With the increasing pace of change in computing technology, islands of relative stability become important to reaping the benefits of geospatial information. Geospatial standards are bases for persistent developments in the complex adaptive ecosystem of geospatial computing technology. Standards are the backbone of the Geoweb and will be also for the Internet of Things (IoT). At COM. Geo 2011, the workshop, "Expanding Geoweb to An Internet of Things", explored ways in which the success of the Geoweb were a basis for the emerging Internet of Things. COM. Geo 2012 aims to continue this discussion of sensor and mobile computing for geospatial research and application. IoT can be seen as a fuller expression of a vision of The Computer for the 21st Century (M. Weiser, 1991, Sci. Amer.). That vision of "Ubiquitous Computing" anticipated computers disappearing into the fabric of everyday life. What perhaps could not have been anticipated was how computing would be changed by the WWW making information ubiquitously accessible via the internet. Now, everyday objects with embedded computers are becoming ubiquitously accessible and interactive via the internet and mobile communications to the benefit of researchers, decision-makers, developers, and application users. Sensor webs and RFID are major elements of IoT. Beginning in 2000, the Open Geospatial Consortium (OGC) anticipated the proliferation of network-accessible sensors and defined a set of Sensor Web Enablement (SWE) standards. SWE allows sensors to be used in user applications not anticipated with the initial deployment of the sensors. The AutoID lab is a pioneer identifying how RFID systems and SWE can work together to for understanding real world objects both from physical measurements and identity. Geospatial location is fundamental to IoT with the spaces in which IoT operates going beyond the geographic positioning technologies currently on mobile devices. Fusion of information from new sensors on-board mobile devices will enable positioning indoors and other locations where GPS is not present. "Indoor maps" with the complexity of 3 dimensions and complex route topology are needed for IoT be placed and used in a rich spatial computing context. End user applications will reap the benefits of ubiquitous information from IoT. Augmented Reality applications will allow users to view a rich set of information about the space around them both historical information and real-time information. The many domains of Business Intelligence will be informed by this stream of information enabling better decisions. OGC brings several innovative, yet stable standards to the computing and geospatial world of IoT. The second generation of SWE standards is currently being finalized. CityGML and IndoorGML meet the need for indoor maps. And the Augmented Reality Markup Language is poised to bring IoT information into a context aware visualization on mobile devices. OGC will continue to work with other standard
{"title":"Connecting islands in the internet of things","authors":"G. Percivall","doi":"10.1145/2345316.2345321","DOIUrl":"https://doi.org/10.1145/2345316.2345321","url":null,"abstract":"With the increasing pace of change in computing technology, islands of relative stability become important to reaping the benefits of geospatial information. Geospatial standards are bases for persistent developments in the complex adaptive ecosystem of geospatial computing technology. Standards are the backbone of the Geoweb and will be also for the Internet of Things (IoT).\u0000 At COM. Geo 2011, the workshop, \"Expanding Geoweb to An Internet of Things\", explored ways in which the success of the Geoweb were a basis for the emerging Internet of Things. COM. Geo 2012 aims to continue this discussion of sensor and mobile computing for geospatial research and application.\u0000 IoT can be seen as a fuller expression of a vision of The Computer for the 21st Century (M. Weiser, 1991, Sci. Amer.). That vision of \"Ubiquitous Computing\" anticipated computers disappearing into the fabric of everyday life. What perhaps could not have been anticipated was how computing would be changed by the WWW making information ubiquitously accessible via the internet. Now, everyday objects with embedded computers are becoming ubiquitously accessible and interactive via the internet and mobile communications to the benefit of researchers, decision-makers, developers, and application users.\u0000 Sensor webs and RFID are major elements of IoT. Beginning in 2000, the Open Geospatial Consortium (OGC) anticipated the proliferation of network-accessible sensors and defined a set of Sensor Web Enablement (SWE) standards. SWE allows sensors to be used in user applications not anticipated with the initial deployment of the sensors. The AutoID lab is a pioneer identifying how RFID systems and SWE can work together to for understanding real world objects both from physical measurements and identity.\u0000 Geospatial location is fundamental to IoT with the spaces in which IoT operates going beyond the geographic positioning technologies currently on mobile devices. Fusion of information from new sensors on-board mobile devices will enable positioning indoors and other locations where GPS is not present. \"Indoor maps\" with the complexity of 3 dimensions and complex route topology are needed for IoT be placed and used in a rich spatial computing context.\u0000 End user applications will reap the benefits of ubiquitous information from IoT. Augmented Reality applications will allow users to view a rich set of information about the space around them both historical information and real-time information. The many domains of Business Intelligence will be informed by this stream of information enabling better decisions.\u0000 OGC brings several innovative, yet stable standards to the computing and geospatial world of IoT. The second generation of SWE standards is currently being finalized. CityGML and IndoorGML meet the need for indoor maps. And the Augmented Reality Markup Language is poised to bring IoT information into a context aware visualization on mobile devices. OGC will continue to work with other standard","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"78 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":"121135472","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}
With more and more live sensors being added to geospatial applications, huge amount of sensor data are generated and saved in spatial database. Managing and mining these large-scale ever-changing data becomes new challenges for geospatial studies. In this paper, we present an application-oriented case study to show how to retrieve target tracking data from big dataset saved in spatial database. Our video event retrieval system collects thirty days (8790 GB) high definition video data from six surveillance cameras, analyze them and extract roughly ten million video target tracks. These tracks are projected onto world coordinates and pumped into a spatial database. The system performance of inserting and retrieving these tracks is analyzed in terms of spatial data type design, spatial index configuration, online operation capacity, query optimization and scalability handling. Our insights of saving, managing and retrieving target tracks in a large-scale are presented.
{"title":"Retrieving large-scale high density video target tracks from spatial database","authors":"Hongli Deng, Kiran Gunda, Z. Rasheed, N. Haering","doi":"10.1145/2345316.2345339","DOIUrl":"https://doi.org/10.1145/2345316.2345339","url":null,"abstract":"With more and more live sensors being added to geospatial applications, huge amount of sensor data are generated and saved in spatial database. Managing and mining these large-scale ever-changing data becomes new challenges for geospatial studies. In this paper, we present an application-oriented case study to show how to retrieve target tracking data from big dataset saved in spatial database. Our video event retrieval system collects thirty days (8790 GB) high definition video data from six surveillance cameras, analyze them and extract roughly ten million video target tracks. These tracks are projected onto world coordinates and pumped into a spatial database. The system performance of inserting and retrieving these tracks is analyzed in terms of spatial data type design, spatial index configuration, online operation capacity, query optimization and scalability handling. Our insights of saving, managing and retrieving target tracks in a large-scale are presented.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"21 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":"123720310","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}
Jyh-Ming Lien, Yanyan Lu, F. Camelli, David W. S. Wong
A large volume of urban models describing urban objects in major international cities has been re-constructed and become freely and publicly available via software like Arc-Globe and Google Earth. However, these models are mostly created for visualization and are loosely structured. For example, current GIS software such as ESRI ArcGIS and urban model synthesis methods typically use overlapping 2D footprints with elevation and height information to depict various components of buildings. In this paper, we present a robust and efficient framework that generates seamless 3D architectural models from these footprints that usually contain small, sharp, and various (nearly) degenerate artifacts due to machine and human errors. We demonstrate the benefits of the proposed method by showcase an atmospheric dispersion simulation in a New York City (NYC) dataset. Finally, we discuss several examples of visualizing and analyzing the simulated Computational Fluid Dynamics (CFD) data into the GIS for further geospatial analysis.
{"title":"City-scale urban transport and dispersion simulation using geographic information system footprints","authors":"Jyh-Ming Lien, Yanyan Lu, F. Camelli, David W. S. Wong","doi":"10.1145/2345316.2345340","DOIUrl":"https://doi.org/10.1145/2345316.2345340","url":null,"abstract":"A large volume of urban models describing urban objects in major international cities has been re-constructed and become freely and publicly available via software like Arc-Globe and Google Earth. However, these models are mostly created for visualization and are loosely structured. For example, current GIS software such as ESRI ArcGIS and urban model synthesis methods typically use overlapping 2D footprints with elevation and height information to depict various components of buildings. In this paper, we present a robust and efficient framework that generates seamless 3D architectural models from these footprints that usually contain small, sharp, and various (nearly) degenerate artifacts due to machine and human errors. We demonstrate the benefits of the proposed method by showcase an atmospheric dispersion simulation in a New York City (NYC) dataset. Finally, we discuss several examples of visualizing and analyzing the simulated Computational Fluid Dynamics (CFD) data into the GIS for further geospatial analysis.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"36 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":"129793334","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}
What happens when you have connected sensors in everyone's pockets, homes, vehicles, workspaces, street corners, shopping areas, and more? With the convergence of Mobile Computing, the Internet of Things (IoT), and the ability to gather and analyze this Big Data, the availability of massive amounts of information will continue to be gathered and you can expect the unexpected to happen. The themes of this panel are driving development in information technology, but what is the intersection with geospatial? Location determination and use of location for context are core capabilities of Mobile and IoT. Knowing your location along with nearby Points of Interest (PoIs) and Indoor maps provide a new level of spatial awareness and decision making. This information will be used and viewed in new ways including Augmented Reality (AR). Social computing with geospatial checkins provides a rich picture of the social environment. With embedded computing becoming even more ubiquitous, Sensor Webs will provide opportunistic sensing of the physical environment. Geospatial filtering is one of the most effective methods to extracting information from these big data streams. These streams will continue to grow, e.g., mobile 3D video at incredibly high resolution. Data Fusion to combine multiple data sources will create new capabilities many based on geospatial processing. How can we realize the full potential of these technological capabilities in regards to geospatial? We can envision a lot of upside with the technology, but at what cost to privacy and rights? How should policy, privacy and rights be included in the conversations and deployments of these technologies and the resultant data? What role will ambient and participatory crowdsourcing play? A goal of our technology development must be to reduce the apparent tradeoff between surveillance for public safety vs. interests and rights of people. Technology development will continue to be a social activity based on geospatial APIs and standards for mobile platforms from organizations like W3C, OGC, IETF, and OMA. Development of these technologies are a basis for the critical outcomes, e.g, in creating Smart Cities including Smart Energy. Crowdsourcing from mobile platforms and M2M-based sensors webs will provide a basis for humanity to better understand our world and make critical decisions about the livability of our future..
{"title":"Realizing the geospatial potential of mobile, IoT and big data","authors":"G. Percivall","doi":"10.1145/2345316.2345327","DOIUrl":"https://doi.org/10.1145/2345316.2345327","url":null,"abstract":"What happens when you have connected sensors in everyone's pockets, homes, vehicles, workspaces, street corners, shopping areas, and more? With the convergence of Mobile Computing, the Internet of Things (IoT), and the ability to gather and analyze this Big Data, the availability of massive amounts of information will continue to be gathered and you can expect the unexpected to happen.\u0000 The themes of this panel are driving development in information technology, but what is the intersection with geospatial? Location determination and use of location for context are core capabilities of Mobile and IoT. Knowing your location along with nearby Points of Interest (PoIs) and Indoor maps provide a new level of spatial awareness and decision making. This information will be used and viewed in new ways including Augmented Reality (AR). Social computing with geospatial checkins provides a rich picture of the social environment. With embedded computing becoming even more ubiquitous, Sensor Webs will provide opportunistic sensing of the physical environment. Geospatial filtering is one of the most effective methods to extracting information from these big data streams. These streams will continue to grow, e.g., mobile 3D video at incredibly high resolution. Data Fusion to combine multiple data sources will create new capabilities many based on geospatial processing.\u0000 How can we realize the full potential of these technological capabilities in regards to geospatial? We can envision a lot of upside with the technology, but at what cost to privacy and rights? How should policy, privacy and rights be included in the conversations and deployments of these technologies and the resultant data? What role will ambient and participatory crowdsourcing play? A goal of our technology development must be to reduce the apparent tradeoff between surveillance for public safety vs. interests and rights of people. Technology development will continue to be a social activity based on geospatial APIs and standards for mobile platforms from organizations like W3C, OGC, IETF, and OMA. Development of these technologies are a basis for the critical outcomes, e.g, in creating Smart Cities including Smart Energy. Crowdsourcing from mobile platforms and M2M-based sensors webs will provide a basis for humanity to better understand our world and make critical decisions about the livability of our future..","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":"130291355","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}
A. Croitoru, A. Stefanidis, Jacek R. Radzikowski, A. Crooks, J. Stahl, N. Wayant
Social media contributions are manifestations of humans acting as sensors, participating in activities, reacting to events, and reporting issues that are considered important. Harvesting this information offers a unique opportunity to monitor the human landscape, and gain unparalleled situational awareness, especially as it relates to sociocultural dynamics. However, this requires the emergence of a novel GeoSocial analysis paradigm. Towards this goal, in this paper we present a framework for collaborative GeoSocial analysis, which is designed around data harvesting from social media feeds (starting with twitter and flickr) and the concept of a collaborative GeoSocial Analysis Workbench (G-SAW). We present key concepts of this framework, and early test implementation results in order to demonstrate the potential of the G-SAW framework for enhanced situational awareness.
{"title":"Towards a collaborative geosocial analysis workbench","authors":"A. Croitoru, A. Stefanidis, Jacek R. Radzikowski, A. Crooks, J. Stahl, N. Wayant","doi":"10.1145/2345316.2345338","DOIUrl":"https://doi.org/10.1145/2345316.2345338","url":null,"abstract":"Social media contributions are manifestations of humans acting as sensors, participating in activities, reacting to events, and reporting issues that are considered important. Harvesting this information offers a unique opportunity to monitor the human landscape, and gain unparalleled situational awareness, especially as it relates to sociocultural dynamics. However, this requires the emergence of a novel GeoSocial analysis paradigm. Towards this goal, in this paper we present a framework for collaborative GeoSocial analysis, which is designed around data harvesting from social media feeds (starting with twitter and flickr) and the concept of a collaborative GeoSocial Analysis Workbench (G-SAW). We present key concepts of this framework, and early test implementation results in order to demonstrate the potential of the G-SAW framework for enhanced situational awareness.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"59 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":"133867182","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}
Airports GIS is a web portal which hosts a few application modules. It allows authorized Airports GIS users to submit changes to airport data. One module in this portal is electronic Airport Layout Plan (eALP). This application when deployed will help create digital Airport Layout Plans. The layout view capability in this module is provided by ESRI's ArcGIS server. We are using GIS data available for a few airports which were included in our pilot program study for electronic ALP development. We expect this application will go to production sometimes next year. It will take several years before most or all airports in USA have their digital Airport Layout Plans. During this transition period we would store legacy Airport Layout Plans in Cloud. These Layout Plans are basically in pdf form. Plan to catalogue these Layout Plans and provide access to users is currently being discussed. As part of Airports GIS survey module requirement we get airport imagery for every submitted airport to Airports GIS. We plan to archive these orthoimages in Cloud and then give the ability to users to take advantage of ESRI's ArcGIS to analyze and manage these imageries. These orthoimages are large in size. In future we will receive many orthoimages because the number of airports who submit this data is growing and the size of each orthoimage is also growing. Hence it needs special care to organize and access these imageries. Once completed this will have siginificant impact on airport planning and budgeting.
{"title":"Cloud to host legacy airport layout plans and orthoimages","authors":"S. Parhi","doi":"10.1145/2345316.2345367","DOIUrl":"https://doi.org/10.1145/2345316.2345367","url":null,"abstract":"Airports GIS is a web portal which hosts a few application modules. It allows authorized Airports GIS users to submit changes to airport data. One module in this portal is electronic Airport Layout Plan (eALP). This application when deployed will help create digital Airport Layout Plans. The layout view capability in this module is provided by ESRI's ArcGIS server. We are using GIS data available for a few airports which were included in our pilot program study for electronic ALP development. We expect this application will go to production sometimes next year. It will take several years before most or all airports in USA have their digital Airport Layout Plans. During this transition period we would store legacy Airport Layout Plans in Cloud. These Layout Plans are basically in pdf form. Plan to catalogue these Layout Plans and provide access to users is currently being discussed.\u0000 As part of Airports GIS survey module requirement we get airport imagery for every submitted airport to Airports GIS. We plan to archive these orthoimages in Cloud and then give the ability to users to take advantage of ESRI's ArcGIS to analyze and manage these imageries. These orthoimages are large in size. In future we will receive many orthoimages because the number of airports who submit this data is growing and the size of each orthoimage is also growing. Hence it needs special care to organize and access these imageries. Once completed this will have siginificant impact on airport planning and budgeting.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"41 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":"115339696","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}
West Nile virus (WNV) is one of the most geographically widespread arboviruses in the world with cases occurring on all continents except Antarctica. The goal of study is to understand a real-time spatial temporal WNV activity using Twitter data. In our study, we collected tweets for the entire world using Twitter Search API with tags #WestNileVirus, and #WNV from August 31, 2011. Collected tweets were stored, cleaned, and geocoded. The Google API was used to display information on the web. The changes per week showed that the numbers were relatively high from August through October then gradually slowed down from December through March. We also found a very large increase in tweet numbers from March and April. This may be due to unusual higher temperature and mosquito activities in March and April this year compared to previous years.
{"title":"Real-time spatio-temporal analysis of West Nile virus using Twitter data","authors":"R. Sugumaran, Jonathan Voss","doi":"10.1145/2345316.2345361","DOIUrl":"https://doi.org/10.1145/2345316.2345361","url":null,"abstract":"West Nile virus (WNV) is one of the most geographically widespread arboviruses in the world with cases occurring on all continents except Antarctica. The goal of study is to understand a real-time spatial temporal WNV activity using Twitter data. In our study, we collected tweets for the entire world using Twitter Search API with tags #WestNileVirus, and #WNV from August 31, 2011. Collected tweets were stored, cleaned, and geocoded. The Google API was used to display information on the web. The changes per week showed that the numbers were relatively high from August through October then gradually slowed down from December through March. We also found a very large increase in tweet numbers from March and April. This may be due to unusual higher temperature and mosquito activities in March and April this year compared to previous years.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"12 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":"122922431","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 ever growing necessity for Big Data processing within the industry, government, and specially within defense applications causes the need and requirement for the fast development of new technologies. In addition, the protection of Big Data can be a serious problem because security is commonly an afterthought during technology development, and the exponentially increasing rate at which new data is generated presents many challenges. Although conventional Turing computation has been remarkably successful, it does not scale well and is failing to adapt to novel application domains in cyberspace. Fortunately, Turing formalism for computation represents only a subset of all possible computational possibilities. Unconventional computing - the quest for new algorithms and physical implementations of novel computing paradigms based on and inspired by principles of information processing in physical and biological systems - may help to solve some of the information overflow problems facing the Defense community. These and other topics will be covered by our diverse panel of experts.
{"title":"Cloud/big data computing for defense","authors":"R. Pino","doi":"10.1145/2345316.2345329","DOIUrl":"https://doi.org/10.1145/2345316.2345329","url":null,"abstract":"The ever growing necessity for Big Data processing within the industry, government, and specially within defense applications causes the need and requirement for the fast development of new technologies. In addition, the protection of Big Data can be a serious problem because security is commonly an afterthought during technology development, and the exponentially increasing rate at which new data is generated presents many challenges. Although conventional Turing computation has been remarkably successful, it does not scale well and is failing to adapt to novel application domains in cyberspace. Fortunately, Turing formalism for computation represents only a subset of all possible computational possibilities. Unconventional computing - the quest for new algorithms and physical implementations of novel computing paradigms based on and inspired by principles of information processing in physical and biological systems - may help to solve some of the information overflow problems facing the Defense community. These and other topics will be covered by our diverse panel of experts.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"307 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":"129750430","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}
Maxwell Love, M. Greene, Hannah Burgess, Matthew Luehrmann, Scott Mead, Thomas A. Babbitt
Radio direction finding is used in many search and rescue applications. The ability to accurately reduce a search area helps focus limited resources properly, which increases the probability of a search and rescue operations success. This paper examines the application of vector geometry, radio line-of-sight, and a terrain-based cost calculation in order to improve the accuracy of the FORTRAN Fix (FFIX) algorithm when used by airborne platforms. By conditioning data sets to be used by FFIX, airborne platform locations can be shifted to more accurately reflect changes in aircraft and antenna orientation. Additionally, we can reduce the area produced by eliminating low probability search areas.
{"title":"Airborne geo-location for search and rescue applications","authors":"Maxwell Love, M. Greene, Hannah Burgess, Matthew Luehrmann, Scott Mead, Thomas A. Babbitt","doi":"10.1145/2345316.2345350","DOIUrl":"https://doi.org/10.1145/2345316.2345350","url":null,"abstract":"Radio direction finding is used in many search and rescue applications. The ability to accurately reduce a search area helps focus limited resources properly, which increases the probability of a search and rescue operations success. This paper examines the application of vector geometry, radio line-of-sight, and a terrain-based cost calculation in order to improve the accuracy of the FORTRAN Fix (FFIX) algorithm when used by airborne platforms. By conditioning data sets to be used by FFIX, airborne platform locations can be shifted to more accurately reflect changes in aircraft and antenna orientation. Additionally, we can reduce the area produced by eliminating low probability search areas.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"50 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":"114574219","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}
This paper presents a unique solution to the visibility problem in 3D urban environments generated by procedural modeling. We shall introduce a visibility algorithm for a 3D urban environment, consisting of mass modeling shapes. Mass modeling consists of basic shape vocabulary with a box as the basic structure. Using boxes as simple mass model shapes, one can generate basic building blocks such as L, H, U and T shapes, creating a complex urban environment model computing visible parts. Visibility analysis is based on an analytic solution for basic building structures as a single box. A building structure is presented as a continuous parameterization approximating of the building's corners. The algorithm quickly generates the visible surfaces' boundary of a single building and, consequently, its visible pyramid volume. Using simple geometric operations of projections and intersections between these visible pyramid volumes, hidden surfaces between buildings are rapidly computed. Real urban environment from Boston, MA, approximated to the 3D basic shape vocabulary model demonstrates our approach.
{"title":"Fast visibility analysis in 3D procedural modeling environments","authors":"O. Gal, Y. Doytsher","doi":"10.1145/2345316.2345348","DOIUrl":"https://doi.org/10.1145/2345316.2345348","url":null,"abstract":"This paper presents a unique solution to the visibility problem in 3D urban environments generated by procedural modeling. We shall introduce a visibility algorithm for a 3D urban environment, consisting of mass modeling shapes. Mass modeling consists of basic shape vocabulary with a box as the basic structure. Using boxes as simple mass model shapes, one can generate basic building blocks such as L, H, U and T shapes, creating a complex urban environment model computing visible parts. Visibility analysis is based on an analytic solution for basic building structures as a single box. A building structure is presented as a continuous parameterization approximating of the building's corners. The algorithm quickly generates the visible surfaces' boundary of a single building and, consequently, its visible pyramid volume. Using simple geometric operations of projections and intersections between these visible pyramid volumes, hidden surfaces between buildings are rapidly computed. Real urban environment from Boston, MA, approximated to the 3D basic shape vocabulary model demonstrates our approach.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"17 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":"131649112","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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