Synthetic data for remote sensing imagery could be applied to just about any industry/business that is attempting to use AI to extract insights from satellite/aerial imagery. The use cases that we've seen include using synthetic data to train or validate algorithms to: - identify rare and unusual objects, including economically interesting structures such as construction cranes - classify land area types - classify/characterize roof-top damage or assets - extract building footprints - identify environmental damage - experimenting with training algorithms on new sensors to detect and classify items There really are unlimited computer vision use cases in remote sensing to which synthetic data could be applied. Feel free to contact us at sales@rendered.ai or follow one of the links above!
This is amazing. Can you clarify why a business would want this data? What is the case-use (i.e. your Crane truck example)
Synthetic data for remote sensing imagery could be applied to just about any industry/business that is attempting to use AI to extract insights from satellite/aerial imagery.
The use cases that we've seen include using synthetic data to train or validate algorithms to:
- identify rare and unusual objects, including economically interesting structures such as construction cranes
- classify land area types
- classify/characterize roof-top damage or assets
- extract building footprints
- identify environmental damage
- experimenting with training algorithms on new sensors to detect and classify items
There really are unlimited computer vision use cases in remote sensing to which synthetic data could be applied.
Feel free to contact us at sales@rendered.ai or follow one of the links above!