The document describes a noise domain model for measuring, modeling, and mapping noise exposure. It includes:
- Measuring noise using tools like NoiseTube which allow citizens to contribute measurements
- Modeling noise exposure using data like traffic, terrain, and buildings as inputs to simulation models
- Mapping noise exposure levels and visualizing them in 2D and 3D using standards like CityGML
Presentació realitzada pel Prof. Dr. Thomas H. Kolbe, de l'Institut für Geodäsie, Geoinformatik und Landmanagement de la Universitat Tècnica de Munic, el dia 22/01/2015 a l'ICGC
Phil Day [Configured Things] | Policy-Driven Real-Time Data Filtering from Io...InfluxData
Policy-Driven Real-Time Data Filtering from IoT Sensors with Flux
Data is central to any smart city, and valuable to a range of different consumers. However, access to the data has to be balanced against privacy concerns to ensure that each recipient only receives the set and quality of data they are authorized to access. This talk describes a solution developed around InfluxDB and Flux which filters data in real time according to a declarative policy model and delivers it securely via web-socket data streams
Presentació realitzada pel Prof. Dr. Thomas H. Kolbe, de l'Institut für Geodäsie, Geoinformatik und Landmanagement de la Universitat Tècnica de Munic, el dia 22/01/2015 a l'ICGC
Phil Day [Configured Things] | Policy-Driven Real-Time Data Filtering from Io...InfluxData
Policy-Driven Real-Time Data Filtering from IoT Sensors with Flux
Data is central to any smart city, and valuable to a range of different consumers. However, access to the data has to be balanced against privacy concerns to ensure that each recipient only receives the set and quality of data they are authorized to access. This talk describes a solution developed around InfluxDB and Flux which filters data in real time according to a declarative policy model and delivers it securely via web-socket data streams
Authors/Presenters: Vasileios Mezaris and Benoit Huet.
Video hyperlinking is the introduction of links that originate from pieces of video material and point to other relevant content, be it video or any other form of digital content. The tutorial presents the state of the art in video hyperlinking approaches and in relevant enabling technologies, such as video analysis and multimedia indexing and retrieval. Several alternative strategies, based on text, visual and/or audio information are introduced, evaluated and discussed, providing the audience with details on what works and what doesn’t on real broadcast material.
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
PrepData4Mobilty (Building Blocks) Methodological approach and Roadmap.pptxFIWARE
Europe is on its way to generate and make use of more data than ever. The project PrepDSpace4Mobility aims at contributing to the development of the common European mobility data space by supporting the creation of a technical infrastructure that will facilitate easy, cross-border access to key data for both passengers and freight. Given the enormous potential of data and digital technologies, the project is expected to have a positive impact on European competitiveness, society, and the environment.
We invited experts in the field of mobility, transport and data space technology to join PrepDSpace4Mobility expert workshop #1 to learn more about the preliminary results of the project and give early feedback in order to sharpen the focus as needed and requested from the real market.
Project PrepDSpace4Mobility is Funded by the European Union and coordinated by acatech (Germany), activities are carried out by Amadeus SAS (France), EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology, a body of the European Union, (Spain), FIWARE (Germany), FhG (Germany), IDSA (Germany), iSHARE (Netherlands), TNO (Netherlands), USI (Germany), VTT (Finland), EMTA (France), Group ADP (France), KU Leuven (Belgium), ERTICO (Belgium), BAST (Germany), UIH (Hungary), and MDS (Germany).
Introducing a new encoding of the ISO 19156 Observations and Measurements model, to support transport of observation data using the JSON encoding beloved of web developers
Presentation at the BIM (BIM In Motion) Workshop organized by Bouygues Construction, at their Challenger site outside Paris. The BIM Workshop took place on Wednesday October the 23rd 2019.
An INSPIRE-based vocabulary for the publication of Agricultural Linked DataRaul Palma
FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.
Contextualizing the Visualization of Climate DataRaquel Alegre
EGU 2014, 27th April - 2nd May 2014, Vienna (Austria)
Session: Techniques and tools for effective visualization and sonification in the geosciences
Category: Earth & Space Science Informatics (ESSI)
"INSPIREd computing for EO Based Services" is the title of the presentation made by Paolo Manunta, Head of European Institutions SBU, Planetek Italia.
The presentation, held on November 26th, 2013 during the Big Data workshop organized by Italian Space Agency (ASI).
The INSPIRE Implementing Rules (IRs) on interoperability of spatial data sets and services and for network services include requirements for setting up a Spatial Data Infrastructure in Europe for supporting environmental policy making as well as policies with impact on the environment. To help Data provider with technical aspects of the IRs as well as with its correct implementation, INSPIRE Technical Guidelines (TG) were developed for each 34 data themes (INSPIRE data specifications) and for the different types of INSPIRE network services (discovery, view, download and transformation).
Spatial objects are mapped, digitalized and stored in a GIS data sets or (spatial) database. Normally, the structure of the data will depend on the specific needs for which the data are collected and used. In order to provide them in compliance with INSPIRE, these source data sets have to be transformed to match the data model prescribed by INSPIRE and have to be provided through INSPIRE download services.
This training will show and illustrate through "hands on" exercises how data sets can be transformed and provided through INSPIRE-compliant services by covering the following topics:
1) Data transformation: This session gives an introduction and explanations about encoding rules, mapping original attributes into the INSPIRE data models and vocabularies and extending data models and vocabularies.
2) Download services: This session will explore the procedure of providing transformed dataset into through an INSPIRE network service, e.g. through an WMS (for view services) or WFS or ATOM feeds (download services).
3) "Hands on" session: This session will give an overview of different architectural approaches (e.g. on-the-fly transformation and stand-alone offline transformation) and concrete software solutions for transforming spatial data and creating INSPIRE-compliant services.
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Azure Digital Twins is a PaaS service to build IoT solution on the Azure platform focused on state and on a spatial intelligence graph to model the devices, the sensors and the environment around them.
In this session we talk aboutthe spatial graph and the pipeline the data follow from generation in the device to the processing.
A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Authors/Presenters: Vasileios Mezaris and Benoit Huet.
Video hyperlinking is the introduction of links that originate from pieces of video material and point to other relevant content, be it video or any other form of digital content. The tutorial presents the state of the art in video hyperlinking approaches and in relevant enabling technologies, such as video analysis and multimedia indexing and retrieval. Several alternative strategies, based on text, visual and/or audio information are introduced, evaluated and discussed, providing the audience with details on what works and what doesn’t on real broadcast material.
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
PrepData4Mobilty (Building Blocks) Methodological approach and Roadmap.pptxFIWARE
Europe is on its way to generate and make use of more data than ever. The project PrepDSpace4Mobility aims at contributing to the development of the common European mobility data space by supporting the creation of a technical infrastructure that will facilitate easy, cross-border access to key data for both passengers and freight. Given the enormous potential of data and digital technologies, the project is expected to have a positive impact on European competitiveness, society, and the environment.
We invited experts in the field of mobility, transport and data space technology to join PrepDSpace4Mobility expert workshop #1 to learn more about the preliminary results of the project and give early feedback in order to sharpen the focus as needed and requested from the real market.
Project PrepDSpace4Mobility is Funded by the European Union and coordinated by acatech (Germany), activities are carried out by Amadeus SAS (France), EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology, a body of the European Union, (Spain), FIWARE (Germany), FhG (Germany), IDSA (Germany), iSHARE (Netherlands), TNO (Netherlands), USI (Germany), VTT (Finland), EMTA (France), Group ADP (France), KU Leuven (Belgium), ERTICO (Belgium), BAST (Germany), UIH (Hungary), and MDS (Germany).
Introducing a new encoding of the ISO 19156 Observations and Measurements model, to support transport of observation data using the JSON encoding beloved of web developers
Presentation at the BIM (BIM In Motion) Workshop organized by Bouygues Construction, at their Challenger site outside Paris. The BIM Workshop took place on Wednesday October the 23rd 2019.
An INSPIRE-based vocabulary for the publication of Agricultural Linked DataRaul Palma
FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.
Contextualizing the Visualization of Climate DataRaquel Alegre
EGU 2014, 27th April - 2nd May 2014, Vienna (Austria)
Session: Techniques and tools for effective visualization and sonification in the geosciences
Category: Earth & Space Science Informatics (ESSI)
"INSPIREd computing for EO Based Services" is the title of the presentation made by Paolo Manunta, Head of European Institutions SBU, Planetek Italia.
The presentation, held on November 26th, 2013 during the Big Data workshop organized by Italian Space Agency (ASI).
The INSPIRE Implementing Rules (IRs) on interoperability of spatial data sets and services and for network services include requirements for setting up a Spatial Data Infrastructure in Europe for supporting environmental policy making as well as policies with impact on the environment. To help Data provider with technical aspects of the IRs as well as with its correct implementation, INSPIRE Technical Guidelines (TG) were developed for each 34 data themes (INSPIRE data specifications) and for the different types of INSPIRE network services (discovery, view, download and transformation).
Spatial objects are mapped, digitalized and stored in a GIS data sets or (spatial) database. Normally, the structure of the data will depend on the specific needs for which the data are collected and used. In order to provide them in compliance with INSPIRE, these source data sets have to be transformed to match the data model prescribed by INSPIRE and have to be provided through INSPIRE download services.
This training will show and illustrate through "hands on" exercises how data sets can be transformed and provided through INSPIRE-compliant services by covering the following topics:
1) Data transformation: This session gives an introduction and explanations about encoding rules, mapping original attributes into the INSPIRE data models and vocabularies and extending data models and vocabularies.
2) Download services: This session will explore the procedure of providing transformed dataset into through an INSPIRE network service, e.g. through an WMS (for view services) or WFS or ATOM feeds (download services).
3) "Hands on" session: This session will give an overview of different architectural approaches (e.g. on-the-fly transformation and stand-alone offline transformation) and concrete software solutions for transforming spatial data and creating INSPIRE-compliant services.
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Azure Digital Twins is a PaaS service to build IoT solution on the Azure platform focused on state and on a spatial intelligence graph to model the devices, the sensors and the environment around them.
In this session we talk aboutthe spatial graph and the pipeline the data follow from generation in the device to the processing.
A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
2. Overview
• Following slides provide a detailed summary
description of the Noise Domain Model
• Measurements
• Modelling
• Mapping
• Plus proposed generic CityGML ADE for:
• Adding time-varying properties to City Objects
• New City Objects: ‘Heat Map’ for visualising thematic
properties that vary over space and time
3. Noise Domain Model
• Develop a model that meets the requirements for:
– Open Data Exchange
– 2D and 3D Visualisation
• Applications:
– Citizen participation in measuring noise exposure (NoiseTube)
– Monitoring /Assessment of noise exposure
• Noise simulation modelling (END Directive)
– Noise Exposure Mapping for citizen engagement and
decision-making
• Strategic Noise Maps (END Directive)
• 2D/3D visualisation of noise exposure (NoiseTube)
4. Noise Domain Model
Measurement Modelling Mapping
Ancillary data:
needed as input to
noise simulation
modelling
3 Viewpoints:
11. Noise Measurements Model
Objective: Extend existing models where possible
Candidate Application Schema:
Observations and
Measurements
ISO 19156: Observations and Measurements
INSPIRE Observations – Specialised Observations:
Point, Trajectory and Gridded Observations
Procedures OGC SensorML 2.0
INSPIRE Observations - Process
Results OGC Coverages 2.0
OGC WaterML 2.0 - TimeSeries
OGC SWE Common
12. 12
OM_Observation: an EVENT whose RESULT is an estimate of a value
of some PROPERTY of some THING obtained using a specified
PROCEDURE …
ISO19156 Observations and Measurements
14. Profiling ISO19156 Observations and Measurements
14
ISO 19156 ‘Observations and measurements’ provides a generic framework for
describing both the observing event and the results of the observation. It is applicable
to a wide range of scientific and technical domains.
The generic nature of this standard means
that it requires further specialisation to
constrain aspects of the model …
15. INSPIRE Specialised Observations
Profiling ISO19156 Observations and Measurements
Defines 3 types of Specialised Observation based on
the Result Type. These extend the example ISO
19156 specialised observations:
• Gridded Observation
• Trajectory or Profile Observations
• Point Observations
INSPIRE Specialised
Observations constrain
the result,
featureOfInterest and
phenomenonTime
16. • Result is an Any type – so...it can be anything!
Encoding the Observation Result
Not easy to
implement or ensure
consistency
....Need to define what type to use
in your Application Schema
INSPIRE Specialised
Observation
Result Type Implementation Schema
PointObservation DiscretePointCoverage OGC Coverages 2.0
PointTimeSeriesObservation TimeSeries OGC WaterML 2.0
MultiPointObservation MultiPointCoverage OGC Coverages 2.0
ProfileObservation RectifiedGridCoverage or
ReferencableGridCoverage
OGC Coverages 2.0
TrajectoryObservation TimeSeries OGC WaterML 2.0
GridObservation RectifiedGridCoverage or
ReferencableGridCoverage
OGC Coverages 2.0
17. • Result is an Any type – so...it can be anything!
Encoding the Observation Result
Not easy to
implement or ensure
consistency
....Need to define what type to use
in your Application Schema
INSPIRE Specialised
Observation
Result Type Implementation Schema
PointObservation DiscretePointCoverage OGC Coverages 2.0
PointTimeSeriesObservation TimeSeries OGC WaterML 2.0
MultiPointObservation MultiPointCoverage OGC Coverages 2.0
ProfileObservation RectifiedGridCoverage or
ReferencableGridCoverage
OGC Coverages 2.0
TrajectoryObservation TimeSeries OGC WaterML 2.0
GridObservation RectifiedGridCoverage or
ReferencableGridCoverage
OGC Coverages 2.0
Question: Should Noise Measurements re-
use/extend INSPIRE Specialised Observations?
Recommendation - YES
18. Procedures: OGC SensorML and INSPIRE Process
SensorML is a comprehensive, generic model for
describing the processes used to estimate the value
of a phenomenon using a sensor
• A measurement process can be described within an
external resource and referenced to in the
Observation
19. Procedures: OGC SensorML and INSPIRE Process
INSPIRE Observation – Process is intended to provide an alternative
lightweight model for describing the procedure compared to
SensorML
«featureType»
Process
«voidable»
+ documentation :DocumentCitation [0..*]
+ inspireld :Identifier
+ name :CharacterString [0..1]
+ processParameter :ProcessParameter [0..*]
+ responsibleParty :RelatedParty [1..*]
+ type :CharacterString
«FeatureType»
observation::OM_Process
«dataType»
ProcessParameter
+ description :CharacterString [0..1]
+ name :ProcessParameterNameValue
s
+generatedObservation
0..*
ProcessUsed +procedure
1
class Process
«voidable
+ docum
+ inspire
+ name
+ proce
+ respon
+ type
«FeatureType»
observation::OM_Observation
+ phenomenonTime :TM_Object
+ resultTime :TM_Instant
+ validTime :TM_Period [0..1]
+ resultQuality :DQ_Element [0..*]
+ parameter :NamedValue [0..*]
Base Types 2::DocumentCitation
+ name :CharacterString
«voidable»
+ shortName :CharacterString [0..1]
+ date :CI_Date
+ link :URL [1..*]
+ specificReference :CharacterString [0..*]
+ d
+ n
«codeList»
ProcessParameterNameValue
tags
asDictionary = true
extensibility = any
vocabulary =
xsdEncodingRule = iso19136_2007_INSPIRE_Extensions
0..*
+relatedObservation 0..*
+generatedObservation
0..*
ProcessUsed +proced
20. Procedures: SensorML and INSPIRE Process
INSPIRE Observation – Process is intended to provide an alternative
lightweight model for describing the procedure compared to
SensorML
«featureType»
Process
«voidable»
+ documentation :DocumentCitation [0..*]
+ inspireld :Identifier
+ name :CharacterString [0..1]
+ processParameter :ProcessParameter [0..*]
+ responsibleParty :RelatedParty [1..*]
+ type :CharacterString
«FeatureType»
observation::OM_Process
«dataType»
ProcessParameter
+ description :CharacterString [0..1]
+ name :ProcessParameterNameValue
s
+generatedObservation
0..*
ProcessUsed +procedure
1
class Process
«voidable
+ docum
+ inspire
+ name
+ proce
+ respon
+ type
«FeatureType»
observation::OM_Observation
+ phenomenonTime :TM_Object
+ resultTime :TM_Instant
+ validTime :TM_Period [0..1]
+ resultQuality :DQ_Element [0..*]
+ parameter :NamedValue [0..*]
Base Types 2::DocumentCitation
+ name :CharacterString
«voidable»
+ shortName :CharacterString [0..1]
+ date :CI_Date
+ link :URL [1..*]
+ specificReference :CharacterString [0..*]
+ d
+ n
«codeList»
ProcessParameterNameValue
tags
asDictionary = true
extensibility = any
vocabulary =
xsdEncodingRule = iso19136_2007_INSPIRE_Extensions
0..*
+relatedObservation 0..*
+generatedObservation
0..*
ProcessUsed +proced
Recommendation 1 – Use SensorML for
describing Sensor Systems:
• It is more mature and comprehensive
• Provides flexibility in the depth of info you can provide
21. Proposed Noise Measurements Model
• Two data exchange requirements:
– Source noise exposure measurements
– Aggregated/modelled noise exposure measurements
1. Source noise exposure measurements:
– Time series collected at mobile locations (NoiseTube)
– Need to extend to include summary statistics
Recommendation 1 – Use INSPIRE Specialised Observation –
Trajectory Observation
22. NoiseTrajectoryObservation
class Context Diagram: NoiseTrajectoryObservation
TimeSeries Result for Trajectory
Observation
TimeSeries Result for Trajectory
Observation
SamplingCoverageObservation
«featureType»
Trajectory and Profile Observations::
TrajectoryObservation
«featureType»
NoiseTrajectoryObservation
«DataType»
NoiseTubeStatistics
+ count :Integer
+ lengthOfTrack :Length
+ maxLAeq :Measure
+ meanLAeq :Measure
+ minLAeq :Measure
constraints
{UoM of maxLAeq shall be given in dBA}
{UoM of minLAeq shall be given in dBA}
{UoM of meanLAeq shall be given in dBA}
«dataType»
Trajectory and Profile
Observations::
TimeLocationValueTriple
+ location :GM_Position
«DataType»
SummaryStatistics
CVT_TimeInstantValuePair
«DataType»
Timeseries::
AnnotatedTimeValuePair
+ geometry :TM_Position
+ value :Record
CVT_DiscreteTimeInstantCoverage
«Type»
Timeseries::Timeseries
+ temporalExtent :TM_Period
Constraints for INSPIRE Specialised
Observation - Trajectory Observation
1. result must be a TimeSeries
2. each point in the result must be a
TimeLocationValueTriple
3. phenomenonTime must be a TM_Period
4. featureOfInterest must be a
SF_SamplingCurve
NOTE: A Specialised NoiseTrajectoryObservation has been
developed to support the requirements of the NoiseTube
+summaryStatistics 0..1
+collection 0..*
+point 0..*
NOTE: a SF_SamplingCurve
feature must also be generated
representing the trajectory
For an aggregated/modelled
trajectory observation the INSPIRE
TrajectoryObservation should be
used
23. 2. Aggregated/modelled noise exposure measurements
• Post-processing modelling may generate generalised noise exposure
measurements for an area of interest:
– Regular gridded data – overlay over terrain model or city model
– Around Road, Rail, Airport, Industry (see Noise Mapping Model)
Proposed Noise Measurements Model
Recommendation 1 – Use INSPIRE Specialised Observation –
Grid Observation
24. class Context Diagram: Gridded Noise Observation
Grid Observation Result - INSPIRE RectifiedGridCoverage
«featureType»
Gridded Observations::
GridObservation
«FeatureType»
Sampling Coverage Observation::
SamplingCoverageObservation
«FeatureType»
coverageObservation::
OM_DiscreteCoverageObservation
«FeatureType»
observation::OM_Observation
Constraints for INSPIRE Grid Observation:
1. The Result shall be a RectifiedGridCoverage
2. phenomenonTime must be a TM_Instant
3. featureOfInterest must be a SF_SamplingSolid or
SF_SamplingSurface
«featureType»
Coverages (Domain and Range)::
RectifiedGridCoverage
constraints
{domainIsRectifiedGrid}
{grid points shall coincide with grid cell centres}
«featureType»
Coverages (Domain and Range)::
CoverageByDomainAndRange
+ coverageFunction :CoverageFunction [0..1]
+ domainSet :Any
+ rangeSet :Any [0..*] {ordered}
«union»
Coverages (Domain and Range)::
CoverageFunction
+ ruleDefinition :CharacterString
+ ruleReference :URI
+ gridFunction :GridFunction
«featureType»
Coverages (Base)::Coverage
+ metadata :Any [0..*]
+ rangeType :RecordType
«dataType»
Coverages (Domain and Range)::GridFunction
+ sequenceRule :CV_SequenceRule [0..1]
+ startPoint :Integer [0..*] {ordered}
NOTE: The GridObservation shall be directly imported
from the INSPIRE Coverage Model without any
addition extensions for Noise.
GridObservation
33. Noise Mapping
END Strategic Noise Mapping:
• Enable assessment of exposure of populations to noise
• Inform development of action plans to reduce noise exposure and
protect existing quiet areas
• inform and engage the public in the development of noise action plans
34. END Strategic Noise Maps
• Environmental noise exposure has to be strategically
mapped in the following areas:
– Agglomerations - large, densely populated urban areas –
(UK: > 250,000 people with a population density of < 500
/km2 )
– Around roads with more than six million vehicle passages
a year
– Around railways with more than 60,000 train passages a
year
– Around airports with more than 50,000 movements a year
35. Noise Exposure Maps
• These are typically thematic maps “Heat Maps”,
contours, grids or city objects whose values may be:
– Noise Exposure: Laeq (dBA), Lden, Lnight
– Statistics: Total or % population exposed
36. Candidate Application Schema
• Basic overlaying of noise exposure information
– ISO 19156 – Observations and Measurements
– OGC Coverages – e.g. RectifiedGridCoverage,
MultiPointCoverage
• Developed Noise Mapping Model:
– Contours (Generic)
– Generic Models for Time-Varying Properties
• TimeVaryingProperty Model
• Heat Map ADE
– City Object – Noise Exposure
Noise Mapping
37. • Very simple, generic model consisting of a contour
line, contour value and contour property type
Noise Contours
Question: Are any other properties required?
class Noise Contours
«FeatureType»
ContourLine
+ geometry :GM_Curve
+ contourValue :Measure
+ contourPropertyType :ContourPropertyType
«CodeList»
ContourPropertyType
tags
codeList = http://www.iscopeproject.net/codeList/ContourPropertyType
extensibility = any
xsdEncodingRule = citygml-ade
Example values for ContourPropertyType:
Noise Exposure: minLAeq, maxLAeq, meanLAeq, Lden, Lnight
etc.....
38. • Real-world phenomenon like noise expose vary both
over time and space
• Starting point develop a generic model for time-
varying properties
Modelling Time-Varying Property
Design Principles:
• Needs to be simple
• Can be used within for multiple purposes:
• Thematic Noise Mapping “Heat Maps”
• City Object Noise Exposure Maps
• Other thematic areas: Energy -Solar Potential
• Based on existing O&M Modelling pattern as has similar
requirements
39. class CityGML ADE: Time Varying Properties
«FeatureType»
TimeDependentVariable
+ referenceTime :TM_Object
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«type»
Records and Class
Metadata::Any
{root}
Metadata entity set information::
MD_Metadata
«FeatureTyp...
observation::
OM_Process
+phenomenon 1
Metadata
+metadata
0..1
Process
+procedure 0..1
+result 1
TimeDependentVariable : has a RESULT which is an estimate of a value of
some PROPERTY belonging to a feature obtained using a specified
PROCEDURE …
Modelling Time-Varying Property
NOTE: there is no
featureOfInterest as the
TimeDependentVariable
is intended to be a
complex property of a
feature
NOTE: The multiplicity of
procedure and metadata
has been relaxed
40. Thematic Heat Map
• The Time-Varying Property can be used within a
Thematic Heat Map feature
• A HeatMap feature has been developed as a new
CityGML City Object LOD0
• 2 Specialised types:
– SurfaceHeatMap
– GriddedHeatMap
41. Thematic Heat Mapclass Context Diagram: Heat Maps
«FeatureType»
SurfaceHeatMap
+ loD0_Surface :GM_MultiSurface
«type»
Records and Class Metadata::
Any
{root}
«FeatureType»
CityGML ADE: Time Varying
Properties::
TimeDependentVariable
+ referenceTime :TM_Object
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«FeatureType»
GriddedHeatMap
constraints
{/* result must be a RectifiedGridCoverage */
inv: self.result.oclIsKindOf(RectifiedGridCoverage)}
CoverageByDomainAndRange
«featureType»
Coverages (Domain and Range)::
RectifiedGridCoverage
+result 1
+phenomenon
1
+result 1
42. • Time-varying properties such as Noise
Exposure/Solar Potential can be added as thematic
attributes to City Objects
Adding Time-Varying Properties to City Objects
• Model should be flexible enough
to allow objects to have multiple
time-varying properties
• Time-varying properties such as Noise
Exposure/Solar Potential can be added as thematic
attributes to City Objects
43. class Buildings
«ADEEleme...
Building
«FeatureType»
CityGML ADE: Time Varying
Properties::
TimeDependentVariable
+ referenceTime :TM_Object
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«type»
Records and Class Metadata::
Any
{root}
AbstractBuilding
«featureType»
Building::Building
+noiseExposure 0..*
+phenomenon 1 +result 1
City Object – Noise Exposure
Example: Building Noise Exposure
44. class Buildings
«ADEEleme...
Building
«FeatureType»
CityGML ADE: Time Varying
Properties::
TimeDependentVariable
+ referenceTime :TM_Object
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«type»
Records and Class Metadata::
Any
{root}
AbstractBuilding
«featureType»
Building::Building
+noiseExposure 0..*
+phenomenon 1 +result 1
City Object – Noise Exposure
Example: Building Noise Exposure
Profiling the Result:
• A business rule should be defined for
each time-varying property to
constrain the result type
• Result type can be existing
record/coverage type:
• SWE Common – Data Array
• Coverage
45. class Buildings
«ADEEleme...
Building
«FeatureType»
CityGML ADE: Time Varying
Properties::
TimeDependentVariable
+ referenceTime :TM_Object
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«type»
Records and Class Metadata::
Any
{root}
AbstractBuilding
«featureType»
Building::Building
«Type»
NoiseExposureDataRecord
+ minLAeq :Measure
+ maxLAeq :Measure
+ meanLAeq :Measure
result must be a NoiseExposureDataRecord
/* result must be a
NoiseExposureDataRecord*/
inv: self.result.oclIsKindOf
(NoiseExposureDataRecord)
+noiseExposure 0..*
+phenomenon 1 +result 1
City Object – Noise Exposure
Example: Building Noise Exposure
Profiling the Result:
• A business rule should be defined for
each time-varying property to
constrain the result type
• Result type can be existing
record/coverage type:
• SWE Common – Data Array
• Coverage
• Or, can explicitly define a <<type>>
class within the domain model
46. Conclusions
• Action 1: Need to share with modelling team to agree
proposed generic CityGML ADE for time-varying properties
and Heat Map
• Action 2: Noise Domain Expert Review to identify what noise
exposure parameters would be published in a heat map or on
a City Object
– Needed to develop controlled vocabulary of phenomenon
– Agree whether SWE Common Data Array would be suitable for
encoding result or define concrete result class
• Action 3: Identify which City Objects would form a Noise
Exposure – City Objects ADE
Editor's Notes
The Noise Domain Model needs to take into account three different viewpoints:Source Measurements of Noise Pollution plus ancillary data inputs (e.g. Road surface material/condition, speed limits, average amount and type of transport). These are required as inputs into.....Models – these can be simple algorithms such aggregation/generalisation to produce summary statistics to complex noise simulation modellingEstimated Noise Exposure: the result of noise measurement and modelling are estimates of noise exposure which can then be visualised in 2D and 3D for decision making