The document discusses annotating geographical maps with social metadata from a surveillance environment. It proposes extracting significant tags from maps to enrich them using a statistical method. A metadata retrieval and search module is developed to allow operators to view historical metadata and suggest new annotations. Case studies applying the tag extraction method to maps of Turin and the Everest area are analyzed. Future work involves standardizing tags, relating similar annotations, and using data mining to integrate web resources for tagging maps from sensor data.
This document describes a methodology for creating dynamic crowding maps using mobile phone data to estimate population exposure during floods. It involves the following steps:
1) Applying Histogram of Oriented Gradients (HOG) to reduce high-dimensional mobile phone user data and cluster similar days.
2) Functionally clustering daily density profiles (DDP) of mobile phone users over time to group days with similar patterns.
3) Estimating total population exposed ("city users") by spatially matching mobile phone and census data to correct for market share.
4) Visualizing representative daily profiles for clusters using functional box plots of DDP trends over time.
The method is applied to a case study area
Qualità dei dati OpenStreetMap: sperimentazioni sulla città di Milano e risul...Marco Minghini
These slides were presented during the Italian OpenStreetMap conference - OSMit 2016 (http://conf.openstreetmap.it), held in Milan (Italy) on May 20-21, 2016. They include a description of some ongoing research works on OpenStreetMap which are under development at the GEOlab (http://geolab.como.polimi.it) of Politecnico di Milano.
A GRASS-based procedure to compare OSM and IGN Paris road network datasetsMarco Minghini
These slides were presented during the WG2 meeting of COST Action IC1203 ENERGIC (http://vgibox.eu) held in Paris on December 3-4, 2015, which was focused on the evaluation of OSM quality through the comparison with official IGN data. The presentation describes an application of an open source GRASS-based procedure - including a Web Processing Service - to compare OSM and authoritative road network datasets (https://github.com/MoniaMolinari/OSM-roads-comparison) in the Paris case study.
This document summarizes a study that used SAR (Synthetic Aperture Radar) data to detect parcel-level damage from the 2003 Bam, Iran earthquake. Researchers developed a parcel database from city maps and extracted building footprints. SAR images before and after the quake were analyzed at the parcel-level. A change detection index was calibrated using simulated radar cross-section curves for different building orientations. The calibrated SAR damage map correlated well with a visual damage interpretation from optical images, validating the parcel-based SAR approach for detecting earthquake damage.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
The document describes an interactive web visualization tool called VISOR-RISNA that was developed to help interpret the results of a regional seismic risk assessment study in Navarre, Spain and aid in defining emergency response levels. VISOR-RISNA allows non-technical users like decision-makers to easily access and understand large amounts of risk data to inform emergency planning in a visually compelling way.
Modelling traffic flows with gravity models and mobile phone large dataUniversity of Salerno
The analysis of origin-destination traffic flows is useful in many contexts of application
as urban planning and tourism economics, and have been commonly studied through the
Gravity Model, which in its simplest formulation states that flows are proportional to masses
of both origin and destination and inversely proportional to distance between them. Using data
from the flow of mobile phone signals among different areas recorded on hourly basis for several
months, in this study we use the Gravity Model to characterize the dynamic of such flows
over the time in the strongly urbanized and flood-prone area of the Mandolossa (western outskirts
of Brescia, northern Italy), with the final aim of predicting the traffic flow during flood
episodes. In order to better account for the dynamic of flows over time, we introduce in the
model a most accurate set of explanatory variables: (i) the density of mobile phone users by
area and time period and (ii) some appropriate temporal effects. Preliminary results show that
the joint use of these two novel sets of explanatory variables allow us to obtain a better linear
fitting of the Gravity Model and a better traffic flow prediction for the flood risk evaluation.
This document describes a methodology for creating dynamic crowding maps using mobile phone data to estimate population exposure during floods. It involves the following steps:
1) Applying Histogram of Oriented Gradients (HOG) to reduce high-dimensional mobile phone user data and cluster similar days.
2) Functionally clustering daily density profiles (DDP) of mobile phone users over time to group days with similar patterns.
3) Estimating total population exposed ("city users") by spatially matching mobile phone and census data to correct for market share.
4) Visualizing representative daily profiles for clusters using functional box plots of DDP trends over time.
The method is applied to a case study area
Qualità dei dati OpenStreetMap: sperimentazioni sulla città di Milano e risul...Marco Minghini
These slides were presented during the Italian OpenStreetMap conference - OSMit 2016 (http://conf.openstreetmap.it), held in Milan (Italy) on May 20-21, 2016. They include a description of some ongoing research works on OpenStreetMap which are under development at the GEOlab (http://geolab.como.polimi.it) of Politecnico di Milano.
A GRASS-based procedure to compare OSM and IGN Paris road network datasetsMarco Minghini
These slides were presented during the WG2 meeting of COST Action IC1203 ENERGIC (http://vgibox.eu) held in Paris on December 3-4, 2015, which was focused on the evaluation of OSM quality through the comparison with official IGN data. The presentation describes an application of an open source GRASS-based procedure - including a Web Processing Service - to compare OSM and authoritative road network datasets (https://github.com/MoniaMolinari/OSM-roads-comparison) in the Paris case study.
This document summarizes a study that used SAR (Synthetic Aperture Radar) data to detect parcel-level damage from the 2003 Bam, Iran earthquake. Researchers developed a parcel database from city maps and extracted building footprints. SAR images before and after the quake were analyzed at the parcel-level. A change detection index was calibrated using simulated radar cross-section curves for different building orientations. The calibrated SAR damage map correlated well with a visual damage interpretation from optical images, validating the parcel-based SAR approach for detecting earthquake damage.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
The document describes an interactive web visualization tool called VISOR-RISNA that was developed to help interpret the results of a regional seismic risk assessment study in Navarre, Spain and aid in defining emergency response levels. VISOR-RISNA allows non-technical users like decision-makers to easily access and understand large amounts of risk data to inform emergency planning in a visually compelling way.
Modelling traffic flows with gravity models and mobile phone large dataUniversity of Salerno
The analysis of origin-destination traffic flows is useful in many contexts of application
as urban planning and tourism economics, and have been commonly studied through the
Gravity Model, which in its simplest formulation states that flows are proportional to masses
of both origin and destination and inversely proportional to distance between them. Using data
from the flow of mobile phone signals among different areas recorded on hourly basis for several
months, in this study we use the Gravity Model to characterize the dynamic of such flows
over the time in the strongly urbanized and flood-prone area of the Mandolossa (western outskirts
of Brescia, northern Italy), with the final aim of predicting the traffic flow during flood
episodes. In order to better account for the dynamic of flows over time, we introduce in the
model a most accurate set of explanatory variables: (i) the density of mobile phone users by
area and time period and (ii) some appropriate temporal effects. Preliminary results show that
the joint use of these two novel sets of explanatory variables allow us to obtain a better linear
fitting of the Gravity Model and a better traffic flow prediction for the flood risk evaluation.
The document describes a proposed smart parking system that uses LED lighting to dynamically designate parking spaces. It can adjust the number of parking spaces based on real-time needs, and uses an automated system to guide vehicles and control traffic flow. The system aims to address common parking issues like finding available spots, locating vehicles, and maximizing space utilization. It incorporates technologies like sensors, computerized controls, GPS, and mobile apps to provide services like reservations, payment, and notifications.
ParkMe has a wide range of smart parking solutions for cities including: custom parking apps, real-time occupancy information, mobile payment integration, etc.
ParkMe works with cities all over the world to help drivers find & pay for the best parking. Cities we've worked with include Los Angeles, Hollywood, Orlando, Dallas, Austin, Miami Beach, & many more. For more info check out www.parkme.com/municipalities or contact us at sales@parkme.com
The document describes an automated parking system called Irisglobe that can save space, time, and money. It details several types of automated parking systems including lift box, 2 park, Z park, ELEPARK, and round types. It explains how the systems work, with cars being placed on a platform and then stored in lower or upper storage levels. The document outlines Irisglobe's strategy for total planning of automated parking systems from initial site surveys to construction and system turnover.
The document discusses India's growing problem with parking shortages as the population increases and more vehicles are on the road. It notes that current parking takes up a lot of space and proposes smart parking systems as a solution. These systems, like multi-level parking structures, lifts, and automated storage, can significantly reduce the space needed for parking large numbers of vehicles. They allow for more efficient use of limited urban land and can help address issues like congestion from parking. The document advocates for implementing such smart parking technologies in India to modernize parking and maximize space usage.
This presentation is from - C-DAC Hyderabad Team ---
1. SPARK operates by monitoring the availability of car parking spaces and makes that information available to customers and facility administrators.
2. Customers use it for guiding them in their choice of parking space and administrators use it to aid in overall parking management & planning.
3. Sensor networks are a natural candidate for car-park management systems, because they allow status to be monitored very accurately - for each
parking space, if desired. Wireless sensor networks have the advantage that they can be deployed in existing car parks without having to install
new cabling for network and electricity to reach each sensing device.
for further information please visit - www.ubicomp.in/spark
This document describes a smart parking system that uses various sensors and technologies to automatically manage vehicle parking. The system uses infrared sensors to detect vehicle presence and control entry and exit gates. A real-time clock tracks parking time and a microcontroller calculates parking fees. Reed switches sense vehicle positions and an LCD displays location and fare information. The system aims to implement systematic parking with one vehicle entering at a time.
Smart Parking Concept - An Internet of Things SolutionrapidBizApps
Universal access to computational power and bandwidth has allowed people and governments to accept and adopt new technologies that make life easier for everybody. A surge in the availability of low cost connected devices has paved the way for powerful technological advances towards the goal of building smart cities. This eBook presents the concept of smart parking that empowers communities to harness connectivity to manage traffic and optimize parking space that scales alongside demand.
Company: rapidBizApps
Website: www.rapidbizapps.com
This document discusses automatic car parking systems. It provides an introduction and overview of basic components like stepper motors and sensors. It describes the hardware workings of using a microcontroller and sensors to control an automatic parking system. The document outlines different types of automatic parking systems and notes their advantages in requiring less space and reducing pollution compared to traditional parking. It also discusses some disadvantages like potential contamination in parking lots.
This document provides an introduction to Internet of Things (IoT) and smart cities. It discusses Kevin Ashton who coined the term "Internet of Things" and his vision for using data to increase efficiency. Key enabling technologies for IoT like cheap sensors, bandwidth, processing and wireless coverage are outlined. Examples of IoT applications in various sectors like manufacturing, transportation, agriculture and smart cities are provided. The document also discusses challenges in making sense of the large amounts of data generated by IoT devices and the importance of a citizen-centric approach to building smart cities by leveraging crowdsourcing and citizen engagement.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
Smart Urban Planning Support through Web Data Science on Open and Enterprise ...Gloria Re Calegari
Prediction of expensive datasets starting from a set of cheap heterogeneous information sources in smart city scenarios.
Prediction of the population and land use of Milano starting from data about Points Of Interest and phone activity.
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...ijsrd.com
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Dat...IOSR Journals
The document describes a proposed spatial data mining system called SD-Miner. SD-Miner consists of three main parts: a graphical user interface, an SD-Miner module for processing spatial data mining functions, and a data storage module. The SD-Miner module provides four spatial data mining functionalities: spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. The document presents the architecture of SD-Miner and provides examples of using it to perform spatial clustering, classification, and characterization on spatial data from a database.
The incorporation of numeric models and simulations onto GIS platforms will answer existing and developing problems of increasing complexity. This can be described as a move from analysis of what is happening to what will happen, or what could happen, and why. Some examples of this type of predictive modelling are: diagnostics and forecasts on shoreline erosion, land uses and their risk assessments, or control of human presence in natural areas. It will also be necessary to combine the simulations to aggregate output for enhanced decision support.
The platform is within the scope of the PIKSEL project, started in 2020 by the Catalan government and CIMNE to develop a system to support territorial management and decision support.
The primary motivation of the platform is the social interconnection of researchers, via the interoperability of content, numerical models and simulations. Models are specifically designed for a function, and like any software, there is no rule or methodology for creating them. Models are extremely heterogeneous, almost all are coded and constructed differently, differing in operating systems, hardware platforms, programming languages, inputs and outputs, and interfaces, with varied requirements, languages, inputs, outputs, measurements and formats. Harmonization of models has been a long sought goal, so that users would be able to combine models with ease, and faces distinct challenges to enact, as detailed by Zhang et al., 2018.
It is required to develop a manner allow ingress of new content, as well as inventory and access mechanisms. This is practical; the development of content (e.g. models, scripts, data) is time consuming and opening the platform for external participation is essential. The ecommerce component will function as a catalog and portal to the PIKSEL platform.
The objective of this investigation is to create a content management system with ecommerce capabilities for a platform as a service (PaaS) that utilizes computational models in addition to data. Said CMS will allow interoperability between resources for aggregated output.
- The document presents a technique called WhereNext that predicts the next location of a trajectory based on analyzing patterns from previous movements without considering individual user information.
- WhereNext builds a prediction tree model from patterns of movement called T-Patterns extracted from trajectory data. It allows spatial and temporal approximation to account for noise in real trajectories.
- The method can be tuned for accuracy and prediction rate. Evaluation on a real dataset of 17,000 vehicle GPS trajectories in Milan showed it effectively predicts next locations.
PREDICTION OF STORM DISASTER USING CLOUD MAP-REDUCE METHODAM Publications
The document discusses prediction of storm disasters using the Cloud Map-Reduce method for stream data mining. It begins with background on spatial data mining and its tasks/techniques. Issues with spatial data mining are also outlined. Stream data mining and its importance for analyzing continuous data streams is then introduced. Common stream data processing methods are discussed, including Apache Storm, Kafka, Spark, Flink, MapReduce and Hadoop. The paper aims to predict storm disasters using stream data mining strategies like Cloud Map-Reduce to analyze spatial datasets in real-time.
This document summarizes a research paper on estimating time-evolving origin-destination (O-D) matrices using high-speed GPS data streams. It discusses using online machine learning techniques to build and maintain O-D matrices and histograms over time in order to model variables like travel time. A real-world case study using taxi GPS data from Porto, Portugal is also presented. Experimental results show the proposed time-evolving O-D matrix and multidimensional discretization techniques outperform static grid-based approaches and offline regression models in estimating travel times.
Provenance Analytics at AAAI Human Computation Conference 2013T Dong Huynh
Trung Dong Huynh presenting the paper entitled "Interpretation of Crowdsourced Activities using Provenance Network Analysis" - How analysing provenance graphs can help interpreting crowdsouced activities in CollabMap
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
Geotagging Social Media Content with a Refined Language Modelling ApproachSymeon Papadopoulos
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
The document describes a proposed smart parking system that uses LED lighting to dynamically designate parking spaces. It can adjust the number of parking spaces based on real-time needs, and uses an automated system to guide vehicles and control traffic flow. The system aims to address common parking issues like finding available spots, locating vehicles, and maximizing space utilization. It incorporates technologies like sensors, computerized controls, GPS, and mobile apps to provide services like reservations, payment, and notifications.
ParkMe has a wide range of smart parking solutions for cities including: custom parking apps, real-time occupancy information, mobile payment integration, etc.
ParkMe works with cities all over the world to help drivers find & pay for the best parking. Cities we've worked with include Los Angeles, Hollywood, Orlando, Dallas, Austin, Miami Beach, & many more. For more info check out www.parkme.com/municipalities or contact us at sales@parkme.com
The document describes an automated parking system called Irisglobe that can save space, time, and money. It details several types of automated parking systems including lift box, 2 park, Z park, ELEPARK, and round types. It explains how the systems work, with cars being placed on a platform and then stored in lower or upper storage levels. The document outlines Irisglobe's strategy for total planning of automated parking systems from initial site surveys to construction and system turnover.
The document discusses India's growing problem with parking shortages as the population increases and more vehicles are on the road. It notes that current parking takes up a lot of space and proposes smart parking systems as a solution. These systems, like multi-level parking structures, lifts, and automated storage, can significantly reduce the space needed for parking large numbers of vehicles. They allow for more efficient use of limited urban land and can help address issues like congestion from parking. The document advocates for implementing such smart parking technologies in India to modernize parking and maximize space usage.
This presentation is from - C-DAC Hyderabad Team ---
1. SPARK operates by monitoring the availability of car parking spaces and makes that information available to customers and facility administrators.
2. Customers use it for guiding them in their choice of parking space and administrators use it to aid in overall parking management & planning.
3. Sensor networks are a natural candidate for car-park management systems, because they allow status to be monitored very accurately - for each
parking space, if desired. Wireless sensor networks have the advantage that they can be deployed in existing car parks without having to install
new cabling for network and electricity to reach each sensing device.
for further information please visit - www.ubicomp.in/spark
This document describes a smart parking system that uses various sensors and technologies to automatically manage vehicle parking. The system uses infrared sensors to detect vehicle presence and control entry and exit gates. A real-time clock tracks parking time and a microcontroller calculates parking fees. Reed switches sense vehicle positions and an LCD displays location and fare information. The system aims to implement systematic parking with one vehicle entering at a time.
Smart Parking Concept - An Internet of Things SolutionrapidBizApps
Universal access to computational power and bandwidth has allowed people and governments to accept and adopt new technologies that make life easier for everybody. A surge in the availability of low cost connected devices has paved the way for powerful technological advances towards the goal of building smart cities. This eBook presents the concept of smart parking that empowers communities to harness connectivity to manage traffic and optimize parking space that scales alongside demand.
Company: rapidBizApps
Website: www.rapidbizapps.com
This document discusses automatic car parking systems. It provides an introduction and overview of basic components like stepper motors and sensors. It describes the hardware workings of using a microcontroller and sensors to control an automatic parking system. The document outlines different types of automatic parking systems and notes their advantages in requiring less space and reducing pollution compared to traditional parking. It also discusses some disadvantages like potential contamination in parking lots.
This document provides an introduction to Internet of Things (IoT) and smart cities. It discusses Kevin Ashton who coined the term "Internet of Things" and his vision for using data to increase efficiency. Key enabling technologies for IoT like cheap sensors, bandwidth, processing and wireless coverage are outlined. Examples of IoT applications in various sectors like manufacturing, transportation, agriculture and smart cities are provided. The document also discusses challenges in making sense of the large amounts of data generated by IoT devices and the importance of a citizen-centric approach to building smart cities by leveraging crowdsourcing and citizen engagement.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
Smart Urban Planning Support through Web Data Science on Open and Enterprise ...Gloria Re Calegari
Prediction of expensive datasets starting from a set of cheap heterogeneous information sources in smart city scenarios.
Prediction of the population and land use of Milano starting from data about Points Of Interest and phone activity.
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...ijsrd.com
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Dat...IOSR Journals
The document describes a proposed spatial data mining system called SD-Miner. SD-Miner consists of three main parts: a graphical user interface, an SD-Miner module for processing spatial data mining functions, and a data storage module. The SD-Miner module provides four spatial data mining functionalities: spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. The document presents the architecture of SD-Miner and provides examples of using it to perform spatial clustering, classification, and characterization on spatial data from a database.
The incorporation of numeric models and simulations onto GIS platforms will answer existing and developing problems of increasing complexity. This can be described as a move from analysis of what is happening to what will happen, or what could happen, and why. Some examples of this type of predictive modelling are: diagnostics and forecasts on shoreline erosion, land uses and their risk assessments, or control of human presence in natural areas. It will also be necessary to combine the simulations to aggregate output for enhanced decision support.
The platform is within the scope of the PIKSEL project, started in 2020 by the Catalan government and CIMNE to develop a system to support territorial management and decision support.
The primary motivation of the platform is the social interconnection of researchers, via the interoperability of content, numerical models and simulations. Models are specifically designed for a function, and like any software, there is no rule or methodology for creating them. Models are extremely heterogeneous, almost all are coded and constructed differently, differing in operating systems, hardware platforms, programming languages, inputs and outputs, and interfaces, with varied requirements, languages, inputs, outputs, measurements and formats. Harmonization of models has been a long sought goal, so that users would be able to combine models with ease, and faces distinct challenges to enact, as detailed by Zhang et al., 2018.
It is required to develop a manner allow ingress of new content, as well as inventory and access mechanisms. This is practical; the development of content (e.g. models, scripts, data) is time consuming and opening the platform for external participation is essential. The ecommerce component will function as a catalog and portal to the PIKSEL platform.
The objective of this investigation is to create a content management system with ecommerce capabilities for a platform as a service (PaaS) that utilizes computational models in addition to data. Said CMS will allow interoperability between resources for aggregated output.
- The document presents a technique called WhereNext that predicts the next location of a trajectory based on analyzing patterns from previous movements without considering individual user information.
- WhereNext builds a prediction tree model from patterns of movement called T-Patterns extracted from trajectory data. It allows spatial and temporal approximation to account for noise in real trajectories.
- The method can be tuned for accuracy and prediction rate. Evaluation on a real dataset of 17,000 vehicle GPS trajectories in Milan showed it effectively predicts next locations.
PREDICTION OF STORM DISASTER USING CLOUD MAP-REDUCE METHODAM Publications
The document discusses prediction of storm disasters using the Cloud Map-Reduce method for stream data mining. It begins with background on spatial data mining and its tasks/techniques. Issues with spatial data mining are also outlined. Stream data mining and its importance for analyzing continuous data streams is then introduced. Common stream data processing methods are discussed, including Apache Storm, Kafka, Spark, Flink, MapReduce and Hadoop. The paper aims to predict storm disasters using stream data mining strategies like Cloud Map-Reduce to analyze spatial datasets in real-time.
This document summarizes a research paper on estimating time-evolving origin-destination (O-D) matrices using high-speed GPS data streams. It discusses using online machine learning techniques to build and maintain O-D matrices and histograms over time in order to model variables like travel time. A real-world case study using taxi GPS data from Porto, Portugal is also presented. Experimental results show the proposed time-evolving O-D matrix and multidimensional discretization techniques outperform static grid-based approaches and offline regression models in estimating travel times.
Provenance Analytics at AAAI Human Computation Conference 2013T Dong Huynh
Trung Dong Huynh presenting the paper entitled "Interpretation of Crowdsourced Activities using Provenance Network Analysis" - How analysing provenance graphs can help interpreting crowdsouced activities in CollabMap
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
Geotagging Social Media Content with a Refined Language Modelling ApproachSymeon Papadopoulos
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
An important measurable indicator of urbanization and its environmental implications has been identified as the urban
impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting
urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they
affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic
integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by
single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D
CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional
processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D
CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In
the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract
more urban impervious surfaces
Part 1 of the printed publication "3D-ICONS Guidelines and Case Studies" First published in November 2014.
Public fascination with the architectural and archaeological heritage is well known, it is proven to be one of the main reasons for tourism according to the UN World Tourism Organisation. Historic buildings and archaeological monuments form a significant component Europe’s cultural heritage; they are the physical testimonies of European history and of the di°erent events that led to the creation of the European landscape, as we know it today.
The documentation of built heritage increasingly avails of 3D scanning and other remote sensing technologies, which produces digital replicas in an accurate and fast way. Such digital models have a large range of uses, from the conservation and preservation of monuments to the communication of their cultural value to the public. They may also support in-depth analysis of their architectural and artistic features as well as allow the production of interpretive reconstructions of their past appearance.
The goal of the 3D-ICONS project, funded under the European Commission’s ICT Policy Support Programme which builds on the results of CARARE (www.carare.eu) and 3D-COFORM (www.3d-coform.eu), is to provide Europeana with 3D models of architectural and archaeological monuments of remarkable cultural importance. The project brings together 16 partners (see appendix 2) from across Europe (11 countries) with relevant expertise in 3D modelling and digitization. The main purpose of this project is to produce around 4000 accurate 3D models which have to be processed into a simplified form in order to be visualized on low end personal computers and on the web.
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Geographical Map Annotation With Social Metadata In a Surveillance Environment
1. GEOGRAPHICAL MAP ANNOTATION
WITH SOCIAL METADATA IN A
SURVEILLANCE ENVIRONMENT
Elena Roglia
Tutor: Prof.ssa Rosa Meo
Università degli Studi di Torino
Scuola di Dottorato in Scienza e Alta Tecnologia
Indirizzo: Informatica
2. Overview
SMAT-F1 Project
Second Level Exploitation of data
Objectives and research questions
Multidimensional data management
Metadata research, management and
visualization
Map annotation with significant tags
Conclusions and future works
2
3. Sistema di Monitoraggio Avanzato
del Territorio – SMAT
SMAT Project aims at studying and demonstrating a
surveillance system, to support:
prevention and control of a wide range of natural
events (fires, floods,landslides)
environment protection against human
intervention (traffic, urban planning, pollution
and cultivation)
3
6. SMAT architecture
6
SMAT-F1, is the first phase of
SMAT project and aims to
demonstrate an integrated use
of three Unmanned Air Vehicle
(UAV) platforms inside of a
primary scenario, relevant for
the Piedmont Region.
8. SS&C
Before mission: mission planning, UAS tasks
allocation.
During mission: mission monitoring, data
collection from the CSs, operator support in the
interaction with the system
After mission: conclusive report and Second Level
Exploitation of data.
8
9. Second Level Exploitation activity
analyze and organize data collected during
missions
prepare mission reports
correlate data
allow visualization, re-processing and retrieval
of data according to the end-user needs
provide a mechanism to retrieve and search
metadata
9
10. Metadata Retrieval and Search
Our goal is to add metadata to geo-referenced
objects related to missions stored in the SS&C
database
Metadata are annotations provided by users
of an open, collaborative system (see later!)
The retrieval of annotations occurs by web
services exported by the collaborative systems
10
12. Objectives and Research Questions
12
How to specify the interesting
spatial objects according to
the different
dimensions
involved?
How to search relationships
between already stored data?
How to extract significant
features in maps?
How to enrich maps?
How to generate a
metadata retrieval and
search module able to
answer the requirements?
15. Mission Facts
Mission facts are stored in relationship with
dimensions:
1. Mission in which the fact occurs
2. UAV performing the mission
3. Payload sensor
4. Airport
5. Spatial target
15
Spatial dimensions
16. Metadata Facts
Metadata facts are stored in relationship with
spatial objects and involve the dimensions:
1. Spatial objects
2. Metadata creation time
16
Target
Airport
Route Waypoints
Flown Points
21. ASL Compiler: Back – end phase
Optimization
• identify mission facts that meet the conditions imposed
• identify spatial objects based on these facts
• identify metadata associated with these spatial objects
Code
Generation
• SQL query statement generation
21
23. MDR Tester
The set of constraints the user specifies in
her/his query is not available a priori but is
known only at run-time.
The number of possible combinations is
exponentially large
Automatic procedure to test Compiler
24
25. Volunteered Geographic
Information - VGI
26
“is the harnessing of tools to create,
assemble, and disseminate
geographic data provided voluntarily
by individuals”
Goodchild, M.F., 2007. Citizens as sensors: the
world of volunteered geography. Journal of
Geography, 69(4):211-221
29. GeoNames
30
over 10 millions of geographical names
7.5 millions of unique features:
elevation, population, postal codes,
administrative division, time zone, etc.
39. Case study: 1
41
The map of Turin city and its neighbourhood
102 distinct tags occurring at least 2 times
84 statistical significant tags:
highway: secondary, highway:pedestrian, highway: cycleway
historic:monument, leisure:garden, amenity:fountain
amenity:parking, amenity:atm, amenity:school, amenity:car
sharing, amenity:hospitals, railway:station, shop:supermarket.
40. Case study: 2
Very elegant and touristic district of Turin
28 distinct tags occurring at least 2 times
19 statistical significant tags:
amenity:fountain, amenity:parking, amenity:theatre,
historic:monument, tourism:museum, railway:tram, amenity:place
of worship, highway: pedestrian, amenity:bicycle rental,
amenity:restaurant
amenity:atm, amenity:university,amenity:school, amenity:library,
amenity:car sharing, amenity:hospitals, railway:station,
amenity:pharmacy, railway:construction, shop: supermarket,
shop:bicycle.
42
Case study 1
41. Case study: 3
Everest Area
14 distinct tags occurring at least 2 times
9 statistical significant tags:
natural:water, natural:peak, natural:glacier, tourism:camp site,
43
42. Case study: 4
30 Random Map in Europe:
No significant features
44
44. Significance of absent tags
46
Frequency
computation for
all tags in the
neighbourhood
Mean µ and
standard
deviation σ
Frequency
computation in
the central cell
47. Empirical Method
Given a tag category we compute:
P1= the ratio between its frequency and the
sum of tag frequencies in the central cell.
P2=the ratio between its frequency and the sum
of tag frequencies in the neighbourhood cells.
49
48. 50
tag category is significant
and it is over-represented
in the central cell
tag category is significant
and it is under-represented
in the central cell
over-representation (+)
under-representation (-)
49. Classification problem
• (TP) number of significant tags that are significant
for both methods;
• (FN) the number tags that are significant for
proposed method but not for the empirical
method;
• (FP) the number tags that the empirical method
defined to be significant but proposed method
finds to be not significant;
• (TN) the number of tags that both methods
define to be not significant.
51
52. Other
• Hills of Turin
• Industrial area of Turin
• Everest
• Random Maps
54
53. 1. when statistical method does not identify
significant characteristics the classifier still
extracts significant tags, producing many false
positives as characteristics of the area.
2. when proposed method identifies significant
features:
if their number is low, the classifier continues to
produce an high number of false positives
if their number is high, the classifier improves in
performance, reducing the number of false positives,
but can make some mistakes producing false
negatives.
55
55. Conclusions
Metadata Retrieval and Search Module
Allow the SS&C operator to show historical
metadata
Suggest new metadata as annotation of the
geo-referenced spatial objects
Map annotation with significant tags
57
56. Future Work
• Spatial object annotation according to a
unique tagging system: adopting the tag
ontology provided by a unique system as a
referential knowledge base and then trying to
learn the correspondences between tags in
the different systems
• Recognition of related annotations which
appear to be different (different nouns or
synonymous referred to the same concept).
58
57. • The study of Data Mining methods for the
elaboration and the integration of Web
resources in order to make communicate the
world of ”Internet of Things” with the world of
”Semantic Web”.
• The study and the application of an algorithm
that suggests the area most characterized in
order to apply the proposed statistical
method.
59
59. My pubblications
• E. Roglia, R.Meo, E.Ponassi, Geographical map annotation with significant tags
available from social networks, Chapter in XML Data Mining: Models, Methods, and
Applications, A.Tagarelli (ed.), 26 pp, Idea Group Inc., to appear in February 2011.
• E. Roglia, R.Meo, A SOA-Based System for Territory Monitoring, Chapter in Geospatial
Web services:Advances in Information Interoperability, Peisheng Zhao and Liping Di
(eds.), 27 pp, Idea Group Inc., October 2010. ISBN: 978-1609601928.
• E.Roglia, R.Meo, A Composite Wrapper for Feature Selection, in Proceedings of
Workshop on Data Mining and Bioinformatics in AI*IA - Intelligenza Artificiale e
Scienza della Vita (DMBIO08) Cagliari (Italy), 13 September, 2008.
• E.Roglia, R.Cancelliere, R.Meo, Classification of Chestnuts with Feature Selection by
Noise Resilient Classifiers, in Proceedings of the 16th European Symposium on
Artificial Neural Networks - Advances in Computational Intelligence and Learning
(ESANN08) Bruges (Belgium), 23-25 April, 2008.
61
Editor's Notes
Il progetto SMAT si propone di studiare e dimostrare un sistema di monitoraggio avanzato del territorio per la prevenzione e il controllo di una vasta gamma di eventi naturali (alluvioni, incendi, frane, traffico, urbanistica, inquinamento e coltivazioni).
Sorveglianza dei corsi d’acqua
Controllo delle aree potenzialmente interessate da incendi
Sorveglianza aree interessate da calamità naturali (frane, alluvioni, terremoti, incendi)
Sorveglianza continuativa di aree in cui si sono verificate calamità naturali
Sorveglianza linee di trasporto energia (elettrodotti, oleodotti, gasdotti)
Monitoraggio di aree rurali con raccolta dati
Monitoraggio del traffico, urbano ed extraurbano
Sorveglianza aree danneggiate o minacciate da interventi umani
Sorveglianza di aree a rischio industriale ed inquinamento
Sorveglianza aree in cui sono in corso eventi di particolare rilevanza
SMAT-F1 è focalizzato sulla dimostrazione dell’utilizzo integrato delle tre piattaforme UAV all’interno di uno scenario operativo primario, rilevante per la Regione Piemonte.
Piattaforme UAV innovative nel segmento aereo
Segmento terrestre
Stazioni di controllo controllano il veivolo e i sensori
Stazione di Supervisione e Coordinamento raccoglie i dati dalle singole control station, è il nodo centrale dell’architettura di SMAT e deve consentire agli operatori di ricevere informazioni dagli UAS per la specifica missione/task, fornire supporto all’elaborazione dei dati e diramare specifiche richieste da parte degli operatori agli UAS interessati;
Infrastrutture di comunicazione
Da un serve per sfruttare la ricchezza di informazioni raccolte da fonti diverse (video,
telemetry, images and text files), dall'altro per consentire la produzione di informazioni utili nella definizione di piani di nuova missione.
La correlazione non solo tra i dati di una missione appena compiuta ma tra dati di missione diverse memorizzati nel db.
I metadati devono essere estratti
high-level data flow of MDR and its interaction with other system components
Questa racchiude la semantica di questo abstract specification language!