Inversion of 2D Remote Sensing Data to 3D Volumetric Models Using Deep Dimensionality Exchange

In a recent experiment, we used an uncommon convolutional architecture to invert multi-channel 2D surface-acquired remote sensing data into 3D volumetric models. The traditional deterministic process to perform this inversion often takes months of computing time. We’re able to decrease it to tens of milliseconds using our deep learning model. This experiment exposed some fascinating analytical methodologies through empirical exploration, namely, an interesting embedding strategy using eigenvector decomposition at feature engineering time, a brute force conversion from 2D to 3D information in latent space, and a clever but exhaustive investigation through several model architectures including generative and fully convolutional models. Join me for a description of the use case, a detailed technical walkthrough of the methods used to solve the problem, and a demonstration of the system on physically realistic synthetic data. I’ll describe the model architectures which failed, explain the most successful model a