The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection

Zhuohong Li; Fangxiao Lu; Hongyan Zhang; Lilin Tu; Jiayi Li; Xin Huang; Caleb Robinson; Nikolay Malkin; Nebojsa Jojic; Pedram Ghamisi; Ronny Hansch; Naoto Yokoya

doi:10.1109/JSTARS.2022.3144318

The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection

Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Lilin Tu, Jiayi Li, Xin Huang, Caleb Robinson, Nikolay Malkin, Nebojsa Jojic, Pedram Ghamisi, Ronny Hansch, Naoto Yokoya^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.

Original language	English
Pages (from-to)	1643-1655
Number of pages	13
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	15
DOIs	https://doi.org/10.1109/JSTARS.2022.3144318
Publication status	Published - 25 Jan 2022

Keywords / Materials (for Non-textual outputs)

convolutional neural networks
deep learning
image analysis and data fusion
land cover change detection
multimodal
random forests
weak supervision

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LiEtalIEEEJSTAEORS2022TheOutcomeOfThe2021Final published version, 7.35 MBLicence: Creative Commons: Attribution (CC-BY)

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Li, Z., Lu, F., Zhang, H., Tu, L., Li, J., Huang, X., Robinson, C., Malkin, N., Jojic, N., Ghamisi, P., Hansch, R., & Yokoya, N. (2022). The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1643-1655. https://doi.org/10.1109/JSTARS.2022.3144318

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title = "The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection",

abstract = "We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.",

keywords = "convolutional neural networks, deep learning, image analysis and data fusion, land cover change detection, multimodal, random forests, weak supervision",

author = "Zhuohong Li and Fangxiao Lu and Hongyan Zhang and Lilin Tu and Jiayi Li and Xin Huang and Caleb Robinson and Nikolay Malkin and Nebojsa Jojic and Pedram Ghamisi and Ronny Hansch and Naoto Yokoya",

year = "2022",

month = jan,

day = "25",

doi = "10.1109/JSTARS.2022.3144318",

language = "English",

volume = "15",

pages = "1643--1655",

journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",

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Li, Z, Lu, F, Zhang, H, Tu, L, Li, J, Huang, X, Robinson, C, Malkin, N, Jojic, N, Ghamisi, P, Hansch, R & Yokoya, N 2022, 'The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1643-1655. https://doi.org/10.1109/JSTARS.2022.3144318

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T1 - The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD

T2 - Multitemporal semantic change detection

AU - Li, Zhuohong

AU - Lu, Fangxiao

AU - Zhang, Hongyan

AU - Tu, Lilin

AU - Li, Jiayi

AU - Huang, Xin

AU - Robinson, Caleb

AU - Malkin, Nikolay

AU - Jojic, Nebojsa

AU - Ghamisi, Pedram

AU - Hansch, Ronny

AU - Yokoya, Naoto

PY - 2022/1/25

Y1 - 2022/1/25

N2 - We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.

AB - We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.

KW - convolutional neural networks

KW - deep learning

KW - image analysis and data fusion

KW - land cover change detection

KW - multimodal

KW - random forests

KW - weak supervision

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JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ER -

The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection

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Keywords / Materials (for Non-textual outputs)

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