The Outcome of the 2021 IEEE GRSS Data Fusion Contest-Track MSD: Multitemporal Semantic Change Detection

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.1643-1655
Main Authors: Li, Zhuohong, Lu, Fangxiao, Zhang, Hongyan, Tu, Lilin, Li, Jiayi, Huang, Xin, Robinson, Caleb, Malkin, Nikolay, Jojic, Nebojsa, Ghamisi, Pedram, Hansch, Ronny, Yokoya, Naoto
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial satellites
Change detection
Convolutional neural networks
Data integration
deep learning
Detection
Earth
Image analysis
image analysis and data fusion
Image processing
land cover change detection
multimodal
Multisensor fusion
Natural environment
Predictive models
random forests
Remote sensing
Semantics
Vegetation
Vegetation mapping
weak supervision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2022.3144318