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Garuda Robotics x DataScience SG Meetup (Sep 2015)

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Eugene Yan Ziyou
Eugene Yan Ziyou

What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer “selfie” drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data. In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges.

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Drones At Work :: Capturing Data, Generating Insights, Solving Real Problems Ong Jiin Joo CTO, Garuda Robotics jiinjoo@garuda.io DSSG - 23/9/2015
Drones at work gathering useful data   (1)  Not  flying  for  fun   (2)  Not  flying  and  shoo5ng  for  aesthe5cs     Drones  at  work:     (1)  Solve  Customer’s  Problems   (2)  Gather  Useful  Data:   Can  be  analyzed  to  produce  intelligence  /  insights    
In the next 45 minutes •  Share our experience –  Behind the scenes –  Technology and processes •  Data capture workflow •  Data analysis workflow •  Precision agriculture case study: Tree counting
Case Study Background Running  example  in  this  presenta1on:  Precision  Agriculture  for  Palm  Oil  Planta5ons     Planta1on  customers  want  to  know:  How  many  trees  are  there  in  my  planta5on?   This  affects:   (i)  Manpower  &  equipment  planning,  (ii)  fer1lizer  purchase  and  dissemina1on    
In case you haven’t heard of Drones / UAVs …
Our Workflow Data  Acquisi5on  /   Genera5on   Data  Storage  /   Transporta5on   Data  Analy5cs  /   Presenta5on   1   2   3  
Data Acquisition :: Planning Project  Planner   -­‐  Define  objec1ves,  targets,  obstacles,  deliverables    
Data Acquisition :: Before Flight Prepara5on  for  deployment   On-­‐site  equipment  prepara5on    
Data Acquisition :: Before Flight Onsite  systems  prepara5ons   Mission  planning  and  briefing  
Data Acquisition :: During Flight •  Autonomous Flight –  Monitor telemetry, video feed .   HUD  (head  up  display)   Antenna  
Data Acquisition :: After Flight 1.  Check  Data  Integrity   •  Is  the  picture  clear,   focused?   2.  Quick  Process   •  Low  res  img   processing  
Data Generation :: Comparing pictures taken over a period of time
Data Generation :: Combining various electromagnetic spectrum
Data Transportation :: Live
Urgency of analysis •  When do we need the deliverable –  Real Time or within minutes / hours –  Non Real Time (days / weeks) •  Some analysis require huge amount of compute – such as image recognition
Tradeoff between using more bandwidth to transport data elsewhere vs. shipping more compute power on site In-­‐country  Telco   Ground  Sta1on   Drone   Cloud  Services   Wi-­‐Fi   3G  Dongle   Internet   backbone  
Fallback plan – transport the old way
Data Storage Size Photogrammetry Example (simplified) •  Fly at 100m, Camera FOV 90° both sides, 1 picture covers 200x200m = 4 ha •  Suppose plantation 10,000 ha square (or 10km by 10km) •  80% overlap required ~= shooting 5 times same area •  Total size: (10,000/4) * 5MB * 5 = 62.5GB – fits one 64GB SD card.
Data Analytics •  More on this on part 2 of the presentation
Data Presentation :: Image Stitching •  Combine   everything   or  by  blocks   •  Highly   repe11ve   •  Lack  control   points  
Data Presentation :: Orthomosaics •  Geometrically  corrected   •  Can  be  placed  on  map   Used  by  surveyors  to  measure  true  distance  
Data Presentation :: 3D Reconstruction •  Photogrammetry  methods   Similarly,  used  by   surveyors  to  measure   length,  area  and  volume  of   interest  in  3D  space  
DRONE DATA ANALYTICS
Data Analytics Framework Descrip1ve   Analy1cs   Predic1ve   Analy1cs   Prescrip1ve   Analy1cs   +   +  
Descriptive Analytics Example:  Telco  Tower  Inspec5on     •  Is  the  antenna  s1ll  slanted  at  2.8  degrees   from  ver1cal?   •  Any  disconnected  wires,  bird  nest,  damage   from  harsh  weather?   Example:  Flare  Stack  Inspec5on     •  Is  the  structural  integrity  of  the  flare  stack   holding  up?     •  Is  the  flare  stack  opera1ng  at  normal   temperature?  
Predictive Analytics What will happen next? Example: Solar Panel •  What is wrong? •  How many times observed •  Correlate with electricity yield curve
Back to our case study :: Plantation Management Dry  leaves,  but   next  to  river.   Why?   Empty  space,   but  no  palm   planted.  Why?   Winding  road,  difficult  to  bring   harvested  palm  out.  Redo?   Palm  of  mixed  age:  high   maintenance  cost.  Is  it   1me  to  replant  the  en1re   area?     If  so,  should  the  river  be   shi]ed  for  water  to  drain   be^er?  
Case Study :: Plantation Management Great!  Now  I  just  have  to   keep  it  going  for  25  years   How  much  fer1lizers   do  I  need  to  get?     How  should  I  distribute  them  so  that  my   workers  don’t  just  throw  excess  away?   How  many  trees   do  I  have!?  
Tree Counting •  Conventional way(s) –  Ground staff count them one by one! –  Guesstimate (e.g. 143 trees / ha)
Tree Counting The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. •  More advanced ways 1.  Satellite imagery 2.  Drone imagery 3.  Apply Computer vision
Tree Counting Posi1ve  Features   Histogram  of     •  Colour   •  Intensity   •  Mean   •  Standard   Devia1on   Nega1ve   Features   (random   sampling)   Our  naïve  model!  RFC  
Tree Counting •  Didn’t work so well… How  can  we  do  beZer?  
Ways to improve tree counting •  Non-CV techniques –  Operations: capture trees at same size and light intensity (vary altitude, time of flight etc.) –  Domain info: planting patterns, tree distance, max tree per block –  Past data: information from previous flights, manual count, last count •  How about CV techniques?
Ways to improve tree counting Source:    Oil  Palm  Tree  Detec2on  with  High  Resolu2on  Mul2-­‐Spectral  Satellite  Imagery      h?p://www.mdpi.com/2072-­‐4292/6/10/9749?trendmd-­‐shared=0        13  April  2014  
Ways to improve tree counting Active research area •  Some new proposals •  Undergoing R&D and trials with our corpus •  Trials with customer with existing data about their tree count
Tree Counting :: Next Steps •  Impact from good tree count –  Yield prediction and correction –  Plantation ops –  Prescriptive Analytics together with Arborists •  Next things to classify –  Healthy trees vs. sick trees –  Other trees / crops –  Heterogeneous plantations
Summary •  Drones are already at work delivering actionable insights •  We can capture the data with our drones, but the challenge is to go beyond the descriptive into the predictive and prescriptive analytics •  Lots of opportunities coming soon
Thank You

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Garuda Robotics x DataScience SG Meetup (Sep 2015)

  • 1. Drones At Work :: Capturing Data, Generating Insights, Solving Real Problems Ong Jiin Joo CTO, Garuda Robotics jiinjoo@garuda.io DSSG - 23/9/2015
  • 2. Drones at work gathering useful data   (1)  Not  flying  for  fun   (2)  Not  flying  and  shoo5ng  for  aesthe5cs     Drones  at  work:     (1)  Solve  Customer’s  Problems   (2)  Gather  Useful  Data:   Can  be  analyzed  to  produce  intelligence  /  insights    
  • 3. In the next 45 minutes •  Share our experience –  Behind the scenes –  Technology and processes •  Data capture workflow •  Data analysis workflow •  Precision agriculture case study: Tree counting
  • 4. Case Study Background Running  example  in  this  presenta1on:  Precision  Agriculture  for  Palm  Oil  Planta5ons     Planta1on  customers  want  to  know:  How  many  trees  are  there  in  my  planta5on?   This  affects:   (i)  Manpower  &  equipment  planning,  (ii)  fer1lizer  purchase  and  dissemina1on    
  • 5. In case you haven’t heard of Drones / UAVs …
  • 6. Our Workflow Data  Acquisi5on  /   Genera5on   Data  Storage  /   Transporta5on   Data  Analy5cs  /   Presenta5on   1   2   3  
  • 7. Data Acquisition :: Planning Project  Planner   -­‐  Define  objec1ves,  targets,  obstacles,  deliverables    
  • 8. Data Acquisition :: Before Flight Prepara5on  for  deployment   On-­‐site  equipment  prepara5on    
  • 9. Data Acquisition :: Before Flight Onsite  systems  prepara5ons   Mission  planning  and  briefing  
  • 10. Data Acquisition :: During Flight •  Autonomous Flight –  Monitor telemetry, video feed .   HUD  (head  up  display)   Antenna  
  • 11. Data Acquisition :: After Flight 1.  Check  Data  Integrity   •  Is  the  picture  clear,   focused?   2.  Quick  Process   •  Low  res  img   processing  
  • 12. Data Generation :: Comparing pictures taken over a period of time
  • 13. Data Generation :: Combining various electromagnetic spectrum
  • 15. Urgency of analysis •  When do we need the deliverable –  Real Time or within minutes / hours –  Non Real Time (days / weeks) •  Some analysis require huge amount of compute – such as image recognition
  • 16. Tradeoff between using more bandwidth to transport data elsewhere vs. shipping more compute power on site In-­‐country  Telco   Ground  Sta1on   Drone   Cloud  Services   Wi-­‐Fi   3G  Dongle   Internet   backbone  
  • 17. Fallback plan – transport the old way
  • 18. Data Storage Size Photogrammetry Example (simplified) •  Fly at 100m, Camera FOV 90° both sides, 1 picture covers 200x200m = 4 ha •  Suppose plantation 10,000 ha square (or 10km by 10km) •  80% overlap required ~= shooting 5 times same area •  Total size: (10,000/4) * 5MB * 5 = 62.5GB – fits one 64GB SD card.
  • 19. Data Analytics •  More on this on part 2 of the presentation
  • 20. Data Presentation :: Image Stitching •  Combine   everything   or  by  blocks   •  Highly   repe11ve   •  Lack  control   points  
  • 21. Data Presentation :: Orthomosaics •  Geometrically  corrected   •  Can  be  placed  on  map   Used  by  surveyors  to  measure  true  distance  
  • 22. Data Presentation :: 3D Reconstruction •  Photogrammetry  methods   Similarly,  used  by   surveyors  to  measure   length,  area  and  volume  of   interest  in  3D  space  
  • 24. Data Analytics Framework Descrip1ve   Analy1cs   Predic1ve   Analy1cs   Prescrip1ve   Analy1cs   +   +  
  • 25. Descriptive Analytics Example:  Telco  Tower  Inspec5on     •  Is  the  antenna  s1ll  slanted  at  2.8  degrees   from  ver1cal?   •  Any  disconnected  wires,  bird  nest,  damage   from  harsh  weather?   Example:  Flare  Stack  Inspec5on     •  Is  the  structural  integrity  of  the  flare  stack   holding  up?     •  Is  the  flare  stack  opera1ng  at  normal   temperature?  
  • 26. Predictive Analytics What will happen next? Example: Solar Panel •  What is wrong? •  How many times observed •  Correlate with electricity yield curve
  • 27. Back to our case study :: Plantation Management Dry  leaves,  but   next  to  river.   Why?   Empty  space,   but  no  palm   planted.  Why?   Winding  road,  difficult  to  bring   harvested  palm  out.  Redo?   Palm  of  mixed  age:  high   maintenance  cost.  Is  it   1me  to  replant  the  en1re   area?     If  so,  should  the  river  be   shi]ed  for  water  to  drain   be^er?  
  • 28. Case Study :: Plantation Management Great!  Now  I  just  have  to   keep  it  going  for  25  years   How  much  fer1lizers   do  I  need  to  get?     How  should  I  distribute  them  so  that  my   workers  don’t  just  throw  excess  away?   How  many  trees   do  I  have!?  
  • 29. Tree Counting •  Conventional way(s) –  Ground staff count them one by one! –  Guesstimate (e.g. 143 trees / ha)
  • 30. Tree Counting The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. •  More advanced ways 1.  Satellite imagery 2.  Drone imagery 3.  Apply Computer vision
  • 31. Tree Counting Posi1ve  Features   Histogram  of     •  Colour   •  Intensity   •  Mean   •  Standard   Devia1on   Nega1ve   Features   (random   sampling)   Our  naïve  model!  RFC  
  • 32. Tree Counting •  Didn’t work so well… How  can  we  do  beZer?  
  • 33. Ways to improve tree counting •  Non-CV techniques –  Operations: capture trees at same size and light intensity (vary altitude, time of flight etc.) –  Domain info: planting patterns, tree distance, max tree per block –  Past data: information from previous flights, manual count, last count •  How about CV techniques?
  • 34. Ways to improve tree counting Source:    Oil  Palm  Tree  Detec2on  with  High  Resolu2on  Mul2-­‐Spectral  Satellite  Imagery      h?p://www.mdpi.com/2072-­‐4292/6/10/9749?trendmd-­‐shared=0        13  April  2014  
  • 35. Ways to improve tree counting Active research area •  Some new proposals •  Undergoing R&D and trials with our corpus •  Trials with customer with existing data about their tree count
  • 36. Tree Counting :: Next Steps •  Impact from good tree count –  Yield prediction and correction –  Plantation ops –  Prescriptive Analytics together with Arborists •  Next things to classify –  Healthy trees vs. sick trees –  Other trees / crops –  Heterogeneous plantations
  • 37. Summary •  Drones are already at work delivering actionable insights •  We can capture the data with our drones, but the challenge is to go beyond the descriptive into the predictive and prescriptive analytics •  Lots of opportunities coming soon