Special Issue "Application of Multitemporal Remote Sensing in Land-Cover/Land-Use Change"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 20 August 2023 | Viewed by 1522

Special Issue Editors

School of Geography and Information Engineering, Future City Campus, China University of Geosciences, Wuhan 430079, China
Interests: high-resolution remote sensing; deep learning; land-use/land-cover classification, change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Wang
E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: time series change detection; hyperspectral image classification
School of Geodesy and Geomatics, Tongji University, Shanghai 200082, China
Interests: multitemporal data analysis and processing; change detection; spectral signal processing; information fusion; multispectral/hyperspectral images processing; remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the change in the natural environment and the effect of human activities, the Earth's surface changes all the time. Land use and land cover changes are the leading cause of environmental changes worldwide. In order to ensure the sustainable development of the environment and humans, monitoring changes with massive and multi-source remote sensing data has become an important application in the remote sensing field. Earth observation satellites collect hundreds of terabytes of remote sensing data each year. How to effectively process and analyze these data for the purpose of monitoring land changes is the issue that we want to address.

To this end, this Special Issue seeks papers presenting novel ideas, techniques and tools to improve remote sensing change detection. Potential topics for this Special Issue may include, but are not limited to the following:

  • Multitemporal remote sensing image analysis;
  • Land-cover/ Land-use change analysis;
  • Building change identification;
  • Scene-level classification and change analysis;
  • Multi-source remote sensing data fusion for change detection;
  • Urban change monitoring;
  • Semantic change analysis;
  • Geospatial databases for sustainable smart cities;
  • Advanced algorithms for change detection based on machine learning/deep learning;
  • Applications of remote sensing image change detection (vegetation change/coastline change/etc.) ;

Dr. Qiqi Zhu
Dr. Nan Wang
Dr. Sicong Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • multitemporal analysis
  • land-cover/land-use change analysis
  • remote sensing data fusion
  • urban change monitoring
  • machine learning
  • change detection applications

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Multi-Task Learning for Building Extraction and Change Detection from Remote Sensing Images
Appl. Sci. 2023, 13(2), 1037; https://doi.org/10.3390/app13021037 - 12 Jan 2023
Cited by 1 | Viewed by 1005
Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as [...] Read more.
Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as in other DL-based applications. To better exploit the available labeled training data for representation learning, we propose a multi-task learning (MTL) network for simultaneous BE and CD, comprising the state-of-the-art (SOTA) powerful Swin transformer as a shared backbone network and multiple heads for predicting building labels and changes. Using the popular CD dataset the Wuhan University building change detection dataset (WHU-CD), we benchmarked detailed designs of the MTL network, including backbone and pre-training choices. With a selected optimal setting, the intersection over union (IoU) score was improved from 70 to 81 for the WHU-CD. The experimental results of different settings demonstrated the effectiveness of the proposed MTL method. In particular, we achieved top scores in BE and CD from optical images in the 2021 Gaofen Challenge. Our method also shows transferable performance on an unseen CD dataset, indicating high label efficiency. Full article
Show Figures

Figure 1

Back to TopTop