TL; DR: With thousands more Earth Observation satellites going up in the next decade, synthetic data will be crucial in training algorithms to turn all of the collected pixels into meaningful data. Rendered.ai is publicly releasing the capability to generate synthetic RGB satellite imagery for rare object detection. To try it for yourself, sign up for the Rendered.ai platform and input the content code ‘SATRDEMO’
Our customers in the Earth Observation space express a great need for the detection of rare objects in satellite imagery. To train deep learning object detection models, thousands or tens of thousands of diverse labeled images are required. Unfortunately, even with the hundreds of satellites capturing terabytes of imagery of the Earth’s surface, the dataset may not be diverse enough to successfully detect objects.
With thousands more Earth Observation satellites going up in the next decade, synthetic data will be crucial in training algorithms to turn all of the collected pixels into meaningful data. Subsequently, we anticipate an even greater demand for our platform for synthetic data generation. With this in mind, we’ve been expanding our expertise in remote sensing synthetic data for AI training and have publicly released the capability to generate synthetic RGB satellite imagery for rare object detection.
Get started generating your own synthetic RGB Satellite Images!
We have released the capabilities of the synthetic RGB Satellite Imagery channel under the content code ‘SATRDEMO’.
- If you are new to the platform, first request access, and then input the content code on the registration page once you are sent a link.
- If you already have an account, you can enter this code in the field labeled ‘Content Code’ when setting up a new workspace.
- For more information on content codes, along with full platform documentation, follow this link.
How does synthetic data improve AI and ML models?
Here at Rendered.ai, we have enabled many customers with the capability of generating diverse physically accurate synthetic datasets to train and validate their artificial intelligence and machine learning models. While using physics-based synthetic data alone improves training and validation, combining physics-based synthetic data with generative domain adaptation techniques provides the best results.
If you’re interested in learning more about how synthetic data is used to improve the training and validation process in AI and ML models and/or you’d like a technical deep dive into our RBG satellite imagery channel:
- Read our blog post on generating synthetic data for aerial object detection with Rendered.ai
- Watch our webinar on this topic
