Innovative geometric deep learning models for onboard detection of anomalous events

Artificial intelligence (AI) tools based on deep learning (DL) which are proven to be highly successful in many domains from biomedical imaging to natural language processing, are still rarely applied not only for onboard learning of Earth Science processes but for Earth Science data analysis in general. One of the key obstructing challenges here is limited capability of the current modeling tools to efficiently integrate time-dimension into the learning process and to accurately describe multi-scale spatio-temporal variability which is ubiquitous in most Earth Science phenomena. As a result, such DL architectures often cannot reliably, accurately and on time learn many salient time-conditioned characteristics of complex interdependent Earth Science systems, resulting in outdated decisions and requiring frequent model updates. To address these challenges, we propose to fuse two emerging directions in time-aware machine learning, namely, geometric deep learnining (GDL) and topological data analysis (TDA). In particular, GDL offers a systematic framework for learning non-Euclidean objects with a distinct local spatial structure such as exhibited, for instance, by the smoke plumes. As a result, GDL allows us for more flexible modeling of complex interactions among entities in a broad range of Earth Science data structures, including multivariate time series and dynamic networks. In turn, TDA yields us complementary information on the time-conditioned underlying intrinsic Earth Science system organization at multiple scales. The ultimate goal of the project is to develop efficient, systematic, and reliable learning mechanisms for the onboard exploration by explicitly integrating both space and time dimensions into the knowledge representation at multiple spectral and spatial resolutions. Using radiance data from NASA's GeoNEX project [Nemani et al. 2020] and High-End Computing (HEC) systems, we will address the following interlinked tasks: T1. Develop time-aware DL architectures with shape signatures from multiple spectral bands for semi-supervised onboard learning of multi-resolution smoke observations. T2. Detect smoke plumes and other anomalies in multi-resolution observations with time-aware DL with a fully trainable and end-to-end multipersistence module. T3. Investigate the uncertainty in topological detection of smoke plumes. T4. Improve the efficiency of GDL for onboard applications. In addition to developing the novel early-stage technology, topological and geometric DL methods for onboard exploration, we will disseminate all new topological and geometric DL tools in the form of publicly available Python packages. We will maintain all software in a public GitHub repository and use GitHub's built-in issue JIRA system for tracking issues and collaborative software management.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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