Computer vision is everywhere! But teaching an algorithm to identify objects requires a lot of data and this is definitely the case when we think about GeoAI
But it is not enough to have a lot of data we also need data that is labeled
If we are looking for cars in images we need a lot of images of cars and we need to know which pixels are the car!
Of course, I am oversimplifying but I hope you get the idea,
Now imagine that you can automatically generate a large labeled data set of realistic images of cars based on the specifications of a specific sensor.
These data sets are often referred to as synthetic data or fake data and to help us understand more about this I have invited Chris Andrews from Rendered AI on the podcast.
Here are a few previous episodes you might find interesting
Computer Vision And GeoAI
https://mapscaping.com/podcast/computer-vision-and-geoai/
In this episode, the discussion is aimed at an increased understanding of the differences between computer vision and the AI that is used in the Earth Observation world.
Labels Matter
https://mapscaping.com/podcast/labels-matter/
What it takes to create labeled training data manually. If you are new to the idea of labeled data sets this is a good place to start.
Fake Satellite Imagery
https://mapscaping.com/podcast/fake-satellite-imagery/
This is a good episode if you want to know more about Generative AI and Generative Adversarial Networks.
Also, check out this website https://thisxdoesnotexist.com/ to get an idea of where and how these Generative Adversarial Networks can be used. Look for a website called This City Does Not Exist http://thiscitydoesnotexist.com/
On a silently similar note try uploading an image to https://bard.google.com/ … it's pretty interesting!
A marketplace for geospatial data and workflows
Geospatial visualisations matter - Civil engineering meets online gaming
Using the geomagnetic field of buildings to navigate indoors
The location context platform
Geospatial gives context to risk
Inspirational geospatial visualizations
Geospatial support for humanitarian emergencies
Crowd source clean the planet - a geospatial approach
Highways in the sky, risk islands and drone flight paths
Immersive maps give context to data - this is Street View for rivers
The Google Drive of geospatial - curation over creation
A geospatial story and some housekeeping
Mapping the way we relate to the urban environment
Creating Global Activity Specific Maps
Positioning as a service and the role of smartphones in the future of geolocation
A diary of your location - recording your travel journeys using GPS tracks
Scaling map data generation using computer vision
A mapping platform for your outdoor adventures
Tracking global air traffic in real time
Translating between machine and human when talking about location
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