Articles | Volume 18, issue 4
Research article
08 Apr 2024
Research article |  | 08 Apr 2024

MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model

Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of egusphere-2023-1297', Anonymous Referee #1, 07 Nov 2023
    • AC1: 'Reply on RC1', Xinwei Chen, 12 Dec 2023
  • RC2: 'Comment on egusphere-2023-1297', Karl Kortum, 20 Nov 2023
    • AC2: 'Reply on RC2', Xinwei Chen, 12 Dec 2023
  • RC3: 'Comment on egusphere-2023-1297', Andreas Stokholm, 05 Dec 2023
    • AC3: 'Reply on RC3', Xinwei Chen, 12 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (10 Jan 2024) by Suman Singha
AR by Xinwei Chen on behalf of the Authors (11 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Feb 2024) by Suman Singha
RR by Karl Kortum (16 Feb 2024)
RR by Andreas Stokholm (22 Feb 2024)
ED: Publish subject to technical corrections (08 Mar 2024) by Suman Singha
AR by Xinwei Chen on behalf of the Authors (09 Mar 2024)  Manuscript 
Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.