CHESS: Coronal Hole Extraction with Semantic Segmentation

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

This project aims at expanding the training of two Convolutional Neural Networks (CNNs) that we have already developed to obtain a more efficient, more accurate, and least-biased CNN model for segmenting coronal holes (CHs). Our two CNNs are based on (i) a U-Net and (ii) a Res-U-Net architecture for Coronal Hole Image Segmentation,with model (ii) currently being more accurate than model (i). These two CNNs have been pre-trained with the coronal hole (CH) boundary data from the Heliophysics Events Knowledgebase (HEK). These initial, pre-training data of the CH boundaries are obtained by the Spatial Possibilistic Clustering Algorithm (SPoCA) applied to images of the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), using the extreme ultraviolet (EUV) 193-Å filter. In many instances, this algorithm cannot differentiate between a CH and another solar structure called ""filament"". Our project will overcome this limitation by adding ground-based observations of the He I 10830 Å spectral line, which is able to provide such disambiguation.

Expected benefits

Improved segmentation of coronal holes with (i) higher accuracy, (ii) better performance, (iii) ready to be implemented in research-to-operations pipelines.

System outputs

Binary masks of coronal holes and co-spatial maps of quantified uncertainties.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

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.

Potential impacts and how they were identified

Not reported