SlideShare ist ein Scribd-Unternehmen logo
1 von 25
Gigapixel resolution time-lapse imaging for phenological
monitoring of every plant in a landscape.




                                                            http://bit.ly/CBR-1


                           Tim Brown, Postdoctoral Fellow,
                           Borevitz Lab, Australia National University
                           www.borevitzlab.anu.edu.au
To address the environmental and
management challenges of the 21 st century we
need a substantially better understanding of
how ecosystems work.
  – Exponentially more data
  – Better models
  – Long time-series data sets




                                           2
Ecology – Where’s my PCR?
Understanding high-order complexity is hard!
1. The environment is complex and continuous but we typically only
   measure limited data over limited snapshots in time

2.       Difficult and expensive to track change on the ground at high spatial and
         temporal resolution

3.       Long term data sets are rare, particularly of images
     –      Satellite data is good but not high enough resolution for many applications

1.       Hard to maintain field-based research over ecologically meaningful time
         periods with high enough sample size

To understand ecosystems we need to be tracking “everything” in the
    environment at high time-rates for long periods of time.
• What if we could watch every plant in our field
  sites from our desk?
  – Map out population genetics, biotic/abiotic data on
    the landscape
  – Slide back in time and watch any interaction for as
    long as there have been sensors
  – Students start new research projects beginning with
    all the data previously collected at a site

  The technology is here – We need to dream big!

                                                   4
Gigavision: gigapixel timelapse camera
Collaboration between
 Borevitz Lab (U. Chicago, USA) and TimeScience (my company)

The Challenge:
• Build a solar powered, weatherproof gigapixel camera that can record
  daily phenology from every plant in a field area.
Gigapixel Imaging – How it works
                                   The Gigapan and
                                   Gigavision systems
                                   allow you to capture
                                   hundreds or
                                   thousands of
                                   zoomed-in images in
                                   a panorama.

                                   Images are then
                                   “Stitched” into a
                                   seamless panorama.

                                       (Single 15MP image)




                                     Area: ~7ha




                                   The super-high
                                   resolution of the final
                                   panorama lets you
                                   monitor huge
                                   landscape areas in
                                   great detail.



                                     Area: ~1m2
The Gigavision Camera – Specifications
• ~1.5 billion pixels / panorama
• Avg. resolution of ~1 pixel / cm over 7 hectares
    – (~600 million times the resolution of MODIS)
• Open-source - Built with off-the-shelf components
• Cellular (3G) or 802.11g wireless access (160MP “thumbnails”)
• Automated capture up to 1 image / hr
• Solar powered (<15w power consumption)
• ~$30,000 -> could be more like $10-15,000
• “Light-phenotyping” of >500 plants for ~$60/plant
                                           For full specs, see Brown et al. 2012.
                                           (Google: “gigavision chapter”
Camera Field of View (FOV)
Dataset statistics
 • Oct 2009 – Oct 2011
    – 2 Growing seasons (April – Oct)
 • 1-4 panoramas / day (~154 images/panorama)
 • >184,000 individual jpg images captured
 • Processed data = 70 million 200x200pixel images
 • 6TB of space
 • 417 usable noon panoramas




                                                11
Growing seasons




                  12
Image Visualization and Data
  Collection




http://www.gigavision.org
http://bit.ly/GVDemoMovie2012

                          14
Species
• 513 individual plants identified
• 8 prominent species (non grasses)
• Species:
  –   Hoary Puccoon = 344
  –   Unidentified (yet) = 52
  –   Cottonwood = 47
  –   Black Oak = 36
  –   Sand Cherry = 18
  –   Juniper = 9
  –   Wormwood = 3
  –   Pitcher’s Thistle (Endangered) = 2
  –   Marsh Marigold = 2
                                           15
Limiting factor is increasingly software
 • For example:
   – Axis Q-6035e
      •   $4,000 USD
      •   Can run on-board software
      •   ~2 gigapixel image in < 10 min at any focal length
      •   Temp range: -40 to +50C
      •   50W




                                                               16
GigaPan – Low Cost Gigapixel Imaging
GigaPan (non-timelapse)
• $350-$1,000
• Works with any camera
• Great for documentation and low time-resolution monitoring (e.g. monthly, annual)
Example:
• Alta Ski Area Bark Beetle Project
     –   Maura Olivos, Alta Environmental Center
     –   Annual gigapixel survey images
     –   Identify beetle infested trees for removal
     –   Online panoramas:
          http://gigapan.com/galleries/5582/gigapans

•   More examples of gigapans here:
     • http://gigapan.com/profiles/TimeScience

•   GigaPan hardware: http://www.gigapan.com/
Alta Bark Beetle Project – Initial survey path for potential panorama locations




                                                Data collected with EveryTrail smartphone app (http://www.everytrail.com/ )


Panoramas: http://gigapan.org/galleries/6787/gigapans
(1) Collins Weather




                                                                               (2) Baldy Shoulder




                                                                                           (3) Road Shot




                                                                                               (4) Grizzly
Browse all panoramas online here: http://gigapan.org/galleries/5582/gigapans
21
22
23
Project summary statistics

  Site Name                 Approximate Area   Image Resolution               Average Pixel Resolution
                            (Acres)            (Gigapixels)                   (Pixels per square inch)

  Collins Weather Station   697                4.07                           0.93

  Baldy Shoulder            770                8.84                           1.83

  Road Shot                 211                3.26                           2.46

  Grizzly Gulch             188                3.65                           3.1

                                               Total: 23.5 billion px
  TOTALS                    830 acres                                         Avg: ~3.2 px/cm2
                                               Avg: 4.7 billion px




                                                                        (Area Estimates: http://www.earthpoint.us/Shapes.aspx)
Funding sources and many thanks to…
TimeScience / www.time-science.com
• Christopher Zimmermann (Data management, image processing, online interface)

University of Chicago
• Justin Borevitz (U. Chicago, not at ANU)
• Nina Noah, Whitney Panneton (University of Chicago)

GigaPan Systems – http://Gigapan.org

                                                 Download this talk here:
                                                 http://bit.ly/ESA2012

Weitere ähnliche Inhalte

Was ist angesagt?

The Challenge of Inference from Genome to Phenome
The Challenge of Inference from Genome to PhenomeThe Challenge of Inference from Genome to Phenome
The Challenge of Inference from Genome to PhenomeXavier Sirault
 
High throughput phenotyping
High throughput phenotypingHigh throughput phenotyping
High throughput phenotypingAshish Tiwari
 
Affordable field high-throughput phenotyping - some tips
Affordable field high-throughput phenotyping - some tipsAffordable field high-throughput phenotyping - some tips
Affordable field high-throughput phenotyping - some tipsCIMMYT
 
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...CIMMYT
 
Plant Phenotyping, a new scientific discipline to quantify plant traits
Plant Phenotyping, a new scientific discipline to quantify plant traitsPlant Phenotyping, a new scientific discipline to quantify plant traits
Plant Phenotyping, a new scientific discipline to quantify plant traitsNetNexusBrasil
 
DRI Energy Related Projects
DRI Energy Related ProjectsDRI Energy Related Projects
DRI Energy Related ProjectsDRIscience
 
Plant phenotyping platforms
Plant phenotyping platformsPlant phenotyping platforms
Plant phenotyping platformsMichal Slota
 
Sensor-based phenotyping technology facilitates science and breeding
Sensor-based phenotyping technology facilitates science and breeding Sensor-based phenotyping technology facilitates science and breeding
Sensor-based phenotyping technology facilitates science and breeding Marcus Jansen
 
HIGH-THROUGHPUT PHENOTYPING
HIGH-THROUGHPUT PHENOTYPINGHIGH-THROUGHPUT PHENOTYPING
HIGH-THROUGHPUT PHENOTYPINGshikha singh
 
Basic of Remote Sensing
Basic of Remote SensingBasic of Remote Sensing
Basic of Remote Sensinggueste5cfed
 
OptIPuter: Metagenomics at Light Speed
OptIPuter: Metagenomics at Light SpeedOptIPuter: Metagenomics at Light Speed
OptIPuter: Metagenomics at Light SpeedLarry Smarr
 
Artificial Lighting and Plant Stimulation in Winter Months
Artificial Lighting and Plant Stimulation in Winter MonthsArtificial Lighting and Plant Stimulation in Winter Months
Artificial Lighting and Plant Stimulation in Winter MonthsUniversity of Florida
 
Forest monitoring through remote sensing
Forest monitoring through remote sensingForest monitoring through remote sensing
Forest monitoring through remote sensingPritam Kumar Barman
 
Remote Sensing and its Applications in Agriculture
Remote Sensing and its Applications in AgricultureRemote Sensing and its Applications in Agriculture
Remote Sensing and its Applications in AgricultureVikas Kashyap
 
Accelerating Toward the Singularity
Accelerating Toward the SingularityAccelerating Toward the Singularity
Accelerating Toward the SingularityLarry Smarr
 
Assessing stress by using remote sensing
Assessing stress by using remote sensingAssessing stress by using remote sensing
Assessing stress by using remote sensingChongtham Allaylay Devi
 
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...EarthCube
 
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...AMOL SHITOLE
 

Was ist angesagt? (20)

The Challenge of Inference from Genome to Phenome
The Challenge of Inference from Genome to PhenomeThe Challenge of Inference from Genome to Phenome
The Challenge of Inference from Genome to Phenome
 
High throughput phenotyping
High throughput phenotypingHigh throughput phenotyping
High throughput phenotyping
 
Affordable field high-throughput phenotyping - some tips
Affordable field high-throughput phenotyping - some tipsAffordable field high-throughput phenotyping - some tips
Affordable field high-throughput phenotyping - some tips
 
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...
Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Toler...
 
Plant Phenotyping, a new scientific discipline to quantify plant traits
Plant Phenotyping, a new scientific discipline to quantify plant traitsPlant Phenotyping, a new scientific discipline to quantify plant traits
Plant Phenotyping, a new scientific discipline to quantify plant traits
 
DRI Energy Related Projects
DRI Energy Related ProjectsDRI Energy Related Projects
DRI Energy Related Projects
 
Hawaii Pacific GIS Conference 2012: Internet GIS - Watershed Dashboard
Hawaii Pacific GIS Conference 2012: Internet GIS - Watershed DashboardHawaii Pacific GIS Conference 2012: Internet GIS - Watershed Dashboard
Hawaii Pacific GIS Conference 2012: Internet GIS - Watershed Dashboard
 
Plant phenotyping platforms
Plant phenotyping platformsPlant phenotyping platforms
Plant phenotyping platforms
 
Sensor-based phenotyping technology facilitates science and breeding
Sensor-based phenotyping technology facilitates science and breeding Sensor-based phenotyping technology facilitates science and breeding
Sensor-based phenotyping technology facilitates science and breeding
 
Presentation1
Presentation1Presentation1
Presentation1
 
HIGH-THROUGHPUT PHENOTYPING
HIGH-THROUGHPUT PHENOTYPINGHIGH-THROUGHPUT PHENOTYPING
HIGH-THROUGHPUT PHENOTYPING
 
Basic of Remote Sensing
Basic of Remote SensingBasic of Remote Sensing
Basic of Remote Sensing
 
OptIPuter: Metagenomics at Light Speed
OptIPuter: Metagenomics at Light SpeedOptIPuter: Metagenomics at Light Speed
OptIPuter: Metagenomics at Light Speed
 
Artificial Lighting and Plant Stimulation in Winter Months
Artificial Lighting and Plant Stimulation in Winter MonthsArtificial Lighting and Plant Stimulation in Winter Months
Artificial Lighting and Plant Stimulation in Winter Months
 
Forest monitoring through remote sensing
Forest monitoring through remote sensingForest monitoring through remote sensing
Forest monitoring through remote sensing
 
Remote Sensing and its Applications in Agriculture
Remote Sensing and its Applications in AgricultureRemote Sensing and its Applications in Agriculture
Remote Sensing and its Applications in Agriculture
 
Accelerating Toward the Singularity
Accelerating Toward the SingularityAccelerating Toward the Singularity
Accelerating Toward the Singularity
 
Assessing stress by using remote sensing
Assessing stress by using remote sensingAssessing stress by using remote sensing
Assessing stress by using remote sensing
 
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
 
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...
Avs nanotechnology and genetic engineering for plant pathology seminar 2015 a...
 

Ähnlich wie Gigapixel imaging, ESA Australia, Dec 2012

From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...
From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...
From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...TimeScience
 
From pixels to point clouds - Using drones,game engines and virtual reality t...
From pixels to point clouds - Using drones,game engines and virtual reality t...From pixels to point clouds - Using drones,game engines and virtual reality t...
From pixels to point clouds - Using drones,game engines and virtual reality t...ARDC
 
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...TimeScience
 
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...Larry Smarr
 
The 'Rubble of the North' -a solution for modelling the irregular architectur...
The 'Rubble of the North' -a solution for modelling the irregular architectur...The 'Rubble of the North' -a solution for modelling the irregular architectur...
The 'Rubble of the North' -a solution for modelling the irregular architectur...3D ICONS Project
 
Detecting solar farms with deep learning
Detecting solar farms with deep learningDetecting solar farms with deep learning
Detecting solar farms with deep learningJason Brown
 
Mike Warren Keynote
Mike Warren KeynoteMike Warren Keynote
Mike Warren KeynoteData Con LA
 
Integration for Planet Satellite Imagery
Integration for Planet Satellite ImageryIntegration for Planet Satellite Imagery
Integration for Planet Satellite ImagerySafe Software
 
New Applications of SuperNetworks and the Implications for Campus Networks
New Applications of SuperNetworks and the Implications for Campus NetworksNew Applications of SuperNetworks and the Implications for Campus Networks
New Applications of SuperNetworks and the Implications for Campus NetworksLarry Smarr
 
Set My Data Free: High-Performance CI for Data-Intensive Research
Set My Data Free: High-Performance CI for Data-Intensive ResearchSet My Data Free: High-Performance CI for Data-Intensive Research
Set My Data Free: High-Performance CI for Data-Intensive ResearchLarry Smarr
 
Coupling Australia’s Researchers to the Global Innovation Economy
Coupling Australia’s Researchers to the Global Innovation EconomyCoupling Australia’s Researchers to the Global Innovation Economy
Coupling Australia’s Researchers to the Global Innovation EconomyLarry Smarr
 
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...Larry Smarr
 
AI for Social Good - Saving the Planet with Data Science
AI for Social Good - Saving the Planet with Data ScienceAI for Social Good - Saving the Planet with Data Science
AI for Social Good - Saving the Planet with Data ScienceGanes Kesari
 
Terabit Applications: What Are They, What is Needed to Enable Them?
Terabit Applications: What Are They, What is Needed to Enable Them?Terabit Applications: What Are They, What is Needed to Enable Them?
Terabit Applications: What Are They, What is Needed to Enable Them?Larry Smarr
 
Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Eugene Yan Ziyou
 
Petroleum lunch seminar 30.10.2014
Petroleum lunch seminar 30.10.2014Petroleum lunch seminar 30.10.2014
Petroleum lunch seminar 30.10.2014Geodata AS
 
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...Codiax
 

Ähnlich wie Gigapixel imaging, ESA Australia, Dec 2012 (20)

From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...
From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...
From gigapixel timelapse cameras to unmanned aerial vehicles to smartphones: ...
 
From pixels to point clouds - Using drones,game engines and virtual reality t...
From pixels to point clouds - Using drones,game engines and virtual reality t...From pixels to point clouds - Using drones,game engines and virtual reality t...
From pixels to point clouds - Using drones,game engines and virtual reality t...
 
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...
TraitCapture:Open source tools for DIY high throughput Phenomics and NextGen ...
 
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...
An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...
 
The 'Rubble of the North' -a solution for modelling the irregular architectur...
The 'Rubble of the North' -a solution for modelling the irregular architectur...The 'Rubble of the North' -a solution for modelling the irregular architectur...
The 'Rubble of the North' -a solution for modelling the irregular architectur...
 
Remote Sensing applications for phenotyping
Remote Sensing applications for phenotypingRemote Sensing applications for phenotyping
Remote Sensing applications for phenotyping
 
Detecting solar farms with deep learning
Detecting solar farms with deep learningDetecting solar farms with deep learning
Detecting solar farms with deep learning
 
Mike Warren Keynote
Mike Warren KeynoteMike Warren Keynote
Mike Warren Keynote
 
Integration for Planet Satellite Imagery
Integration for Planet Satellite ImageryIntegration for Planet Satellite Imagery
Integration for Planet Satellite Imagery
 
New Applications of SuperNetworks and the Implications for Campus Networks
New Applications of SuperNetworks and the Implications for Campus NetworksNew Applications of SuperNetworks and the Implications for Campus Networks
New Applications of SuperNetworks and the Implications for Campus Networks
 
Set My Data Free: High-Performance CI for Data-Intensive Research
Set My Data Free: High-Performance CI for Data-Intensive ResearchSet My Data Free: High-Performance CI for Data-Intensive Research
Set My Data Free: High-Performance CI for Data-Intensive Research
 
Coupling Australia’s Researchers to the Global Innovation Economy
Coupling Australia’s Researchers to the Global Innovation EconomyCoupling Australia’s Researchers to the Global Innovation Economy
Coupling Australia’s Researchers to the Global Innovation Economy
 
Presentation: Fee & Brigley
Presentation: Fee & BrigleyPresentation: Fee & Brigley
Presentation: Fee & Brigley
 
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...
End-to-end Optical Fiber Cyberinfrastructure for Data-Intensive Research: Imp...
 
AI for Social Good - Saving the Planet with Data Science
AI for Social Good - Saving the Planet with Data ScienceAI for Social Good - Saving the Planet with Data Science
AI for Social Good - Saving the Planet with Data Science
 
Terabit Applications: What Are They, What is Needed to Enable Them?
Terabit Applications: What Are They, What is Needed to Enable Them?Terabit Applications: What Are They, What is Needed to Enable Them?
Terabit Applications: What Are They, What is Needed to Enable Them?
 
Goal andga oct01
Goal andga oct01Goal andga oct01
Goal andga oct01
 
Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)
 
Petroleum lunch seminar 30.10.2014
Petroleum lunch seminar 30.10.2014Petroleum lunch seminar 30.10.2014
Petroleum lunch seminar 30.10.2014
 
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...
Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Sat...
 

Kürzlich hochgeladen

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 

Kürzlich hochgeladen (20)

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 

Gigapixel imaging, ESA Australia, Dec 2012

  • 1. Gigapixel resolution time-lapse imaging for phenological monitoring of every plant in a landscape. http://bit.ly/CBR-1 Tim Brown, Postdoctoral Fellow, Borevitz Lab, Australia National University www.borevitzlab.anu.edu.au
  • 2. To address the environmental and management challenges of the 21 st century we need a substantially better understanding of how ecosystems work. – Exponentially more data – Better models – Long time-series data sets 2
  • 3. Ecology – Where’s my PCR? Understanding high-order complexity is hard! 1. The environment is complex and continuous but we typically only measure limited data over limited snapshots in time 2. Difficult and expensive to track change on the ground at high spatial and temporal resolution 3. Long term data sets are rare, particularly of images – Satellite data is good but not high enough resolution for many applications 1. Hard to maintain field-based research over ecologically meaningful time periods with high enough sample size To understand ecosystems we need to be tracking “everything” in the environment at high time-rates for long periods of time.
  • 4. • What if we could watch every plant in our field sites from our desk? – Map out population genetics, biotic/abiotic data on the landscape – Slide back in time and watch any interaction for as long as there have been sensors – Students start new research projects beginning with all the data previously collected at a site The technology is here – We need to dream big! 4
  • 5. Gigavision: gigapixel timelapse camera Collaboration between Borevitz Lab (U. Chicago, USA) and TimeScience (my company) The Challenge: • Build a solar powered, weatherproof gigapixel camera that can record daily phenology from every plant in a field area.
  • 6. Gigapixel Imaging – How it works The Gigapan and Gigavision systems allow you to capture hundreds or thousands of zoomed-in images in a panorama. Images are then “Stitched” into a seamless panorama. (Single 15MP image) Area: ~7ha The super-high resolution of the final panorama lets you monitor huge landscape areas in great detail. Area: ~1m2
  • 7. The Gigavision Camera – Specifications • ~1.5 billion pixels / panorama • Avg. resolution of ~1 pixel / cm over 7 hectares – (~600 million times the resolution of MODIS) • Open-source - Built with off-the-shelf components • Cellular (3G) or 802.11g wireless access (160MP “thumbnails”) • Automated capture up to 1 image / hr • Solar powered (<15w power consumption) • ~$30,000 -> could be more like $10-15,000 • “Light-phenotyping” of >500 plants for ~$60/plant For full specs, see Brown et al. 2012. (Google: “gigavision chapter”
  • 8.
  • 9.
  • 10. Camera Field of View (FOV)
  • 11. Dataset statistics • Oct 2009 – Oct 2011 – 2 Growing seasons (April – Oct) • 1-4 panoramas / day (~154 images/panorama) • >184,000 individual jpg images captured • Processed data = 70 million 200x200pixel images • 6TB of space • 417 usable noon panoramas 11
  • 13. Image Visualization and Data Collection http://www.gigavision.org
  • 15. Species • 513 individual plants identified • 8 prominent species (non grasses) • Species: – Hoary Puccoon = 344 – Unidentified (yet) = 52 – Cottonwood = 47 – Black Oak = 36 – Sand Cherry = 18 – Juniper = 9 – Wormwood = 3 – Pitcher’s Thistle (Endangered) = 2 – Marsh Marigold = 2 15
  • 16. Limiting factor is increasingly software • For example: – Axis Q-6035e • $4,000 USD • Can run on-board software • ~2 gigapixel image in < 10 min at any focal length • Temp range: -40 to +50C • 50W 16
  • 17. GigaPan – Low Cost Gigapixel Imaging GigaPan (non-timelapse) • $350-$1,000 • Works with any camera • Great for documentation and low time-resolution monitoring (e.g. monthly, annual) Example: • Alta Ski Area Bark Beetle Project – Maura Olivos, Alta Environmental Center – Annual gigapixel survey images – Identify beetle infested trees for removal – Online panoramas: http://gigapan.com/galleries/5582/gigapans • More examples of gigapans here: • http://gigapan.com/profiles/TimeScience • GigaPan hardware: http://www.gigapan.com/
  • 18. Alta Bark Beetle Project – Initial survey path for potential panorama locations Data collected with EveryTrail smartphone app (http://www.everytrail.com/ ) Panoramas: http://gigapan.org/galleries/6787/gigapans
  • 19.
  • 20. (1) Collins Weather (2) Baldy Shoulder (3) Road Shot (4) Grizzly Browse all panoramas online here: http://gigapan.org/galleries/5582/gigapans
  • 21. 21
  • 22. 22
  • 23. 23
  • 24. Project summary statistics Site Name Approximate Area Image Resolution Average Pixel Resolution (Acres) (Gigapixels) (Pixels per square inch) Collins Weather Station 697 4.07 0.93 Baldy Shoulder 770 8.84 1.83 Road Shot 211 3.26 2.46 Grizzly Gulch 188 3.65 3.1 Total: 23.5 billion px TOTALS 830 acres Avg: ~3.2 px/cm2 Avg: 4.7 billion px (Area Estimates: http://www.earthpoint.us/Shapes.aspx)
  • 25. Funding sources and many thanks to… TimeScience / www.time-science.com • Christopher Zimmermann (Data management, image processing, online interface) University of Chicago • Justin Borevitz (U. Chicago, not at ANU) • Nina Noah, Whitney Panneton (University of Chicago) GigaPan Systems – http://Gigapan.org Download this talk here: http://bit.ly/ESA2012