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
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
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
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
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
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
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
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