J. Searcy

Jacob Searcy, Anish Dulal, Scott Bridgham et al.

Natural climate solutions (NCS) offer an approach to mitigating carbon dioxide (CO2) emissions. However, monitoring the carbon drawdown of ecosystems over large geographic areas remains challenging. Eddy-flux covariance towers provide ground truth for predictive 'upscaling' models derived from satellite products, but many satellites now produce measurements on spatial scales smaller than a flux tower's footprint. We introduce Footprint-Aware Regression (FAR), a first-of-its-kind, deep-learning framework that simultaneously predicts spatial footprints and pixel-level (30 m scale) estimates of carbon flux. FAR is trained on our AMERI-FAR25 dataset which combines 439 site years of tower data with corresponding Landsat scenes. Our model produces high-resolution predictions and achieves R2 = 0.78 when predicting monthly net ecosystem exchange on test sites from a variety of ecosystems.

Jacob A. Searcy, Susan L. Sokolowski

Sizing and fitting of Personal Protective Equipment (PPE) is a critical part of the product creation process; however, traditional methods to do this type of work can be labor intensive and based on limited or non-representative anthropomorphic data. In the case of PPE, a poor fit can jeopardize an individual's health and safety. In this paper we present an unsupervised machine learning algorithm that can identify a representative set of exemplars, individuals that can be utilized by designers as idealized sizing models. The algorithm is based around a Variational Autoencoder (VAE) with a Point-Net inspired encoder and decoder architecture trained on Human point-cloud data obtained from the CEASAR dataset. The learned latent space is then clustered to identify a specified number of sizing groups. We demonstrate this technique on scans of human faces to provide designers of masks and facial coverings a reference set of individuals to test existing mask styles.