How to Handle the Luxury of Having Too Much Training Data

Explore strategies for managing an abundance of training data in AI model development for automatic mapping of objects from aerial imagery. Learn how to create a well-balanced dataset by selectively choosing relevant examples, focusing on diverse building types while excluding non-relevant data like oceans, forests, and roads. Discover techniques for dynamically selecting and producing training data during model training and evaluation to develop robust AI for building detection across Norway. Gain insights into overcoming the challenges of having too much data and optimizing the training process for improved model performance.