construction is based on the previously developed FuzzyFind Dictionary reversing the error-correction scheme of Golay Code. This clusterization involves processing of intensive continuous data streams that can be effectively implemented using multi-core pipelining with forced interrupts. The suggested clusterization is especially suitable for the Big Data computational model as it materializes the requirement of purposeful selection of information items in unsteady framework of cloud computing and stream processing. Furthermore, the uncertainties in relation to the considered method of clusterization are moderated due to the idea of the bounded rationality, an approach that does not require a complete exact knowledge for sensible decision-making.
{"title":"On clusterization of \"big data\" streams","authors":"S. Berkovich, Duoduo Liao","doi":"10.1145/2345316.2345320","DOIUrl":"https://doi.org/10.1145/2345316.2345320","url":null,"abstract":"Big Data refers to the rising flood of digital data from many different sources, including the sensors, digitizers, scanners, mobile phones, cameras, software-based tools, internet, and so on. \"Big\" and \"diverse\" are two important characteristics of Big Data. The diversity of the Big Data, such as text, geometry, image, video, or sound, also increases difficulties of big data processing.\u0000 Coping with the \"Big Data\" problems requires a radical change in the philosophy of the organization of information processing. Primarily, the Big Data approach has to modify the underlying computational model in order to manage the uncertainty in the access to information items in a huge nebulous environment. As a result, the produced outcomes are directly influenced only by some active part of all information items, while the rest of the available information items just indirectly affect the choice of the active part. An analogous functionality exhibits the organization of the brain featuring the unconsciousness, and a characteristic similarity shows the retrieval process in Google.\u0000 In this talk, we introduce a novel method for on-the-fly clusterization of amorphous data from diverse sources. The devised construction is based on the previously developed FuzzyFind Dictionary reversing the error-correction scheme of Golay Code. This clusterization involves processing of intensive continuous data streams that can be effectively implemented using multi-core pipelining with forced interrupts. The suggested clusterization is especially suitable for the Big Data computational model as it materializes the requirement of purposeful selection of information items in unsteady framework of cloud computing and stream processing. Furthermore, the uncertainties in relation to the considered method of clusterization are moderated due to the idea of the bounded rationality, an approach that does not require a complete exact knowledge for sensible decision-making.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited understanding of the capabilities of these systems means that defining point density in project specifications is a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the capabilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings -all important features in asset management surveys. This paper will introduce the current version of the MobIle Mapping point densIty Calculator (MIMIC), MIMIC's visualisation module and finally discuss the methods employed to validate our work.
{"title":"MIMIC: Mobile mapping point density calculator","authors":"C. Cahalane, T. McCarthy, C. McElhinney","doi":"10.1145/2345316.2345335","DOIUrl":"https://doi.org/10.1145/2345316.2345335","url":null,"abstract":"The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited understanding of the capabilities of these systems means that defining point density in project specifications is a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the capabilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings -all important features in asset management surveys. This paper will introduce the current version of the MobIle Mapping point densIty Calculator (MIMIC), MIMIC's visualisation module and finally discuss the methods employed to validate our work.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114523267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the k-clustering query problem on road networks, an important problem in Geographic Information Systems ("GIS"). Using previously developed Euclidean embeddings and reduction to fast nearest neighbor search, we show and analyze approximation algorithms for these problems. Since these problems are difficult to solve exactly --- and even hard to approximate for most variants --- we compare our constant factor approximation algorithms to exact answers on small synthetic datasets and on a dataset representing Tallahassee, Florida, a small city. We have implemented a web application that demonstrates our method for road networks in the same small city.
{"title":"Fast k-clustering queries on embeddings of road networks","authors":"James McClain, Piyush Kumar","doi":"10.1145/2345316.2345331","DOIUrl":"https://doi.org/10.1145/2345316.2345331","url":null,"abstract":"In this paper, we study the k-clustering query problem on road networks, an important problem in Geographic Information Systems (\"GIS\"). Using previously developed Euclidean embeddings and reduction to fast nearest neighbor search, we show and analyze approximation algorithms for these problems. Since these problems are difficult to solve exactly --- and even hard to approximate for most variants --- we compare our constant factor approximation algorithms to exact answers on small synthetic datasets and on a dataset representing Tallahassee, Florida, a small city. We have implemented a web application that demonstrates our method for road networks in the same small city.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"14 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We have created an interactive map that can smoothly zoom to any region. The core of our system utilizes wavelets to achieve this effect. The system is implemented to view hydrographic flowline data, such as in the USGS National Hydrography Dataset (NHD). The map demonstrates that a wavelet-based approach is well suited for basic generalization operations. It provides smoothing and pruning that is continuously dependent on map scale. The method is applied to the Vermont river network, with the goal of creating an interactive map visualization. The process involves removing cycles from the network, prioritizing the segments according to their Strahler numbers, and extracting tributaries. Then each tributary is decomposed into wavelet details. When the user requests a map of a region B, the window size infers the scale s. Functions ε(s) and σ(s) determine the accuracy and the pruning level. The tributaries that are visible in B are synthesized to the required accuracy ε(s) and displayed according to the pruning function σ(s). In our system, the pruning is designed to be continuous with respect to the scale. Our implementation shows that the interactive map renders views in subsecond time. We have determined experimentally that the FBI (9--7) biorthogonal wavelet family provides the best compromise between quality of approximation and computation time.
{"title":"Wavelet-based automated river network generalization","authors":"M. Gutman, C. Weaver","doi":"10.1145/2345316.2345332","DOIUrl":"https://doi.org/10.1145/2345316.2345332","url":null,"abstract":"We have created an interactive map that can smoothly zoom to any region. The core of our system utilizes wavelets to achieve this effect. The system is implemented to view hydrographic flowline data, such as in the USGS National Hydrography Dataset (NHD). The map demonstrates that a wavelet-based approach is well suited for basic generalization operations. It provides smoothing and pruning that is continuously dependent on map scale.\u0000 The method is applied to the Vermont river network, with the goal of creating an interactive map visualization. The process involves removing cycles from the network, prioritizing the segments according to their Strahler numbers, and extracting tributaries. Then each tributary is decomposed into wavelet details.\u0000 When the user requests a map of a region B, the window size infers the scale s. Functions ε(s) and σ(s) determine the accuracy and the pruning level. The tributaries that are visible in B are synthesized to the required accuracy ε(s) and displayed according to the pruning function σ(s). In our system, the pruning is designed to be continuous with respect to the scale.\u0000 Our implementation shows that the interactive map renders views in subsecond time. We have determined experimentally that the FBI (9--7) biorthogonal wavelet family provides the best compromise between quality of approximation and computation time.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BAE Systems is pursuing research in real-time 3-D mapping technology that can be used to navigate an unmanned autonomous vehicle (UAV). Geospatial technology, such as digital photogrammetry and GIS, offers advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. We believe the next breakthrough may be automatically identifying 3-D objects. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation, and visualization applications, it has the potential to be valuable in robotics and UAV applications.
{"title":"Real-time 3-D mapping for robotic applications","authors":"William Smith, Bingcai Zhang","doi":"10.1145/2345316.2345353","DOIUrl":"https://doi.org/10.1145/2345316.2345353","url":null,"abstract":"BAE Systems is pursuing research in real-time 3-D mapping technology that can be used to navigate an unmanned autonomous vehicle (UAV). Geospatial technology, such as digital photogrammetry and GIS, offers advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. We believe the next breakthrough may be automatically identifying 3-D objects. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation, and visualization applications, it has the potential to be valuable in robotics and UAV applications.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122126655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this video, we showcase two atmospheric dispersion simulations in an Oklahoma City dataset and a New York City (NYC) dataset. These simulations are created using a robust and efficient framework that generates seamless 3D architectural models from overlapping 2D footprints. These footprints with elevation and height information are commonly used to depict various components of buildings in GIS software such as ESRI ArcGIS and urban model synthesis methods, and usually contain small, sharp, and various (nearly) degenerate artifacts due to machine and human errors. In the first part of the video showing a simulation in Oklahoma City, the location is south of the public library in an area where there is a building currently. Two iso-surfaces of 10-4 and 10-5 ppm are shown in green and the brown clouds. The inflow is a westerly wind with a wind speed of 5 m/s at 10 meters above ground level. In the second part of the video showing a simulation in NYC, the location is the Financial District, Manhattan. The simulation assumed a boundary condition for the inflow of a logarithmic profile of 2 m/s with a velocity at 10 meters from the ground. An iso-surface of 10-5 ppm is shown. The final volume mesh produce contains 333 million tetrahedra, and 59 million points. The total time of the NYC simulation, including the initialization time and dispersion, took approximately two days on a high performance computing system running 2048 cores in a CRAY XK6 nodes. In both simulations, the release is continuous.
{"title":"Transport and dispersion simulation in downtown Oklahoma City and New York City","authors":"F. Camelli, Jyh-Ming Lien, David W. S. Wong","doi":"10.1145/2345316.2345366","DOIUrl":"https://doi.org/10.1145/2345316.2345366","url":null,"abstract":"In this video, we showcase two atmospheric dispersion simulations in an Oklahoma City dataset and a New York City (NYC) dataset. These simulations are created using a robust and efficient framework that generates seamless 3D architectural models from overlapping 2D footprints. These footprints with elevation and height information are commonly used to depict various components of buildings in GIS software such as ESRI ArcGIS and urban model synthesis methods, and usually contain small, sharp, and various (nearly) degenerate artifacts due to machine and human errors. In the first part of the video showing a simulation in Oklahoma City, the location is south of the public library in an area where there is a building currently. Two iso-surfaces of 10-4 and 10-5 ppm are shown in green and the brown clouds. The inflow is a westerly wind with a wind speed of 5 m/s at 10 meters above ground level. In the second part of the video showing a simulation in NYC, the location is the Financial District, Manhattan. The simulation assumed a boundary condition for the inflow of a logarithmic profile of 2 m/s with a velocity at 10 meters from the ground. An iso-surface of 10-5 ppm is shown. The final volume mesh produce contains 333 million tetrahedra, and 59 million points. The total time of the NYC simulation, including the initialization time and dispersion, took approximately two days on a high performance computing system running 2048 cores in a CRAY XK6 nodes. In both simulations, the release is continuous.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127405498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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