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Drones At Work :: Capturing Data, Generating Insights,
Solving Real Problems
Ong Jiin Joo CTO, Garuda Robotics
[email protected] 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
