CGAN ISALD: Conditional Generative Adversarial Network for Infinite Synthetic Aerial Landscape Data

Machine Learning (ML) is rapidly advancing, but one challenge stands in the way: a lack of high-quality training data. No matter how advanced an algorithm, it cannot learn without good data. ML algorithms require large quantities of data with task-specific metadata, such as data type, subject, and annotations. High-quality, annotated GIS data are notoriously difficult to find. Building usable data sets requires researchers to annotate manually - a timely and costly solution.  VISIMO's CGAN can generate endless synthetic annotated images. After being trained on limited data, it can produce infinite variations and combinations. The model accepts structural details (e.g., roads, buildings, etc.) and constructs realistic images around specific objects. The solution can be expanded to produce variations of seasons, landscapes, or biomes on command, as well as to any type of N-dimensional data.