SlideShare a Scribd company logo
LiDAR processing for road network asset inventory Conor Mc Elhinney, Tim McCarthy Tuesday 28th September 2010
Outline ,[object Object]
 EuRSI
 Road Edge Extraction
 Pole Detection and Extraction
 Resolution Effect of speed on scan lines
 Active Contours
 Publications
 Conclusion,[object Object]
 EuRSI
 Road Edge Extraction
 Pole Detection and Extraction
 Resolution Effect of speed on scan lines
 Active Contours
 Publications
 Conclusion,[object Object]
Geo-referencing the data Workflow GPS Base Station GPS GPS Mobile
Geo-referencing the data Workflow INS GPS Base Station NAV GPS GPS Mobile
Geo-referencing the data Workflow INS LiDAR GPS Base Station NAV + GPS Geo-referenced Point Cloud GPS Mobile
Geo-referencing the data Workflow INS LiDAR Imagery GPS Base Station NAV + + GPS Geo-referenced Point Cloud Geo-referenced Imagery GPS Mobile
Geo-referencing the data Workflow INS LiDAR Imagery GPS Base Station NAV + + GPS Geo-referenced Point Cloud Geo-referenced Imagery GPS Mobile + Geo-referenced Multi-Source Data
NRA Fellowship Application The central aim of this project is to design, construct and validate a comprehensive and innovative, road-network asset inventory software application Segments l x w x h LiDAR (GBs) Process Knowledge Road, curvature... Sign posts, lamps.. Bridges, fences...
NRA Fellowship Application The central aim of this project is to design, construct and validate a comprehensive and innovative, road-network asset inventory software application that enables LiDAR and LiDAR/image data to be automatically and semi-automatically processed LiDAR (GBs) Segments l x w x h Sign Processing Road Processing Knowledge Road, curvature... Sign posts, lamps.. ?
File Preparation workflow Block 1 Geo-referenced Multi-Source Data 1 Block 2 Geo-referenced Multi-Source Data 2 Split / Merge / Join into Geographic Blocks Block 3 Geo-referenced Multi-Source Data 3 . . . . . . Geo-referenced Multi-Source Data N Block N
File Preparation workflow Block 1 Processing Knowledge Block 2 + Block 3 . . . Block N +
What are  you left with? LiDAR folder Block 1 Block 1 Block 1 Block 2 Block 2 Block 2 Survey 10 Apr Survey 5 Dec Survey 2 May Block 3 Block 3 Block 3 . . . . . . . . . ....... Block N Block N Block N MetaData: Geo Bounds, date, processing done MetaData: Geo Bounds, date, processing done MetaData: Geo Bounds, date, processing done
Question Give me all the lidar data in dublin 4? LiDAR folder
Question Give me all the lidar data in dublin 4 between December 2009 and 2010? LiDAR folder
Question Give me only the aerial DEMs created in Dublin 4? LiDAR folder
Question New aerial MMS data has been collected, select the relevant terrestrial MMS data and refine the results from previous algorithms. LiDAR folder
Workflow solutions This is why there are company’s who specialise in developing workflow solutions.
Our Solution LIDAR data Imagery .......
Our Solution LIDAR data Imagery ....... GIS  DB GIS  DB
Our Solution LIDAR data Imagery ....... Spatial Query GIS  DB GIS  DB
Our Solution LIDAR data Imagery ....... Spatial Query GIS  DB GIS  DB Cam 1 Cam 2 Point Cloud
Our Solution LIDAR data Imagery ....... Spatial Query GIS  DB GIS  DB Cam 1 Cam 2 Point Cloud } Data Fusion / Processing
Our Solution LIDAR data Imagery ....... Spatial Query GIS  DB GIS  DB Cam 1 Cam 2 Point Cloud } Road Data Info Data Fusion / Processing
Our Solution LIDAR data Imagery ....... Spatial Query GIS  DB GIS  DB Cam 1 Cam 2 Point Cloud } Road Data Info Data Fusion / Processing Visualisation
Video – Web spatial query
Demo – Desktop navigation
Demo – 2D query
Demo – 3D query Load 3D data for pole extraction
Outline ,[object Object]
 EuRSI
 Road Edge Extraction
 Pole Detection and Extraction
 Resolution Effect of speed on scan lines
 Active Contours
 Publications
 Conclusion,[object Object],[object Object]
What are we trying to do? n ,[object Object],LiDAR point cloud Processing + + Imagery
What are we trying to do? n ,[object Object],Road Surface LiDAR point cloud Processing + + Imagery
What are we trying to do? n ,[object Object]
Once we have converted the point cloud data into a surface, we can start extracting the geometrical properties of the road.Road Surface Road Geometry LiDAR point cloud Processing Centreline / Width ... + + Crossfall Grade / Curvature Imagery
What are we trying to do? semi- n ,[object Object]
Once we have converted the point cloud data into a surface, we can start extracting the geometrical properties of the road.Road Surface Road Geometry Processing LiDAR point cloud Centreline / Width ... + + + Crossfall Grade / Curvature Imagery
What have we achieved so far? ,[object Object],Road Surface Processing LiDAR point cloud + + + Imagery
What have we achieved so far? ,[object Object]
 It takes as input only the LiDAR point cloud.Road Surface Processing LiDAR point cloud + + + Imagery
What have we achieved so far? ,[object Object]
 It takes as input only the LiDAR point cloud.
 There is no manual stages to date.Road Surface Processing LiDAR point cloud + + + Imagery
What have we achieved so far? ,[object Object]
 It takes as input only the LiDAR point cloud.
 There is no manual stages to date.
 We can extract the left and right edges without bias to the road typeProcessing Road Edges LiDAR point cloud + + + Imagery
What have we achieved so far? ,[object Object]
 It takes as input only the LiDAR point cloud.
 There is no manual stages to date.
 We can extract the left and right edges without bias to the road type
 We will then use this information to extract the road points and calculate its surface.Road Surface Processing Road Edges LiDAR point cloud + + + Imagery
The Big Picture ,[object Object],[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers
The Big Picture ,[object Object]
 Semi-automatic feature extraction	- road edges 	- road surface 	- road geometry 	- roadside signs / poles / trees 	- roadside vegetation 	- roadside features, crash barriers ,[object Object],[object Object]
 EuRSI
 Road Edge Extraction
 Pole Detection and Extraction
 Resolution Effect of speed on scan lines
 Active Contours
 Publications

More Related Content

Viewers also liked

Text independent speaker recognition system
Text independent speaker recognition systemText independent speaker recognition system
Text independent speaker recognition system
Deepesh Lekhak
 
Automatic Speaker Recognition system using MFCC and VQ approach
Automatic Speaker Recognition system using MFCC and VQ approachAutomatic Speaker Recognition system using MFCC and VQ approach
Automatic Speaker Recognition system using MFCC and VQ approach
Abdullah al Mamun
 
Track 1 session 1 - st dev con 2016 - contextual awareness
Track 1   session 1 - st dev con 2016 - contextual awarenessTrack 1   session 1 - st dev con 2016 - contextual awareness
Track 1 session 1 - st dev con 2016 - contextual awareness
ST_World
 
Module15: Sliding Windows Protocol and Error Control
Module15: Sliding Windows Protocol and Error Control Module15: Sliding Windows Protocol and Error Control
Module15: Sliding Windows Protocol and Error Control
gondwe Ben
 
Track 2 session 1 - st dev con 2016 - avnet - making things real
Track 2   session 1 - st dev con 2016 - avnet - making things realTrack 2   session 1 - st dev con 2016 - avnet - making things real
Track 2 session 1 - st dev con 2016 - avnet - making things real
ST_World
 
Digital Image Processing
Digital Image ProcessingDigital Image Processing
Digital Image Processing
Sahil Biswas
 

Viewers also liked (6)

Text independent speaker recognition system
Text independent speaker recognition systemText independent speaker recognition system
Text independent speaker recognition system
 
Automatic Speaker Recognition system using MFCC and VQ approach
Automatic Speaker Recognition system using MFCC and VQ approachAutomatic Speaker Recognition system using MFCC and VQ approach
Automatic Speaker Recognition system using MFCC and VQ approach
 
Track 1 session 1 - st dev con 2016 - contextual awareness
Track 1   session 1 - st dev con 2016 - contextual awarenessTrack 1   session 1 - st dev con 2016 - contextual awareness
Track 1 session 1 - st dev con 2016 - contextual awareness
 
Module15: Sliding Windows Protocol and Error Control
Module15: Sliding Windows Protocol and Error Control Module15: Sliding Windows Protocol and Error Control
Module15: Sliding Windows Protocol and Error Control
 
Track 2 session 1 - st dev con 2016 - avnet - making things real
Track 2   session 1 - st dev con 2016 - avnet - making things realTrack 2   session 1 - st dev con 2016 - avnet - making things real
Track 2 session 1 - st dev con 2016 - avnet - making things real
 
Digital Image Processing
Digital Image ProcessingDigital Image Processing
Digital Image Processing
 

Similar to LiDAR processing for road network asset inventory

Webinar1 darpa07
Webinar1 darpa07Webinar1 darpa07
Webinar1 darpa07
MKGJUICE
 
Automatic Road Extraction from Airborne LiDAR : A Review
Automatic Road Extraction from Airborne LiDAR : A ReviewAutomatic Road Extraction from Airborne LiDAR : A Review
Automatic Road Extraction from Airborne LiDAR : A Review
IJERA Editor
 
fyp presentation of group 43011 final.pptx
fyp presentation of group 43011 final.pptxfyp presentation of group 43011 final.pptx
fyp presentation of group 43011 final.pptx
IIEE - NEDUET
 
Automated Vehicle (Google Car)
Automated Vehicle (Google Car)Automated Vehicle (Google Car)
Automated Vehicle (Google Car)
sohaildanish
 
How to bring the real world into CarSim
How to bring the real world into CarSimHow to bring the real world into CarSim
How to bring the real world into CarSim
Henning Lategahn
 
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
NanubalaDhruvan
 
Maps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
Maps for Autonomous Driving - it-symposium.ruhr 2019 BochumMaps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
Maps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
Richard Süselbeck
 
Icst 2012 pres
Icst 2012 presIcst 2012 pres
Icst 2012 pres
Arpan Pal
 
2016-1B-Nune
2016-1B-Nune2016-1B-Nune
2016-1B-Nune
Rakesh Nune
 
StevesDWESlide_exported
StevesDWESlide_exportedStevesDWESlide_exported
StevesDWESlide_exported
Steve Snow
 
Model autonomous car
Model autonomous carModel autonomous car
Model autonomous car
GowthamSureshKumar1
 
Autonomouscar
Autonomouscar Autonomouscar
Autonomouscar
Manikanta Chinna
 
Autonomous Driving Lab - Simultaneous Localization and Mapping WP
Autonomous Driving Lab - Simultaneous Localization and Mapping WPAutonomous Driving Lab - Simultaneous Localization and Mapping WP
Autonomous Driving Lab - Simultaneous Localization and Mapping WP
Dmytro Fishman
 
Технологии беспилотных автомобилей
Технологии беспилотных автомобилейТехнологии беспилотных автомобилей
Технологии беспилотных автомобилей
Vitebsk DSC
 
An autonomous driverless car
An autonomous driverless carAn autonomous driverless car
An autonomous driverless car
Alexander Decker
 
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
IT Arena
 
GOOGLE DRIVERLESS CAR
GOOGLE DRIVERLESS CAR GOOGLE DRIVERLESS CAR
GOOGLE DRIVERLESS CAR
Shubham Chaugale
 
A017430110
A017430110A017430110
A017430110
IOSR Journals
 
3D Perception for Autonomous Driving - Datasets and Algorithms -
3D Perception for Autonomous Driving - Datasets and Algorithms -3D Perception for Autonomous Driving - Datasets and Algorithms -
3D Perception for Autonomous Driving - Datasets and Algorithms -
Kazuyuki Miyazawa
 
Civil Maps TechLab Demo Day 2017
Civil Maps TechLab Demo Day 2017Civil Maps TechLab Demo Day 2017
Civil Maps TechLab Demo Day 2017
Civil Maps
 

Similar to LiDAR processing for road network asset inventory (20)

Webinar1 darpa07
Webinar1 darpa07Webinar1 darpa07
Webinar1 darpa07
 
Automatic Road Extraction from Airborne LiDAR : A Review
Automatic Road Extraction from Airborne LiDAR : A ReviewAutomatic Road Extraction from Airborne LiDAR : A Review
Automatic Road Extraction from Airborne LiDAR : A Review
 
fyp presentation of group 43011 final.pptx
fyp presentation of group 43011 final.pptxfyp presentation of group 43011 final.pptx
fyp presentation of group 43011 final.pptx
 
Automated Vehicle (Google Car)
Automated Vehicle (Google Car)Automated Vehicle (Google Car)
Automated Vehicle (Google Car)
 
How to bring the real world into CarSim
How to bring the real world into CarSimHow to bring the real world into CarSim
How to bring the real world into CarSim
 
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2
 
Maps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
Maps for Autonomous Driving - it-symposium.ruhr 2019 BochumMaps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
Maps for Autonomous Driving - it-symposium.ruhr 2019 Bochum
 
Icst 2012 pres
Icst 2012 presIcst 2012 pres
Icst 2012 pres
 
2016-1B-Nune
2016-1B-Nune2016-1B-Nune
2016-1B-Nune
 
StevesDWESlide_exported
StevesDWESlide_exportedStevesDWESlide_exported
StevesDWESlide_exported
 
Model autonomous car
Model autonomous carModel autonomous car
Model autonomous car
 
Autonomouscar
Autonomouscar Autonomouscar
Autonomouscar
 
Autonomous Driving Lab - Simultaneous Localization and Mapping WP
Autonomous Driving Lab - Simultaneous Localization and Mapping WPAutonomous Driving Lab - Simultaneous Localization and Mapping WP
Autonomous Driving Lab - Simultaneous Localization and Mapping WP
 
Технологии беспилотных автомобилей
Технологии беспилотных автомобилейТехнологии беспилотных автомобилей
Технологии беспилотных автомобилей
 
An autonomous driverless car
An autonomous driverless carAn autonomous driverless car
An autonomous driverless car
 
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...
 
GOOGLE DRIVERLESS CAR
GOOGLE DRIVERLESS CAR GOOGLE DRIVERLESS CAR
GOOGLE DRIVERLESS CAR
 
A017430110
A017430110A017430110
A017430110
 
3D Perception for Autonomous Driving - Datasets and Algorithms -
3D Perception for Autonomous Driving - Datasets and Algorithms -3D Perception for Autonomous Driving - Datasets and Algorithms -
3D Perception for Autonomous Driving - Datasets and Algorithms -
 
Civil Maps TechLab Demo Day 2017
Civil Maps TechLab Demo Day 2017Civil Maps TechLab Demo Day 2017
Civil Maps TechLab Demo Day 2017
 

More from Conor Mc Elhinney

Presenting - Why we switch off
Presenting - Why we switch offPresenting - Why we switch off
Presenting - Why we switch off
Conor Mc Elhinney
 
Mobile Mapping Spatial Database Framework
Mobile Mapping Spatial Database FrameworkMobile Mapping Spatial Database Framework
Mobile Mapping Spatial Database Framework
Conor Mc Elhinney
 
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extractionGeo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Conor Mc Elhinney
 
Multi-thematic spatial databases
Multi-thematic spatial databasesMulti-thematic spatial databases
Multi-thematic spatial databases
Conor Mc Elhinney
 
Digital Hologram Image Processing
Digital Hologram Image ProcessingDigital Hologram Image Processing
Digital Hologram Image Processing
Conor Mc Elhinney
 
Focused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holographyFocused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holography
Conor Mc Elhinney
 
Digital Holography
Digital HolographyDigital Holography
Digital Holography
Conor Mc Elhinney
 
Initial results from EuRSI project
Initial results from EuRSI projectInitial results from EuRSI project
Initial results from EuRSI project
Conor Mc Elhinney
 
Digital Hologram Image Processing
Digital Hologram Image ProcessingDigital Hologram Image Processing
Digital Hologram Image Processing
Conor Mc Elhinney
 

More from Conor Mc Elhinney (9)

Presenting - Why we switch off
Presenting - Why we switch offPresenting - Why we switch off
Presenting - Why we switch off
 
Mobile Mapping Spatial Database Framework
Mobile Mapping Spatial Database FrameworkMobile Mapping Spatial Database Framework
Mobile Mapping Spatial Database Framework
 
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extractionGeo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
 
Multi-thematic spatial databases
Multi-thematic spatial databasesMulti-thematic spatial databases
Multi-thematic spatial databases
 
Digital Hologram Image Processing
Digital Hologram Image ProcessingDigital Hologram Image Processing
Digital Hologram Image Processing
 
Focused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holographyFocused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holography
 
Digital Holography
Digital HolographyDigital Holography
Digital Holography
 
Initial results from EuRSI project
Initial results from EuRSI projectInitial results from EuRSI project
Initial results from EuRSI project
 
Digital Hologram Image Processing
Digital Hologram Image ProcessingDigital Hologram Image Processing
Digital Hologram Image Processing
 

Recently uploaded

Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
Tatiana Kojar
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
MichaelKnudsen27
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Edge AI and Vision Alliance
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Fwdays
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Alex Pruden
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Neo4j
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Pitangent Analytics & Technology Solutions Pvt. Ltd
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
Ivo Velitchkov
 

Recently uploaded (20)

Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
 

LiDAR processing for road network asset inventory