Can computer vision mimic human vision? Maybe – but we need the right tools to process the high volume of data required by machine learning algorithms. Integration tools like FME can be used to harness the power of geospatial and machine learning for object detection. In this webinar, you will learn how to: - Use the ML libraries exposed in FME for object detection on photos or with remote sensing data (with an Open CV integration) - How to improve the detection results with geospatial analysis - Deliver results to stakeholders with quality outputs like maps, images, or info shared directly to a destination system

1. How to Improve
Computer Vision
with Geospatial Tools
Dmitri Bagh, Safe Software

2. Dmitri Bagh
Scenario Creation Analyst

3. Years of solving data challenges
25
Safe So ware
WHO WE ARE
10,000
Organizations trusting us worldwide
www.safe.com
Partners supporting our network
150
128
Countries with FME customers

5. The Mystery of Balboa Park

6. The Mystery of Balboa Park
The
End

7. Computer Vision (CV)
Uses:
● Medicine
● Surveillance
● Space Exploration
● Autonomous vehicles
● Military
● Manufacturing
● GEOSPATIAL!

9. Object Detection
This is what we are going to talk about

10. Object Detection

11. Object Detection

12. Object Detection. Samples

13. Object Detection. Training

14. Object Detection. Detection

15. CV Tools

16. Connect Your Data Sources Transform Your Data Automate Your Workflows

17. CV tools available in FME
Coming soon...

18. Five stages
of the OD process
1. Data collection
2. Annotation
3. Training
4. Detection
5. Reporting/Result Delivery

19. Use online services
● Lots of specialists behind the service
● A LOT of computational power
● Huge training datasets
● Lots of known objects
● Collection and Training already done
● Only predefined objects
● Requires online connection
● Might get costly
● Surprisingly huge environmental
impact
● You are the specialist
● Limited computational resources for
training
● Small training datasets
● Collection needed
● You decide what to detect
● Works offline
● No cost for detection
Train yourself

20. Data Collection

21. Training Data Collection
Existing datasets (e.g. Stop signs)
Field collection (photos, video)
Digitizing (aerial imagery)
Sample generator

22. Data Collection
● Collect lots of samples (hundreds or thousands)
● Collect even more negative samples.
It’s good to have negatives from the same location
● Use annotations as clippers to create even more negatives

23. Data Collection

24. Annotation

25. Annotation
● opencv_annotation.exe
● Convert digitized rectangles to annotation file with FME
Positive17.jpg 1 344 108 163 163
Positive19.JPG 1 419 58 91 92
Positive02.jpg 1 1948 294 890 824
Positive20.jpg 1 1590 138 306 332
Positive21.JPG 1 64 58 304 338
Positive22.png 2 368 164 31 35 37 129 33 31
Positive23.jpg 1 173 38 175 177
Positive24.jpg 1 517 124 40 40

26. Command line syntax for opencv_annotation.exe
● Don’t get frustrated - it must be precise down to a single slash
● If the path to the utility contains spaces, prefix the whole command with “& “ and then use
quotation marks
PS C:> C:appsFME2019.1pluginsopencvopencv_annotation.exe
-a="c:tempannotations.txt" -i="C:usersdbaghDropbox (Safe Software
Inc.)DmitriRasterRasterObjectDetectorHydrantsdataPositives" -m=1000 -r=4
Annotation

27. Demo 1
Let’s try to annotate a few photos

28. Demo 2
Let’s turn
our positives 👍
into negatives 👎

29. GIS for annotation

30. Demo 3
Let’s make a workspace
for creating annotations
from GIS layers

31. Some lessons learned
● Make sure there are no zeros in the coordinates
● Remove lines with zero entries
● No annotation should go beyond the image extents
● Use relative path in annotation file
● Use both CR and LF at the end of each line
Annotation

32. Training

33. Training. Step 1
RasterObjectDetectorSamplePreparer
● Remember your width and
height.
● Use relative paths for input and
outputs.

34. Training. Step 2
RasterObjectDetectionModelTrainer
● Use width and height from the
previous step
● Use defaults - they are carefully
chosen (unless you know what you
do)
● Be prepared to wait LONG
● Expect a lot of resources taken by
the process
● Re-use intermediate results after
an error
● Clean the Temp folder to start over

35. Demo 4
Let’s prepare our samples for training

36. This is what KNOWLEDGE looks like

37. Detection

38. Detection

39. Demo 5
Let’s try to beat Amazon Rekognition
by setting the best parameters
in RasterObjectDetector

40. Detection. Gallery

41. Detection. Gallery

42. Detection. Gallery

43. Using spatial filters

44. Reporting

45. Reporting
● Simple vector rectangles
depicting objects
● Images with MapnikRasterizer
● Detection Services Demos
Stop signs, Fire Hydrants
● Interactive Web Maps

46. Demo 6
Make an image
with MapnikRasterizer
Make a mosaic of all images

47. Further
reading and
viewing
● FME Does Computer Vision
● How detection works, Vimeo
● How the Haar cascade object
detection works
● What is LBP
● Cascade Classifier Training
● Environmental impact of AI
● “Teachable Machine”, a simple
and fun game of machine
learning

48. Q&A

49. Thank you!
Get a free trial of FME or live chat at safe.com
Email me at: dmitri@safe.com