NASA · NASA-846 · record R0451

Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML): A Case Study in Canopy Height Model and Canopy Height Change Prediction

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Here we introduce an updated version of Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), an end-to-end data fusion and artificial intelligence (AI) and machine learning (ML) framework tailored for Earth Observation (EO). MEOW-ML supports the full AI/ML lifecycle, from data preparation to model training and evaluation.

Expected benefits

It speeds up the iterative loops of data processing and model architecture development, and, critically, enables the integration of heterogeneous data sources. A primary intent for MEOW-ML is its application in the design of New Observing Strategies (NOS) which require very large data sets with data of diverse types acquired by a variety of sensors potentially aboard multiple sub-orbital and orbital platforms. MEOW-ML can be used to test various combinations of data types, qualities and resolutions to optimize the design of an NOS.

System outputs

As an example, we present a trained ML model with promising performance using this framework, for predicting forest productivity and degradation over time by using canopy height change (CHC) as a proxy. We conducted modeling at two spatial resolutions to illustrate the potential use of this framework for NOS design activities.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported