Title (Editor-in-Chief, Managing Editor, or Co-Editors-in-Chief)
Journal Name
Journal Address
Submission Date: Month Day, Year
Dear Dr./Mr./Ms. Editor’s last name or Managing Editor or Editor-in-Chief:
Paragraph 1 [1-2 Sentences]: Introduce the manuscript title under submission with a brief summary of the manuscript’s major point or findings and how they relate to the journal’s aims and scope.
Paragraph 2 [1-3 Sentences]: A statement that the manuscript has neither been previously published nor is under consideration by any other journal. If there are multiple authors, a statement that they have all approved the content of the paper. Occasionally, you might note if you have publicly presented the research elsewhere.
Paragraph 3 [1-2 Sentences]: A thank you for the editor’s time and consideration.
Sincerely,
Your Name
Corresponding Author
Institution Title
Institution/Affiliation Name
Institution Address
Email address
Telephone with country code
Fax, if available with country code
Additional Contact, if the corresponding author is not available for a multi-authored work
Institution Title
Institution/Affiliation Name
Institution Address
Email address
Telephone with country code
Fax, if available with country code
|
Journal Editor’s First and Last Name, Graduate Degree Title: Editor-in-Chief, Managing Editor, or Co-Editors-in-Chief Journal Name Journal Address Submission Date: Month Day, Year Dear Dr./Mr./Ms. Editor’s last name or Managing Editor or Editor-in-Chief: Paragraph 1 [1-2 Sentences]: Introduce the manuscript title under submission with a brief summary of the manuscript’s major point or findings. Paragraph 2 [ 2-3 Sentences]: Explain how the manuscript relates to recent publications in the journal. Paragraph 3 [2-5 Sentences]: Provide context for the research. Explain how the research relates to the journal’s aim and scope. Describe how the manuscript/research appeals to the journal’s audience. Paragraph 4 [1-3 Sentences]: A statement that the manuscript has not been previously published nor is under consideration by any other journal. If there are multiple authors, a statement that they have all approved the content of the paper. Occasionally, you might include if you have publicly presented the research elsewhere. Paragraph 5 [1-2 Sentences]: A selection of reviewers, if requested. Paragraph 6 [1-2 Sentences]: A thank you for the editor’s time and consideration. Sincerely, Your Name Corresponding Author Institution Title Institution/Affiliation Name Institution Address Email address Telephone with country code Fax, if available with country code Additional Contact, if the corresponding author is not available and a multi-authored work Institution Title Institution/Affiliation Name Institution Address Email address Telephone with country code Fax, if available with country code |
Remember, your first draft does not have to be your last. Make sure to get feedback from different readers, especially if this is one of your first publications. It is not uncommon to go through several stages of revisions. Check out the Writing Center’s handout on editing and proofreading and video on proofreading to help with this last stage of writing.
Works consulted
We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.
American Psychological Association. n.d. “Cover Letter.” APA Style. Accessed April 2019. https://apastyle.apa.org/style-grammar-guidelines/research-publication/cover-letters.
Belcher, Wendy Laura. 2009. Writing Your Journal Article in Twelve Weeks: A Guide to Academic Publishing Success . Thousand Oaks, CA: Sage Press.
BioScience Writers (website). 2012. “Writing Cover Letters for Scientific Manuscripts.” September 29, 2012. https://biosciencewriters.com/Writing-Cover-Letters-for-Scientific-Manuscripts.aspx .
Jones, Caryn. n.d. “Writing Effective Cover Letters for Journal Submissions: Tips and a Word Template.” Think Science. Accessed August 2019. https://thinkscience.co.jp/en/articles/writing-journal-cover-letters.html .
Kelsky, Karen. 2013. “How To Write a Journal Article Submission Cover Letter.” The Professor Is In (blog), April 26, 2013. https://theprofessorisin.com/2013/04/26/how-to-write-a-journal-article-submission-cover-letter/ .
Kelsky, Karen. 2013. “Of Cover Letters and Magic (A Follow-up Post).” The Professor Is In (blog), April 29, 2013. http://theprofessorisin.com/2013/04/29/of-cover-letters-and-magic-a-followup-post/ .
Mudrak, Ben. n.d. “Writing a Cover Letter.” AJE . https://www.aje.com/dist/docs/Writing-a-cover-letter-AJE-2015.pdf .
Wordvice. n.d. “How to Write the Best Journal Submission Cover Letter.” Accessed January 2019. https://wordvice.com/journal-submission-cover-letter/ .
You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill
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Part one of our 3-part series on the dos and don’ts of communicating with editors and reviewers.
A good cover letter is a crucial part of the manuscript submission package to Nature Methods . It is not simply an archaic form of communication that is becoming obsolete in a digital world; rather, it should be viewed as an opportunity to convey many important pieces of information about a paper to the editors.
Manuscripts submitted to Nature Methods must first pass an editorial evaluation stage, but as professional editors, we are not experts in every scientific field that the journal covers. Providing context for the paper in a cover letter not only can help the editors reach a quicker decision but also can sometimes tip the balance in favor of sending a borderline paper out for peer review.
Here are some practical tips for potential authors.
- Do give a brief, largely non-technical summary of the method. Explain how it will have an impact and why the method and its applications will be interesting to a broad biological audience. This can include more forward-looking information about potential future applications that authors may be reticent to share with reviewers or readers of their manuscript. Such a summary is especially crucial for highly technical papers, where the chance that the advance may not be fully appreciated by the editors is often higher.
- Do put the work in context. Briefly explain the novelty and the specific advances over previous work but be realistic about what the method can and cannot achieve. Many authors are hesitant to compare their work to previous methods for fear that it will appear to reviewers that they are putting down the contributions of other researchers. But editors may not be aware of the nuances of various approaches to address a methodological problem and are more likely to reject a paper without peer review when the advance over previous work is not clear. Authors should not hesitate to discuss freely in the cover letter why they believe method is an advance (most ideally, backed up with strong performance characteristics in the manuscript!).
- Do suggest referees. If the editors decide to send the paper for peer review, providing a list of potential referees, their email addresses, and a very short description of their expertise, can help the editor assign referees more rapidly. Of course, whether the editor decides to use any of the suggested referees is up to him or her. This is also the place to list researchers that you believe should be excluded from reviewing the paper. (Please note that the names of excluded reviewers should also be included in the relevant field of the online submission form.) The editors will honor your exclusion list as long as you don’t exclude more than five people; if you exclude everyone relevant in a scientific field such that the review process will not be productive or fair, the editor may ask you to shorten the list.
- Do tell us about any related work from your group under consideration or in press elsewhere. Explain how it relates, and include copies of the related manuscripts with your submission.
- Do mention any unusual circumstances. For example, known competition with another group’s paper, co-submission to Nature Methods planned with another group, or co-submission of a related results paper to another NPG journal, etc.
- Do mention if you have previously discussed the work with an editor. As editors, we meet a lot of researchers at conferences and lab visits and many papers are pitched to us. A brief mention of when and where such a conversation occurred can help jog the memory of why we invited the authors to submit it in the first place.
The DON’Ts:
- Don’t simply reiterate that you have submitted a paper to us and/or copy and paste the title and abstract of the paper. The cover letter should be viewed as an opportunity to present useful meta-information about the paper, and not tossed off simply as a submission requirement.
- Don’t go on for pages about what the paper is about and summarize all of your results. The editor will always read the paper itself so long cover letters are usually redundant. A one-page cover letter in almost all cases is sufficient.
- Don’t use highly technical jargon and acronyms. Explaining the advance in a general manner can go a long way in helping the editors reach a quicker decision; cover letters that are largely unreadable are of no help to the editors.
- Don’t overhype or over-interpret. While a description of why the method will advance the field is definitely appreciated, obvious overstatements about the impact or reach of the work do not help and can even reflect poorly on the authors’ judgment of the needs of a field.
- Don’t assume that going on about your scientific reputation or endorsements from others in the field will sway us. This is not pertinent to our editorial decision. Our decisions are based on whether we think the paper will be a good editorial fit for the journal, not on the laurels of the authors or because someone important in the field suggested that they submit the work to Nature Methods
And finally, a minor editorial pet peeve:
- Don’t address your cover letter to “Dear Sir.” This is antiquated language, not to mention often incorrect, given that two-thirds of Nature Methods’ editors are women. Stick to the gender-neutral “Dear Editor” in cases where you are not addressing a specific editor.
Don’t miss parts 2 and 3 of this series of posts covering rebuttal letters and appeal letters . We encourage questions, comments and feedback below. The editors will do their best to answer any questions you have.
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What should be included in a cover letter?
You may be required to submit a cover letter with your submission. Individual journals may have specific requirements regarding the cover letter's contents, so please consult the individual journal's Guide for Authors.
A cover letter is a simple, brief business letter, designed to introduce your manuscript to a prospective Editor. If the Guide for Authors does not specify what to include in your cover letter, you may wish to include some of the following items:
- Specify special considerations that should be given to the paper (if any).
- A brief background regarding the research involved or how the data was collected.
- Details of any previous or concurrent submissions.
- It's also useful to provide the Editor-in-Chief with any information that will support your submission (e.g. original or confirmatory data, relevance, topicality).
- The inclusion (or exclusion) of certain Reviewers (if propose/oppose reviewers isn't an available step in the submission process).
- Bring to the Editor’s attention any Conflict of Interest or Permissions information which may be relevant. Be sure to upload any accompanying forms or declarations as required to your submission.
Please note: When your manuscript is received at Elsevier, it's considered to be in its 'final form' ready to be reviewed, so please check your manuscript carefully before you submit it to the Editor. A guide to the publication process and getting your article published in an Elsevier journal is available on the Elsevier Publishing Campus .
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How to Write a Cover Letter When Submitting Your Research Paper
Albert chan on july 3, 2018 at 12:00 am.
![research journal cover letter research journal cover letter](https://www.dovepress.com/get_image/456)
- Write the cover letter with your institution’s letterhead to demonstrate professionalism and reliability.
- Personalise the cover letter by addressing the journal’s editor by their name.
- State the article type of your manuscript at the beginning of your cover letter (original research article, methodology, case report, etc.)
- Provide the full details of all the authors, including email address and phone number, in your cover letter.
- Explain briefly the research goals and results in one or two sentences.
- Explain the importance of your study: what will the paper’s contribution to the literature be? What impact will the paper have in the research field?
- Tell the editor why you think the study is best suited for the journal, and why the journal’s readers will be interested in the study.
- Declare that the manuscript (in whole or in part) has not been submitted or published in other journals, all authors have read and agreed to the content of the manuscript, you have complied with all ethical and reporting guidelines and have received ethical approval from the relevant committee(s).
- Disclose all potential conflicts of interest (if any).
- Thank the editor for taking the time to read your cover letter and consider your paper for submission.
- Keep the content of the cover letter brief, concise and courteous.
Don’t
- Mention any published literature without citation.
- Provide any personal information which is unrelated to the submission.
- Mention any previous publication records unless it is related to this research.
- Mention any potential professional benefits you may gain from the publication of this work.
- Provide research information that can be found in the paper.
- Copy and paste the abstract and paper content in the cover letter.
- Use complex sentence structures.
- Glorify your past research papers or any of your academic prestige in the cover letter.
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Submitting your manuscript: Write the right cover letter
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It may seem obvious, but a journal editor's first serious impression of a submitted manuscript lies not only with the article title but also, rather simply, with the cover letter . The cover letter is your first "formal" interaction with a journal, and it embodies a request, so to speak, to consider your article for publication. But it also provides you with an excellent opportunity to present the significance of your scientific contribution.
I've worked as an editor for primary research and review manuscripts alike, and despite their many similarities, there are distinctions to writing the cover letter for each. Here are some helpful tips for writing a suitable cover letter for Cell Press scientific journals.
Cover letter basics: What do we look for?
1. Let's start with content. We look for letters that start by succinctly explaining what was previously known in a given field and then state the authors' motivation for wishing to publish. Following that, the conceptual advance , timeliness, and novelty should be immediately conveyed. What sets apart this scientific contribution? What is the significance of the work, and where does the article lead us? Will this research be of interest to a broad readership?
2. Get to the point. We want a concise letter that quickly gets to the main point and the take-home message; this sets the stage for your manuscript. Succinctly explain the topic of discussion, and quickly convey the key conclusions. Do not submit a long dissertation. Generally, one page suffices and is preferred.
3. Do not rehash the abstract of the paper. Copying and pasting the abstract into your cover letter verbatim is a big no-no. Instead, we seek a synthesis of the key points—possibly, and depending on style, the summary might resemble a brief story pitch in an elevator! But importantly, you need to venture beyond the summary: write a sentence that takes you further than the obvious conclusions. How does the content move the field forward? Are the implications far-reaching?
4. Get excited! Authors' excitement about their scientific contributions can undoubtedly inspire the editor who's reading the cover letter. Overall, the sentiment of "you're gonna love reading this paper!" should seep through—make that happen!
5. Include a wish list of reviewers. Relevant information on potential reviewers (including their field of expertise) can be included and is definitely a plus, as it can be quite helpful to the editor. By contrast, please don't provide a long list of excluded reviewers (three maximum), and most certainly do not suggest excluding authors from entire continents on the map! Also, save the editor some time by specifying which author should be the lead contact , and indicate their affiliation.
6. Keep it simple ... and humble. In terms of style, consider sincerity and simplicity . The letter should be humble and forthcoming; don't be ostentatious or florid. Claims of priority, if not fully supported, tend to be a turnoff. In addition, statements indicating that the article or related findings have been presented at X number of conferences and are "tremendously" well received by the scientific community—or otherwise—do not add much to the cover letter. They might instead suggest right off the bat that a lot of cooing and convincing of the journal editor will be required. So let the "science" speak for itself. Also, a statement declaring that the article is original and isn't being considered elsewhere can only add to your cause!
7. Proofread your letter by checking the spelling, grammar, and syntax. A well-written letter indicates that you take your submission seriously and that you are an author who pays attention to detail.
8. Check every detail. Avoid mistakes such as directing the cover letter to the editor(s) of a different journal, or to a different journal altogether. This might suggest that you've submitted your article elsewhere, that it might have been poorly received, and perhaps that the Cell Press journal you're submitting to isn't your first choice. It could also suggest that you don't pay sufficient attention to detail. Sadly, these sorts of errors continue to surprise me and happen more often than I would like.
The cover letter: Primary research or Trends reviews?
There are subtle differences in writing a cover letter for a primary research journal versus a reviews journal, such as the Trends journals at Cell Press.
Many different article formats exist within both the primary research journals and the Trends journals. Make sure it's very clear which type of format you're submitting. As the Editor of Trends in Molecular Medicine , I find that this detail is not always specified by the author(s) in the cover letter. Knowing what type of manuscript you are submitting can help you fully nail down the cover letter in terms of the intent, scope, and take-home message of the article. It also recapitulates your prior agreement with the editor regarding article format: is it a review or an opinion piece?
Along these lines, the content of your cover letter will differ for a review or opinion piece as opposed to an original research contribution. For both, the timeliness and novelty need to strongly come across. However, for a research article, the specific advance relative to previous experimental findings needs to be clearly indicated. For a Trends article, the synthesis and conceptual advance should be particularly stated in terms of what is new and has been trending in the field for the last one to five years. For an opinion piece, take a strong and novel stance on a hypothesis or idea. Projecting into the future, beyond the main take-home message of the paper, is also a strong consideration for Trends articles.
I recommend that you familiarize yourself with the journal that you are submitting to—browse through the journal website and do your homework on author guidelines and the scope of the journal prior to submission! In the case of Trends journals, know who the editor is. Each Trends journal is run by a single editor, so beginning your cover letter with "Dear Madam" when the editor is male, or "Dear Sir" when the editor is female, may not create a favorable impression. While such mistakes are usually overruled by the content and quality of the science, it certainly helps to have your cover letter completely in order!
Keep on writing—we love hearing from you and receiving your submissions! For more tips on writing cover letters for scientific manuscripts, check out this page . Also read more from Cell Press Editor in Chief Emilie Marcus on when—and when not—to submit your paper .
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Posted by Catarina Sacristán Catarina is the Editor of Trends in Molecular Medicine . She received her PhD in immunology from Tufts University, followed by postdoctoral research in Mexico and at NYU. She also did a stint in cardiovascular research at a biomedical engineering firm. She enjoys thinking about immunology, genetics, signaling, imaging, virology, metabolism, neuroscience, cancer, therapeutics, and more. She came to Cell Press from The Journal of Experimental Medicine . A movie buff, she also loves to read, write, ski, horseback ride. and dance.
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Cover letter for your manuscript
A cover letter can be used to help convey a work’s importance to the editors. It should also be used to highlight any potential issues such as related manuscripts currently under consideration in any other Springer Nature publication, as well as indicating whether you have had any prior discussions with a Springer Nature editor about the work described in the manuscript. Please use the cover letter to declare that the manuscript is not currently being considered for publication in any other journal and, if necessary, please include any reviewers you wish to recommend or exclude (including the reasons why). Finally, the cover letter is a good place to include any other issues or anything you were unsure of, that you have encountered whilst submitting your manuscript.
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Cover Letter怎么写?关键点+经典模板请收好!
在投稿过程中,随论文一起提交的cover letter(投稿信)往往是编辑们首先读到的内容。作为学术发表之路上的一块敲门砖,cover letter作用不容小觑——通过在简短的篇幅中快速展示出研究的重点及亮点,帮助编辑快速对论文进行初步评估并建立第一印象。
本期文章,我们将通过一系列关键点详述如何写出一封专业的cover letter,并附上写作模板供研究者参考。快来看看吧!
在称呼编辑的姓名时,请仔细检查,确保完整且正确地书写,避免错误 1 。 如果期刊有多个联合编辑(co-editors),建议根据其专业和职责来具体到个人进行称呼。
如果确实无法找到其具体信息,不妨使用一个通用的称呼,例如“Dear Editors”¹ 。
清晰简明地介绍稿件标题,并说明稿件类型,例如文章(article)、通讯(communication)、综述(review)等。如果期刊有特别说明的要求,则可能需列出全部论文作者。
在介绍完基本信息后,简要解释论文研究内容,包括核心论点、数据收集方法等,让编辑快速、清晰地了解论文内容。建议叙述简短明了,避免繁琐冗长,不必过度重复论文已有的摘要和引言等段落。
说明论文的科研意义是投稿信的重要部分。研究者可以从研究的重要性和潜在影响出发,强调其在该领域的相关性。同时,可以介绍文章与期刊学术范围以及宗旨相契合的方面,以印证投稿的合理性。
部分期刊会要求作者在投稿时提供一系列的声明,以确保论文符合期刊本身和科学出版行业的道德规范。我们建议作者提前查阅各期刊具体的要求,如没有明确说明,可参考以下角度根据实际情况进行声明:
原创性声明(Originality of work)
- 声明文章的原创性,确认其未在他处发表过,以此展示研究成果的独特性。
利益冲突声明 (Conflict of interest statement)
- 针对所有潜在的利益冲突,提供清晰透明的声明:说明不存在可能影响客观性的个人、财务等利益关系;如果存在,则说明潜在冲突的来源和性质。
资金来源声明(Funding source,如适用)
审稿人相关信息(如适用)
如果投稿流程中暂无收集研究者对审稿人建议的环节,且研究者存在对审稿人的特殊要求(如推荐纳入或建议排除特定审稿人等),也可以在投稿信中进行相关说明。
结合以上5点,以下是一封常规投稿信的可用模板,供研究者参考:
![research journal cover letter Example of a Cover Letter](https://cn.scientific-publishing.webshop.elsevier.com/wp-content/uploads/2024/06/Example-of-a-Cover-Letter-800x1017.png)
(此示例意在展示投稿信的格式和语气表达规范,仅供研究者在写作时参考使用 2 。)
作为论文发表的敲门砖,投稿信影响着论文给编辑留下的第一印象。一封优秀的投稿信是文章最终成功发表的重要第一步。
想要写出专业凝练的投稿信?如果您需要语言方面的帮助,爱思唯尔语言服务正是您值得信赖的选择。我们的编辑专家以英文为母语,专业背景涵盖 100+ 学科领域。我们提供多种类型的服务,满足您的不同需求。语言润色高级服务可根据您的论文内容定制投稿信,并提供全方位的论文写作协助。
马上联系我们,让发表省时省力更省心,助力您的学术之旅畅行无虞!
Reference
- Nicholas, D. (2019). How to choose a journal and write a cover letter. Saudi Journal of Anaesthesia, 13(5), 35. https://doi.org/10.4103/sja.sja_691_18
- Loyola University Chicago. (n.d.). JCSHESA Sample Cover Letter. https://ecommons.luc.edu/jcshesa/cover_letter_template.pdf
![research journal cover letter Tone and Structure in Articles](https://cn.scientific-publishing.webshop.elsevier.com/wp-content/uploads/2024/05/Tone-and-Structure-in-Articles-560x420.jpg)
仅有论点还不够?论文写作中别忽视语气和结构
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为重新提交论文手稿写一封有效的投稿信
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Elsevier|金色开放获取模式和绿色开放获取模式的区别
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*Reproduced from Brand et al. (2015), Learned Publishing 28(2), with permission of the authors.
Sample CRediT author statement
Zhang San: Conceptualization, Methodology, Software Priya Singh. : Data curation, Writing- Original draft preparation. Wang Wu : Visualization, Investigation. Jan Jansen : Supervision. : Ajay Kumar : Software, Validation.: Sun Qi: Writing- Reviewing and Editing,
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A systematic literature review and bibliometric analysis of semantic segmentation models in land cover mapping.
![ORCID research journal cover letter](https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1718874496)
1. Introduction
2. materials and methods, 2.1. research questions (rqs).
- RQ1. What are the emerging patterns in land cover mapping?
- RQ2. What are the domain studies of semantic segmentation models in land cover mapping?
- RQ3. What are the data used in semantic segmentation models for land cover mapping?
- RQ4. What are the architecture and performances of semantic segmentation methodologies used in land cover mapping?
2.2. Search Strategy
2.3. study selection criteria, 2.4. eligibility and data analysis, 2.5. data synthesis, 3. results and discussion, 3.1. rq1. what are the emerging patterns in land cover mapping.
- Annual distribution of research studies
- Leading Journals
- Geographic distribution of studies
- Leading Themes and Timelines
3.2. RQ2. What Are Domain Studies of Semantic Segmentation Models in Land Cover Mapping?
- Land Cover Studies
- Precision Agriculture
- Environment
- Coastal Areas
3.3. RQ3. What Are the Data Used in Semantic Segmentation Models for Land Cover Mapping?
- Study Locations
- Data Sources
- Benchmark datasets
3.4. RQ4. What Are the Architecture and Performances of Semantic Segmentation Methodologies Used in Land Cover Mapping?
- Encoder-Decoder based structure
- Transformer-based structure
- Hybrid-based structure
- Performance analysis of semantic segmentation model structures on ISPRS 2-D labelling Potsdam and Vaihingen datasets
- Common experimental training settings
4. Challenges, Future Insights and Directions
4.1. land cover mapping.
- Extracting boundary information
- Generating Precise Land Cover Maps
4.2. Semantic Segmentation Methodologies
- Enhancing deep learning model performance
- Analysis of RS images
- Unlabeled and Imbalance RS data
5. Conclusions
Author contributions, data availability statement, acknowledgments, conflicts of interest, abbreviations.
BANet | Bilateral Awareness Network |
CNN | Convolutional Neural Networks |
DCNN | Deep Convolutional Neural Network |
DEANET | Dual Encoder with Attention Network |
DGFNET | Dual-Gate Fusion Network |
DL | Deep Learning |
DSM | Digital Surface Model |
FCN | Fully Convolutional Networks |
GF-2 | GaoFen-2 |
GF-3 | GaoFen-3 |
GID | GaoFen Image Data |
HFENet | Hierarchical Feature Extraction Network |
HMRT | Hybrid Multi-resolution and Transformer semantic extraction Network |
IEEE | Institute of Electrical and Electronics Engineers |
IoU | Mean Intersection over Union |
ISPRS | International Society for Photogrammetry and Remote Sensing |
LC | Land Cover |
LiDAR | Light Detection and Ranging data |
LoveDA | Land-cOVEr Domain Adaptive |
LULC | Land Use and Land Cover |
MARE | Multi-Attention REsu-Net |
MDPI | Multidisciplinary Digital Publishing Institute |
MIoU | Mean Intersection over Union |
NLP | Natural Language Processing |
OA | Overall Accuracy |
PolSAR | Polarimetric Synthetic Aperture Radar |
RAANET | Residual ASPP with Attention Net |
RQ | Research Question |
RS | Remote Sensing |
RSI | Remote Sensing Imaginary |
SAR | Synthetic Aperture Radar |
SBANet | Semantic Boundary Awareness Network |
SEG-ESRGAN | Segmentation Enhanced Super-Resolution Generative Adversarial Network |
SOCNN | Superpixel-Optimized convolutional neural network |
SOTA | State-Of-The-Art |
UAS | Unmanned Aircraft System |
UAV | Unmanned Aerial Vehicle |
VEDAI | VEhicle Detection in Aerial Imagery |
WHDLD | Wuhan Dense Labeling Dataset |
- Vali, A.; Comai, S.; Matteucci, M. Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sens. 2020 , 12 , 2495. [ Google Scholar ] [ CrossRef ]
- Ma, J.; Wu, L.; Tang, X.; Liu, F.; Zhang, X.; Jiao, L. Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network. Remote Sens. 2020 , 12 , 2350. [ Google Scholar ] [ CrossRef ]
- Pourmohammadi, P.; Adjeroh, D.A.; Strager, M.P.; Farid, Y.Z. Predicting Developed Land Expansion Using Deep Convolutional Neural Networks. Environ. Model. Softw. 2020 , 134 , 104751. [ Google Scholar ] [ CrossRef ]
- Di Pilato, A.; Taggio, N.; Pompili, A.; Iacobellis, M.; Di Florio, A.; Passarelli, D.; Samarelli, S. Deep Learning Approaches to Earth Observation Change Detection. Remote Sens. 2021 , 13 , 4083. [ Google Scholar ] [ CrossRef ]
- Wei, P.; Chai, D.; Lin, T.; Tang, C.; Du, M.; Huang, J. Large-Scale Rice Mapping under Different Years Based on Time-Series Sentinel-1 Images Using Deep Semantic Segmentation Model. ISPRS J. Photogramm. Remote Sens. 2021 , 174 , 198–214. [ Google Scholar ] [ CrossRef ]
- Dal Molin Jr., R.; Rizzoli, P. Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series. Remote Sens. 2022 , 14 , 1381. [ Google Scholar ] [ CrossRef ]
- Sun, Y.; Zhang, X.; Huang, J.; Wang, H.; Xin, Q. Fine-Grained Building Change Detection from Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 8000605. [ Google Scholar ] [ CrossRef ]
- Trenčanová, B.; Proença, V.; Bernardino, A. Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes. Remote Sens. 2022 , 14 , 1262. [ Google Scholar ] [ CrossRef ]
- Zhang, X.; Wang, Z.; Zhang, J.; Wei, A. MSANet: An Improved Semantic Segmentation Method Using Multi-Scale Attention for Remote Sensing Images. Remote Sens. Lett. 2022 , 13 , 1249–1259. [ Google Scholar ] [ CrossRef ]
- Scepanovic, S.; Antropov, O.; Laurila, P.; Rauste, Y.; Ignatenko, V.; Praks, J. Wide-Area Land Cover Mapping with Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 14 , 10357–10374. [ Google Scholar ] [ CrossRef ]
- Guo, Y.; Liu, Y.; Oerlemans, A.; Lao, S.; Wu, S.; Lew, M.S. Deep Learning for Visual Understanding: A Review. Neurocomputing 2016 , 187 , 27–48. [ Google Scholar ] [ CrossRef ]
- Huang, J.; Weng, L.; Chen, B.; Xia, M. DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover. ISPRS Int. J. Geoinf. 2021 , 10 , 125. [ Google Scholar ] [ CrossRef ]
- Chen, S.; Wu, C.; Mukherjee, M.; Zheng, Y. Ha-Mppnet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation. ISPRS Int. J. Geoinf. 2021 , 10 , 672. [ Google Scholar ] [ CrossRef ]
- Hao, S.; Zhou, Y.; Guo, Y. A Brief Survey on Semantic Segmentation with Deep Learning. Neurocomputing 2020 , 406 , 302–321. [ Google Scholar ] [ CrossRef ]
- Chen, G.; Zhang, X.; Wang, Q.; Dai, F.; Gong, Y.; Zhu, K. Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018 , 11 , 1633–1644. [ Google Scholar ] [ CrossRef ]
- Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Munich, Germany, 5–9 October 2015; Volume 9351. [ Google Scholar ]
- Chen, B.; Xia, M.; Huang, J. Mfanet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover. Remote Sens. 2021 , 13 , 731. [ Google Scholar ] [ CrossRef ]
- Weng, L.; Pang, K.; Xia, M.; Lin, H.; Qian, M.; Zhu, C. Sgformer: A Local and Global Features Coupling Network for Semantic Segmentation of Land Cover. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 6812–6824. [ Google Scholar ] [ CrossRef ]
- Wang, L.; Li, R.; Zhang, C.; Fang, S.; Duan, C.; Meng, X.; Atkinson, P.M. UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery. ISPRS J. Photogramm. Remote Sens. 2022 , 190 , 196–214. [ Google Scholar ] [ CrossRef ]
- Xiao, D.; Kang, Z.; Fu, Y.; Li, Z.; Ran, M. Csswin-Unet: A Swin-Unet Network for Semantic Segmentation of Remote Sensing Images by Aggregating Contextual Information and Extracting Spatial Information. Int. J. Remote Sens. 2023 , 44 , 7598–7625. [ Google Scholar ] [ CrossRef ]
- Garcia-Garcia, A.; Orts-Escolano, S.; Oprea, S.; Villena-Martinez, V.; Martinez-Gonzalez, P.; Garcia-Rodriguez, J. A Survey on Deep Learning Techniques for Image and Video Semantic Segmentation. Appl. Soft Comput. J. 2018 , 70 , 41–65. [ Google Scholar ] [ CrossRef ]
- Lateef, F.; Ruichek, Y. Survey on Semantic Segmentation Using Deep Learning Techniques. Neurocomputing 2019 , 338 , 321–348. [ Google Scholar ] [ CrossRef ]
- Yuan, X.; Shi, J.; Gu, L. A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery. Expert. Syst. Appl. 2021 , 169 , 114417. [ Google Scholar ] [ CrossRef ]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021 , 372 , n71. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Manley, K.; Nyelele, C.; Egoh, B.N. A Review of Machine Learning and Big Data Applications in Addressing Ecosystem Service Research Gaps. Ecosyst. Serv. 2022 , 57 , 101478. [ Google Scholar ] [ CrossRef ]
- Tian, T.; Chu, Z.; Hu, Q.; Ma, L. Class-Wise Fully Convolutional Network for Semantic Segmentation of Remote Sensing Images. Remote Sens. 2021 , 13 , 3211. [ Google Scholar ] [ CrossRef ]
- Wan, L.; Tian, Y.; Kang, W.; Ma, L. D-TNet: Category-Awareness Based Difference-Threshold Alternative Learning Network for Remote Sensing Image Change Detection. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5633316. [ Google Scholar ] [ CrossRef ]
- Picon, A.; Bereciartua-Perez, A.; Eguskiza, I.; Romero-Rodriguez, J.; Jimenez-Ruiz, C.J.; Eggers, T.; Klukas, C.; Navarra-Mestre, R. Deep Convolutional Neural Network for Damaged Vegetation Segmentation from RGB Images Based on Virtual NIR-Channel Estimation. Artif. Intell. Agric. 2022 , 6 , 199–210. [ Google Scholar ] [ CrossRef ]
- Zhang, Z.; Huang, X.; Li, J. DWin-HRFormer: A High-Resolution Transformer Model With Directional Windows for Semantic Segmentation of Urban Construction Land. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 5400714. [ Google Scholar ] [ CrossRef ]
- Wang, L.; Li, R.; Wang, D.; Duan, C.; Wang, T.; Meng, X. Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images. Remote Sens. 2021 , 13 , 3065. [ Google Scholar ] [ CrossRef ]
- Akcay, O.; Kinaci, A.C.; Avsar, E.O.; Aydar, U. Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+. ISPRS Int. J. Geoinf. 2022 , 11 , 23. [ Google Scholar ] [ CrossRef ]
- Sun, Z.; Zhou, W.; Ding, C.; Xia, M. Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geoinf. 2022 , 11 , 165. [ Google Scholar ] [ CrossRef ]
- Chen, T.-H.K.; Qiu, C.; Schmitt, M.; Zhu, X.X.; Sabel, C.E.; Prishchepov, A.V. Mapping Horizontal and Vertical Urban Densification in Denmark with Landsat Time-Series from 1985 to 2018: A Semantic Segmentation Solution. Remote Sens. Environ. 2020 , 251 , 112096. [ Google Scholar ] [ CrossRef ]
- Wu, F.; Wang, C.; Zhang, H.; Li, J.; Li, L.; Chen, W.; Zhang, B. Built-up Area Mapping in China from GF-3 SAR Imagery Based on the Framework of Deep Learning. Remote Sens. Environ. 2021 , 262 , 112515. [ Google Scholar ] [ CrossRef ]
- Xu, S.; Zhang, S.; Zeng, J.; Li, T.; Guo, Q.; Jin, S. A Framework for Land Use Scenes Classification Based on Landscape Photos. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 , 13 , 6124–6141. [ Google Scholar ] [ CrossRef ]
- Xu, L.; Shi, S.; Liu, Y.; Zhang, H.; Wang, D.; Zhang, L.; Liang, W.; Chen, H. A Large-Scale Remote Sensing Scene Dataset Construction for Semantic Segmentation. Int. J. Image Data Fusion 2023 , 14 , 299–323. [ Google Scholar ] [ CrossRef ]
- Sirous, A.; Satari, M.; Shahraki, M.M.; Pashayi, M. A Conditional Generative Adversarial Network for Urban Area Classification Using Multi-Source Data. Earth Sci. Inf. 2023 , 16 , 2529–2543. [ Google Scholar ] [ CrossRef ]
- Vasavi, S.; Sri Somagani, H.; Sai, Y. Classification of Buildings from VHR Satellite Images Using Ensemble of U-Net and ResNet. Egypt. J. Remote Sens. Space Sci. 2023 , 26 , 937–953. [ Google Scholar ] [ CrossRef ]
- Kang, J.; Fernandez-Beltran, R.; Sun, X.; Ni, J.; Plaza, A. Deep Learning-Based Building Footprint Extraction with Missing Annotations. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 3002805. [ Google Scholar ] [ CrossRef ]
- Yu, J.; Zeng, P.; Yu, Y.; Yu, H.; Huang, L.; Zhou, D. A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data. Remote Sens. 2022 , 14 , 1128. [ Google Scholar ] [ CrossRef ]
- Wei, P.; Chai, D.; Huang, R.; Peng, D.; Lin, T.; Sha, J.; Sun, W.; Huang, J. Rice Mapping Based on Sentinel-1 Images Using the Coupling of Prior Knowledge and Deep Semantic Segmentation Network: A Case Study in Northeast China from 2019 to 2021. Int. J. Appl. Earth Obs. Geoinf. 2022 , 112 , 102948. [ Google Scholar ] [ CrossRef ]
- Liu, S.; Peng, D.; Zhang, B.; Chen, Z.; Yu, L.; Chen, J.; Pan, Y.; Zheng, S.; Hu, J.; Lou, Z.; et al. The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing. Remote Sens. 2022 , 14 , 893. [ Google Scholar ] [ CrossRef ]
- Bem, P.P.D.; de Carvalho Júnior, O.A.; Carvalho, O.L.F.D.; Gomes, R.A.T.; Guimarāes, R.F.; Pimentel, C.M.M. Irrigated Rice Crop Identification in Southern Brazil Using Convolutional Neural Networks and Sentinel-1 Time Series. Remote Sens. Appl. 2021 , 24 , 100627. [ Google Scholar ] [ CrossRef ]
- Niu, B.; Feng, Q.; Su, S.; Yang, Z.; Zhang, S.; Liu, S.; Wang, J.; Yang, J.; Gong, J. Semantic Segmentation for Plastic-Covered Greenhouses and Plastic-Mulched Farmlands from VHR Imagery. Int. J. Digit. Earth 2023 , 16 , 4553–4572. [ Google Scholar ] [ CrossRef ]
- Sykas, D.; Sdraka, M.; Zografakis, D.; Papoutsis, I. A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022 , 15 , 3323–3339. [ Google Scholar ] [ CrossRef ]
- Descals, A.; Wich, S.; Meijaard, E.; Gaveau, D.L.A.; Peedell, S.; Szantoi, Z. High-Resolution Global Map of Smallholder and Industrial Closed-Canopy Oil Palm Plantations. Earth Syst. Sci. Data 2021 , 13 , 1211–1231. [ Google Scholar ] [ CrossRef ]
- He, J.; Lyu, D.; He, L.; Zhang, Y.; Xu, X.; Yi, H.; Tian, Q.; Liu, B.; Zhang, X. Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows. Remote Sens. 2023 , 15 , 105. [ Google Scholar ] [ CrossRef ]
- Wan, L.; Li, S.; Chen, Y.; He, Z.; Shi, Y. Application of Deep Learning in Land Use Classification for Soil Erosion Using Remote Sensing. Front. Earth Sci. 2022 , 10 , 849531. [ Google Scholar ] [ CrossRef ]
- Cho, A.Y.; Park, S.-E.; Kim, D.-J.; Kim, J.; Li, C.; Song, J. Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 242–253. [ Google Scholar ] [ CrossRef ]
- Bergado, J.R.; Persello, C.; Reinke, K.; Stein, A. Predicting Wildfire Burns from Big Geodata Using Deep Learning. Saf. Sci. 2021 , 140 , 105276. [ Google Scholar ] [ CrossRef ]
- Wang, Z.; Yang, P.; Liang, H.; Zheng, C.; Yin, J.; Tian, Y.; Cui, W. Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sens. 2022 , 14 , 45. [ Google Scholar ] [ CrossRef ]
- Liu, C.-C.; Zhang, Y.-C.; Chen, P.-Y.; Lai, C.-C.; Chen, Y.-H.; Cheng, J.-H.; Ko, M.-H. Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation. Remote Sens. 2019 , 11 , 119. [ Google Scholar ] [ CrossRef ]
- Ji, W.; Chen, Y.; Li, K.; Dai, X. Multicascaded Feature Fusion-Based Deep Learning Network for Local Climate Zone Classification Based on the So2Sat LCZ42 Benchmark Dataset. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 449–467. [ Google Scholar ] [ CrossRef ]
- Ayhan, B.; Kwan, C. Tree, Shrub, and Grass Classification Using Only RGB Images. Remote Sens. 2020 , 12 , 1333. [ Google Scholar ] [ CrossRef ]
- Maxwell, A.E.; Bester, M.S.; Guillen, L.A.; Ramezan, C.A.; Carpinello, D.J.; Fan, Y.; Hartley, F.M.; Maynard, S.M.; Pyron, J.L. Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps. Remote Sens. 2020 , 12 , 4145. [ Google Scholar ] [ CrossRef ]
- Zhou, G.; Xu, J.; Chen, W.; Li, X.; Li, J.; Wang, L. Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 4401220. [ Google Scholar ] [ CrossRef ]
- Lee, S.-H.; Han, K.-J.; Lee, K.; Lee, K.-J.; Oh, K.-Y.; Lee, M.-J. Classification of Landscape Affected by Deforestation Using High-resolution Remote Sensing Data and Deep-learning Techniques. Remote Sens. 2020 , 12 , 3372. [ Google Scholar ] [ CrossRef ]
- Yu, T.; Wu, W.; Gong, C.; Li, X. Residual Multi-Attention Classification Network for a Forest Dominated Tropical Landscape Using High-Resolution Remote Sensing Imagery. ISPRS Int. J. Geoinf. 2021 , 10 , 22. [ Google Scholar ] [ CrossRef ]
- Pashaei, M.; Kamangir, H.; Starek, M.J.; Tissot, P. Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study over a Wetland. Remote Sens. 2020 , 12 , 959. [ Google Scholar ] [ CrossRef ]
- Fang, B.; Chen, G.; Chen, J.; Ouyang, G.; Kou, R.; Wang, L. Cct: Conditional Co-Training for Truly Unsupervised Remote Sensing Image Segmentation in Coastal Areas. Remote Sens. 2021 , 13 , 3521. [ Google Scholar ] [ CrossRef ]
- Buchsteiner, C.; Baur, P.A.; Glatzel, S. Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning. Remote Sens. 2023 , 15 , 3961. [ Google Scholar ] [ CrossRef ]
- Wang, Z.; Mahmoudian, N. Aerial Fluvial Image Dataset for Deep Semantic Segmentation Neural Networks and Its Benchmarks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 4755–4766. [ Google Scholar ] [ CrossRef ]
- Chen, J.; Chen, G.; Wang, L.; Fang, B.; Zhou, P.; Zhu, M. Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network. Sensors 2020 , 20 , 7032. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Li, Y.; Zhou, Y.; Zhang, Y.; Zhong, L.; Wang, J.; Chen, J. DKDFN: Domain Knowledge-Guided Deep Collaborative Fusion Network for Multimodal Unitemporal Remote Sensing Land Cover Classification. ISPRS J. Photogramm. Remote Sens. 2022 , 186 , 170–189. [ Google Scholar ] [ CrossRef ]
- Tzepkenlis, A.; Marthoglou, K.; Grammalidis, N. Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery. Remote Sens. 2023 , 15 , 2027. [ Google Scholar ] [ CrossRef ]
- Billson, J.; Islam, M.D.S.; Sun, X.; Cheng, I. Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation. Remote Sens. 2023 , 15 , 1253. [ Google Scholar ] [ CrossRef ]
- Bergamasco, L.; Bovolo, F.; Bruzzone, L. A Dual-Branch Deep Learning Architecture for Multisensor and Multitemporal Remote Sensing Semantic Segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 2147–2162. [ Google Scholar ] [ CrossRef ]
- Yang, X.; Zhang, B.; Chen, Z.; Bai, Y.; Chen, P. A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery. Remote Sens. 2022 , 14 , 5062. [ Google Scholar ] [ CrossRef ]
- Yang, X.; Chen, Z.; Zhang, B.; Li, B.; Bai, Y.; Chen, P. A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation. Remote Sens. 2022 , 14 , 1432. [ Google Scholar ] [ CrossRef ]
- Boonpook, W.; Tan, Y.; Nardkulpat, A.; Torsri, K.; Torteeka, P.; Kamsing, P.; Sawangwit, U.; Pena, J.; Jainaen, M. Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery. ISPRS Int. J. Geoinf. 2023 , 12 , 14. [ Google Scholar ] [ CrossRef ]
- Bergado, J.R.; Persello, C.; Stein, A. Recurrent Multiresolution Convolutional Networks for VHR Image Classification. IEEE Trans. Geosci. Remote Sens. 2018 , 56 , 6361–6374. [ Google Scholar ] [ CrossRef ]
- Karila, K.; Matikainen, L.; Karjalainen, M.; Puttonen, E.; Chen, Y.; Hyyppä, J. Automatic Labelling for Semantic Segmentation of VHR Satellite Images: Application of Airborne Laser Scanner Data and Object-Based Image Analysis. ISPRS Open J. Photogramm. Remote Sens. 2023 , 9 , 100046. [ Google Scholar ] [ CrossRef ]
- Zhang, X.; Du, L.; Tan, S.; Wu, F.; Zhu, L.; Zeng, Y.; Wu, B. Land Use and Land Cover Mapping Using Rapideye Imagery Based on a Novel Band Attention Deep Learning Method in the Three Gorges Reservoir Area. Remote Sens. 2021 , 13 , 1225. [ Google Scholar ] [ CrossRef ]
- Zhu, Y.; Geis, C.; So, E.; Jin, Y. Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 14 , 3251–3265. [ Google Scholar ] [ CrossRef ]
- Fan, Z.; Zhan, T.; Gao, Z.; Li, R.; Liu, Y.; Zhang, L.; Jin, Z.; Xu, S. Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model. IEEE Access 2022 , 10 , 56267–56281. [ Google Scholar ] [ CrossRef ]
- Clark, A.; Phinn, S.; Scarth, P. Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation. Land 2023 , 12 , 1268. [ Google Scholar ] [ CrossRef ]
- Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Gill, E.; Molinier, M. A New Fully Convolutional Neural Network for Semantic Segmentation of Polarimetric SAR Imagery in Complex Land Cover Ecosystem. ISPRS J. Photogramm. Remote Sens. 2019 , 151 , 223–236. [ Google Scholar ] [ CrossRef ]
- Wenger, R.; Puissant, A.; Weber, J.; Idoumghar, L.; Forestier, G. Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset. Remote Sens. 2023 , 15 , 151. [ Google Scholar ] [ CrossRef ]
- Xia, J.; Yokoya, N.; Adriano, B.; Zhang, L.; Li, G.; Wang, Z. A Benchmark High-Resolution GaoFen-3 SAR Dataset for Building Semantic Segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 14 , 5950–5963. [ Google Scholar ] [ CrossRef ]
- Kotru, R.; Turkar, V.; Simu, S.; De, S.; Shaikh, M.; Banerjee, S.; Singh, G.; Das, A. Development of a Generalized Model to Classify Various Land Covers for ALOS-2 L-Band Images Using Semantic Segmentation. Adv. Space Res. 2022 , 70 , 3811–3821. [ Google Scholar ] [ CrossRef ]
- Mehra, A.; Jain, N.; Srivastava, H.S. A Novel Approach to Use Semantic Segmentation Based Deep Learning Networks to Classify Multi-Temporal SAR Data. Geocarto Int. 2022 , 37 , 163–178. [ Google Scholar ] [ CrossRef ]
- Pešek, O.; Segal-Rozenhaimer, M.; Karnieli, A. Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types. Remote Sens. 2022 , 14 , 5210. [ Google Scholar ] [ CrossRef ]
- Jing, H.; Wang, Z.; Sun, X.; Xiao, D.; Fu, K. PSRN: Polarimetric Space Reconstruction Network for PolSAR Image Semantic Segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 14 , 10716–10732. [ Google Scholar ] [ CrossRef ]
- Zhang, R.; Chen, J.; Feng, L.; Li, S.; Yang, W.; Guo, D. A Refined Pyramid Scene Parsing Network for Polarimetric SAR Image Semantic Segmentation in Agricultural Areas. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 4014805. [ Google Scholar ] [ CrossRef ]
- Garg, R.; Kumar, A.; Bansal, N.; Prateek, M.; Kumar, S. Semantic Segmentation of PolSAR Image Data Using Advanced Deep Learning Model. Sci. Rep. 2021 , 11 , 15365. [ Google Scholar ] [ CrossRef ]
- Zheng, N.-R.; Yang, Z.-A.; Shi, X.-Z.; Zhou, R.-Y.; Wang, F. Land Cover Classification of Synthetic Aperture Radar Images Based on Encoder—Decoder Network with an Attention Mechanism. J. Appl. Remote Sens. 2022 , 16 , 014520. [ Google Scholar ] [ CrossRef ]
- Shi, X.; Fu, S.; Chen, J.; Wang, F.; Xu, F. Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021 , 14 , 3107–3119. [ Google Scholar ] [ CrossRef ]
- Yoshida, K.; Pan, S.; Taniguchi, J.; Nishiyama, S.; Kojima, T.; Islam, M.T. Airborne LiDAR-Assisted Deep Learning Methodology for Riparian Land Cover Classification Using Aerial Photographs and Its Application for Flood Modelling. J. Hydroinformatics 2022 , 24 , 179–201. [ Google Scholar ] [ CrossRef ]
- Arief, H.A.; Strand, G.-H.; Tveite, H.; Indahl, U.G. Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network. Remote Sens. 2018 , 10 , 973. [ Google Scholar ] [ CrossRef ]
- Xu, Z.; Su, C.; Zhang, X. A Semantic Segmentation Method with Category Boundary for Land Use and Land Cover (LULC) Mapping of Very-High Resolution (VHR) Remote Sensing Image. Int. J. Remote Sens. 2021 , 42 , 3146–3165. [ Google Scholar ] [ CrossRef ]
- Liu, M.; Zhang, P.; Shi, Q.; Liu, M. An Adversarial Domain Adaptation Framework with KL-Constraint for Remote Sensing Land Cover Classification. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 3002305. [ Google Scholar ] [ CrossRef ]
- Lee, D.G.; Shin, Y.H.; Lee, D.C. Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model. J. Sens. 2020 , 2020 , 8825509. [ Google Scholar ] [ CrossRef ]
- Wang, Y.; Shi, H.; Zhuang, Y.; Sang, Q.; Chen, L. Bidirectional Grid Fusion Network for Accurate Land Cover Classification of High-Resolution Remote Sensing Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 , 13 , 5508–5517. [ Google Scholar ] [ CrossRef ]
- Shi, H.; Fan, J.; Wang, Y.; Chen, L. Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images. Remote Sens. 2021 , 13 , 3715. [ Google Scholar ] [ CrossRef ]
- He, S.; Lu, X.; Gu, J.; Tang, H.; Yu, Q.; Liu, K.; Ding, H.; Chang, C.; Wang, N. RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation. IEEE Access 2022 , 10 , 34858–34871. [ Google Scholar ] [ CrossRef ]
- Yang, N.; Tang, H. Semantic Segmentation of Satellite Images: A Deep Learning Approach Integrated with Geospatial Hash Codes. Remote Sens. 2021 , 13 , 2723. [ Google Scholar ] [ CrossRef ]
- Boguszewski, A.; Batorski, D.; Ziemba-Jankowska, N.; Dziedzic, T.; Zambrzycka, A. LandCover.Ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Nashville, TN, USA, 20–25 June 2021. [ Google Scholar ]
- Gao, J.; Weng, L.; Xia, M.; Lin, H. MLNet: Multichannel Feature Fusion Lozenge Network for Land Segmentation. J. Appl. Remote Sens. 2022 , 16 , 016513. [ Google Scholar ] [ CrossRef ]
- Demir, I.; Koperski, K.; Lindenbaum, D.; Pang, G.; Huang, J.; Basu, S.; Hughes, F.; Tuia, D.; Raska, R. DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–23 June 2018; Volume 2018. [ Google Scholar ]
- Wei, H.; Xu, X.; Ou, N.; Zhang, X.; Dai, Y. Deanet: Dual Encoder with Attention Network for Semantic Segmentation of Remote Sensing Imagery. Remote Sens. 2021 , 13 , 3900. [ Google Scholar ] [ CrossRef ]
- Maggiori, E.; Tarabalka, Y.; Charpiat, G.; Alliez, P. Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; Volume 2017. [ Google Scholar ]
- Li, W.; He, C.; Fang, J.; Zheng, J.; Fu, H.; Yu, L. Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data. Remote Sens. 2019 , 11 , 403. [ Google Scholar ] [ CrossRef ]
- Ji, S.; Wang, D.; Luo, M. Generative Adversarial Network-Based Full-Space Domain Adaptation for Land Cover Classification from Multiple-Source Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2021 , 59 , 3816–3828. [ Google Scholar ] [ CrossRef ]
- Chen, Q.; Wang, L.; Wu, Y.; Wu, G.; Guo, Z.; Waslander, S.L. Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings. ISPRS J. Photogramm. Remote Sens. 2019 , 147 , 42–55. [ Google Scholar ] [ CrossRef ]
- Audebert, N.; Le Saux, B.; Lefèvre, S. Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. Remote Sens. 2017 , 9 , 368. [ Google Scholar ] [ CrossRef ]
- Abdollahi, A.; Pradhan, B.; Shukla, N.; Chakraborty, S.; Alamri, A. Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data. Remote Sens. 2021 , 13 , 3710. [ Google Scholar ] [ CrossRef ]
- Khan, S.D.; Alarabi, L.; Basalamah, S. Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images. Information 2021 , 12 , 230. [ Google Scholar ] [ CrossRef ]
- Liu, R.; Tao, F.; Liu, X.; Na, J.; Leng, H.; Wu, J.; Zhou, T. RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sens. 2022 , 14 , 3109. [ Google Scholar ] [ CrossRef ]
- Sang, Q.; Zhuang, Y.; Dong, S.; Wang, G.; Chen, H. FRF-Net: Land Cover Classification from Large-Scale VHR Optical Remote Sensing Images. IEEE Geosci. Remote Sens. Lett. 2020 , 17 , 1057–1061. [ Google Scholar ] [ CrossRef ]
- Guo, Y.; Wang, F.; Xiang, Y.; You, H. Article Dgfnet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images. Remote Sens. 2021 , 13 , 3755. [ Google Scholar ] [ CrossRef ]
- Niu, X.; Zeng, Q.; Luo, X.; Chen, L. FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images. Remote Sens. 2022 , 14 , 215. [ Google Scholar ] [ CrossRef ]
- Wu, M.; Zhang, C.; Liu, J.; Zhou, L.; Li, X. Towards Accurate High Resolution Satellite Image Semantic Segmentation. IEEE Access 2019 , 7 , 55609–55619. [ Google Scholar ] [ CrossRef ]
- Li, J.; Wang, H.; Zhang, A.; Liu, Y. Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model. Sensors 2022 , 22 , 9678. [ Google Scholar ] [ CrossRef ]
- Salgueiro, L.; Marcello, J.; Vilaplana, V. SEG-ESRGAN: A Multi-Task Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images. Remote Sens. 2022 , 14 , 5862. [ Google Scholar ] [ CrossRef ]
- Marsocci, V.; Scardapane, S.; Komodakis, N. MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing. Remote Sens. 2021 , 13 , 3275. [ Google Scholar ] [ CrossRef ]
- Wang, S.; Mu, X.; Yang, D.; He, H.; Zhao, P. Attention Guided Encoder-Decoder Network with Multi-Scale Context Aggregation for Land Cover Segmentation. IEEE Access 2020 , 8 , 215299–215309. [ Google Scholar ] [ CrossRef ]
- Feng, D.; Zhang, Z.; Yan, K. A Semantic Segmentation Method for Remote Sensing Images Based on the Swin Transformer Fusion Gabor Filter. IEEE Access 2022 , 10 , 77432–77451. [ Google Scholar ] [ CrossRef ]
- Bai, J.; Wen, Z.; Xiao, Z.; Ye, F.; Zhu, Y.; Alazab, M.; Jiao, L. Hyperspectral Image Classification Based on Multibranch Attention Transformer Networks. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 3196661. [ Google Scholar ] [ CrossRef ]
- Meng, X.; Yang, Y.; Wang, L.; Wang, T.; Li, R.; Zhang, C. Class-Guided Swin Transformer for Semantic Segmentation of Remote Sensing Imagery. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 6517505. [ Google Scholar ] [ CrossRef ]
- Wang, D.; Yang, R.; Zhang, Z.; Liu, H.; Tan, J.; Li, S.; Yang, X.; Wang, X.; Tang, K.; Qiao, Y.; et al. P-Swin: Parallel Swin Transformer Multi-Scale Semantic Segmentation Network for Land Cover Classification. Comput. Geosci. 2023 , 175 , 105340. [ Google Scholar ] [ CrossRef ]
- Dong, R.; Fang, W.; Fu, H.; Gan, L.; Wang, J.; Gong, P. High-Resolution Land Cover Mapping through Learning with Noise Correction. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 4402013. [ Google Scholar ] [ CrossRef ]
- Shen, X.; Weng, L.; Xia, M.; Lin, H. Multi-Scale Feature Aggregation Network for Semantic Segmentation of Land Cover. Remote Sens. 2022 , 14 , 6156. [ Google Scholar ] [ CrossRef ]
- Luo, Y.; Wang, J.; Yang, X.; Yu, Z.; Tan, Z. Pixel Representation Augmented through Cross-Attention for High-Resolution Remote Sensing Imagery Segmentation. Remote Sens. 2022 , 14 , 5415. [ Google Scholar ] [ CrossRef ]
- Yuan, X.; Chen, Z.; Chen, N.; Gong, J. Land Cover Classification Based on the PSPNet and Superpixel Segmentation Methods with High Spatial Resolution Multispectral Remote Sensing Imagery. J. Appl. Remote Sens. 2021 , 15 , 034511. [ Google Scholar ] [ CrossRef ]
- Zhang, C.; Yue, P.; Tapete, D.; Shangguan, B.; Wang, M.; Wu, Z. A Multi-Level Context-Guided Classification Method with Object-Based Convolutional Neural Network for Land Cover Classification Using Very High Resolution Remote Sensing Images. Int. J. Appl. Earth Obs. Geoinf. 2020 , 88 , 102086. [ Google Scholar ] [ CrossRef ]
- Van den Broeck, W.A.J.; Goedemé, T.; Loopmans, M. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning. Remote Sens. 2022 , 14 , 5911. [ Google Scholar ] [ CrossRef ]
- Zhang, B.; Chen, T.; Wang, B. Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5611412. [ Google Scholar ] [ CrossRef ]
- Li, A.; Jiao, L.; Zhu, H.; Li, L.; Liu, F. Multitask Semantic Boundary Awareness Network for Remote Sensing Image Segmentation. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5400314. [ Google Scholar ] [ CrossRef ]
- Shan, L.; Wang, W. DenseNet-Based Land Cover Classification Network with Deep Fusion. IEEE Geosci. Remote Sens. Lett. 2022 , 19 . [ Google Scholar ] [ CrossRef ]
- Safarov, F.; Temurbek, K.; Jamoljon, D.; Temur, O.; Chedjou, J.C.; Abdusalomov, A.B.; Cho, Y.-I. Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture. Sensors 2022 , 22 , 9784. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Liu, Z.-Q.; Tang, P.; Zhang, W.; Zhang, Z. CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation. Remote Sens. 2022 , 14 , 5027. [ Google Scholar ] [ CrossRef ]
- Wang, D.; Yang, R.; Liu, H.; He, H.; Tan, J.; Li, S.; Qiao, Y.; Tang, K.; Wang, X. HFENet: Hierarchical Feature Extraction Network for Accurate Landcover Classification. Remote Sens. 2022 , 14 , 4244. [ Google Scholar ] [ CrossRef ]
- Zhang, H.; Liu, M.; Wang, Y.; Shang, J.; Liu, X.; Li, B.; Song, A.; Li, Q. Automated Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using Recurrent Residual U-Net. Int. J. Appl. Earth Obs. Geoinf. 2021 , 105 , 102557. [ Google Scholar ] [ CrossRef ]
- Maggiolo, L.; Marcos, D.; Moser, G.; Serpico, S.B.; Tuia, D. A Semisupervised CRF Model for CNN-Based Semantic Segmentation with Sparse Ground Truth. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5606315. [ Google Scholar ] [ CrossRef ]
- Barthakur, M.; Sarma, K.K.; Mastorakis, N. Modified Semi-Supervised Adversarial Deep Network and Classifier Combination for Segmentation of Satellite Images. IEEE Access 2020 , 8 , 117972–117985. [ Google Scholar ] [ CrossRef ]
- Wang, Q.; Luo, X.; Feng, J.; Li, S.; Yin, J. CCENet: Cascade Class-Aware Enhanced Network for High-Resolution Aerial Imagery Semantic Segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022 , 15 , 6943–6956. [ Google Scholar ] [ CrossRef ]
- Zhang, C.; Zhang, L.; Zhang, B.Y.J.; Sun, J.; Dong, S.; Wang, X.; Li, Y.; Xu, J.; Chu, W.; Dong, Y.; et al. Land Cover Classification in a Mixed Forest-Grassland Ecosystem Using LResU-Net and UAV Imagery. J. Res. 2022 , 33 , 923–936. [ Google Scholar ] [ CrossRef ]
- Xu, Y.; Wu, L.; Xie, Z.; Chen, Z. Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens. 2018 , 10 , 144. [ Google Scholar ] [ CrossRef ]
- Li, L.; Yao, J.; Liu, Y.; Yuan, W.; Shi, S.; Yuan, S. Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts. Remote Sens. 2017 , 9 , 701. [ Google Scholar ] [ CrossRef ]
- Cecili, G.; De Fioravante, P.; Congedo, L.; Marchetti, M.; Munafò, M. Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy. Land 2022 , 11 , 1919. [ Google Scholar ] [ CrossRef ]
- Abadal, S.; Salgueiro, L.; Marcello, J.; Vilaplana, V. A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery. Remote Sens. 2021 , 13 , 4547. [ Google Scholar ] [ CrossRef ]
- Henry, C.J.; Storie, C.D.; Palaniappan, M.; Alhassan, V.; Swamy, M.; Aleshinloye, D.; Curtis, A.; Kim, D. Automated LULC Map Production Using Deep Neural Networks. Int. J. Remote Sens. 2019 , 40 , 4416–4440. [ Google Scholar ] [ CrossRef ]
- Shojaei, H.; Nadi, S.; Shafizadeh-Moghadam, H.; Tayyebi, A.; Van Genderen, J. An Efficient Built-up Land Expansion Model Using a Modified U-Net. Int. J. Digit. Earth 2022 , 15 , 148–163. [ Google Scholar ] [ CrossRef ]
- Dong, X.; Zhang, C.; Fang, L.; Yan, Y. A Deep Learning Based Framework for Remote Sensing Image Ground Object Segmentation. Appl. Soft Comput. 2022 , 130 , 109695. [ Google Scholar ] [ CrossRef ]
- Guo, X.; Chen, Z.; Wang, C. Fully Convolutional Densenet with Adversarial Training for Semantic Segmentation of High-Resolution Remote Sensing Images. J. Appl. Remote Sens. 2021 , 15 , 016520. [ Google Scholar ] [ CrossRef ]
- Zhang, B.; Wan, Y.; Zhang, Y.; Li, Y. JSH-Net: Joint Semantic Segmentation and Height Estimation Using Deep Convolutional Networks from Single High-Resolution Remote Sensing Imagery. Int. J. Remote Sens. 2022 , 43 , 6307–6332. [ Google Scholar ] [ CrossRef ]
- Li, W.; Chen, K.; Shi, Z. Geographical Supervision Correction for Remote Sensing Representation Learning. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5411520. [ Google Scholar ] [ CrossRef ]
- Shi, W.; Qin, W.; Chen, A. Towards Robust Semantic Segmentation of Land Covers in Foggy Conditions. Remote Sens. 2022 , 14 , 4551. [ Google Scholar ] [ CrossRef ]
- Zhang, W.; Tang, P.; Zhao, L. Fast and Accurate Land Cover Classification on Medium Resolution Remote Sensing Images Using Segmentation Models. Int. J. Remote Sens. 2021 , 42 , 3277–3301. [ Google Scholar ] [ CrossRef ]
- Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V. Semantic Segmentation of Forest Stands of Pure Species Combining Airborne Lidar Data and Very High Resolution Multispectral Imagery. ISPRS J. Photogramm. Remote Sens. 2017 , 126 , 129–145. [ Google Scholar ] [ CrossRef ]
- Zhang, Z.; Lu, W.; Cao, J.; Xie, G. MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery. Remote Sens. 2022 , 14 , 4514. [ Google Scholar ] [ CrossRef ]
- Li, W.; Chen, K.; Chen, H.; Shi, Z. Geographical Knowledge-Driven Representation Learning for Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2022 , 60 , 5405516. [ Google Scholar ] [ CrossRef ]
- Liu, W.; Liu, J.; Luo, Z.; Zhang, H.; Gao, K.; Li, J. Weakly Supervised High Spatial Resolution Land Cover Mapping Based on Self-Training with Weighted Pseudo-Labels. Int. J. Appl. Earth Obs. Geoinf. 2022 , 112 , 102931. [ Google Scholar ] [ CrossRef ]
- Liu, Q.; Kampffmeyer, M.; Jenssen, R.; Salberg, A.-B. Dense Dilated Convolutions Merging Network for Land Cover Classification. IEEE Trans. Geosci. Remote Sens. 2020 , 58 , 6309–6320. [ Google Scholar ] [ CrossRef ]
- Li, Z.; Zhang, H.; Lu, F.; Xue, R.; Yang, G.; Zhang, L. Breaking the Resolution Barrier: A Low-to-High Network for Large-Scale High-Resolution Land-Cover Mapping Using Low-Resolution Labels. ISPRS J. Photogramm. Remote Sens. 2022 , 192 , 244–267. [ Google Scholar ] [ CrossRef ]
- Yuan, Q.; Mohd Shafri, H.Z. Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction. Remote Sens. 2022 , 14 , 4920. [ Google Scholar ] [ CrossRef ]
- Mboga, N.; D’aronco, S.; Grippa, T.; Pelletier, C.; Georganos, S.; Vanhuysse, S.; Wolff, E.; Smets, B.; Dewitte, O.; Lennert, M.; et al. Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS Int. J. Geoinf. 2021 , 10 , 523. [ Google Scholar ] [ CrossRef ]
- Zhang, Z.; Doi, K.; Iwasaki, A.; Xu, G. Unsupervised Domain Adaptation of High-Resolution Aerial Images via Correlation Alignment and Self Training. IEEE Geosci. Remote Sens. Lett. 2021 , 18 , 746–750. [ Google Scholar ] [ CrossRef ]
- Simms, D.M. Fully Convolutional Neural Nets In-the-Wild. Remote Sens. Lett. 2020 , 11 , 1080–1089. [ Google Scholar ] [ CrossRef ]
- Liu, C.; Du, S.; Lu, H.; Li, D.; Cao, Z. Multispectral Semantic Land Cover Segmentation from Aerial Imagery with Deep Encoder-Decoder Network. IEEE Geosci. Remote Sens. Lett. 2022 , 19 , 5000105. [ Google Scholar ] [ CrossRef ]
- Sun, P.; Lu, Y.; Zhai, J. Mapping Land Cover Using a Developed U-Net Model with Weighted Cross Entropy. Geocarto Int. 2022 , 37 , 9355–9368. [ Google Scholar ] [ CrossRef ]
- Chen, J.; Sun, B.; Wang, L.; Fang, B.; Chang, Y.; Li, Y.; Zhang, J.; Lyu, X.; Chen, G. Semi-Supervised Semantic Segmentation Framework with Pseudo Supervisions for Land-Use/Land-Cover Mapping in Coastal Areas. Int. J. Appl. Earth Obs. Geoinf. 2022 , 112 , 102881. [ Google Scholar ] [ CrossRef ]
Click here to enlarge figure
Data Sources | Number of Articles | References |
---|
RS Satellites | | |
Sentinel-2 | 7 | [ , , , , ] |
Landsat | 5 | [ , , , ] |
Worldview-03 | 2 | [ , ] |
Rapid eye | 1 | [ ] |
Worldview-02 | 1 | [ ] |
Quickbird | 1 | [ ] |
ZY-3 | 1 | [ ] |
PlanetScope | 1 | [ ] |
GF-2 | 2 | [ , ] |
Aerial images | | |
Phantom m multi-rotor AUS | 1 | [ ] |
Quadcopter drone | 1 | [ ] |
Vexcel Ultracam Eagle Camera | 1 | [ ] |
DJI-Phantom 4 pro UAV | 1 | [ ] |
SAR SAT | | |
RADARSAT-2 | 1 | [ ] |
Sentinel-1 | 6 | [ , , , , , ] |
GF-3 | 1 | [ ] |
ALOS-2 | 1 | [ ] |
Others | | |
Earth digitalglobe | 2 | [ , ] |
Mobile phone | 1 | [ ] |
Lidar Sources | 1 | [ ] |
Models | Datasets | Performance Metrics | Limitation/Future Work |
---|
RAANet [ ] | LoveDA, ISPRS Vaihingen | MIoU = 77.28, MIoU = 73.47 | Accuracy can be improved with optimization. |
PSE-UNet Model [ ] | Salinas Dataset | MIoU = 88.50 | Inaccurate segmentation of land cover features with low frequencies, superfluous parameter redundancy, and unvalidated generalization capabilities. |
SEG-ESRGAN [ ] | Sentinel-2 and WorldView-2 image pairs. | MIoU = 62.78 | The assessment of utilizing medium-resolution images has not been tested |
Class-wise FCN [ ] | Vaihingen, Potsdam | MIoU = 72.35, MIoU = 76.88 | Enhancements in performance can be achieved through class-wise considerations for multiple classes, along with improved and more efficient implementations. |
MARE [ ] | Vaihingen | MIoU = 81.76 | Improve performance through parameter optimization and extend approach incorporating other self-supervised algorithms. |
Feature fusion with dual attention and flexible contextual adaptation [ ] | Vaihingen, GaoFen-2 | MIoU = 70.51, MIoU = 56.98 | Computational complexity issue. |
Deanet [ ] | LandCover.ai, DSTL dataset, DeepGlobe | MIoU = 90.28, MIoU = 52.70, MIoU = 71.80 | Suboptimal performance. Future efforts involve incorporating the spatial attention module into a single unified backbone network. |
An encoder-decoder framework featuring attention-guided multi-scale context integration [ ] | GF-2 images | MIoU = 62.3% | Reduced accuracy on imbalance data. |
Models | Data | Performance | Limitation |
---|
Swin-S-GF [ ], | GID | OA = 89.15 MIoU = 80.14 | Computational complexity issue and slow convergence speed. |
CG-Swin [ ] | Vaihingen, Potsdam | OA = 91.68 MIoU = 83.39, OA = 91.93 MIoU = 87.61 | Extending CG-Swin to accommodate multi-modal data sources for more comprehensive and robust classification. |
BANet [ ] | Vaihingen, Potsdam, UAVid dataset | MIoU = 81.35, MIoU = 86.25, MIoU = 64.6 | Combine convolution and Transformer as a hybrid structure to improve performance. |
Spectral spatial transformer [ ] | Indian dataset | OA = 0.94 | Computational complexity issue |
Sgformer [ ] | Landcover dataset | MIOU = 0.85 | Computational complexity issue and slow convergence speed. |
Parallel Swin Transformer [ ] | Postdam, GID WHDLD | OA = 89.44, OA = 84.67, OA = 84.86 | Performance can be improved. |
Models | Datasets | Performance Metrics | Limitation |
---|
RSI-Net [ ] | Vaihingen, Potsdam, GID | OA = 91.83, OA = 93.31, OA = 93.67 | Limitation in segmentation of pixel-wise semantics. Enhanced feature map fusion decoders can lead to performance improvements. |
HMRT [ ] | Potsdam | OA = 85.99 MIoU = 74.14 | Parameter complexity issue, decrease in segmentation accuracy due to a lot of noise. Optimization is required. |
UNetFormer [ ] | UAVid, Vaihingen, Potsdam, LoveDA | MIoU = 67.8, OA = 91.0 MIoU = 82.7, OA = 91.3 MIoU = 86.8, MIoU = 52.4 | Investigate the Transformer’s potential and practicality in addressing geospatial vision tasks is open for research. |
(TL-ResUNet) model [ ] | DeepGlobe | IoU = 0.81 | Improve classification performance is open for research, and developing real time and automated solution for land use land cover. |
CNN-enhanced heterogeneous GCN [ ] | Beijing dataset, Shenzhen dataset. | MIoU = 70.48, MIoU = 62.45 | Future endeavor is to optimize the utilization of pretrained deep CNN features and GCN features across various segmentation scales. |
HFENet [ ] | MZData, LandCover Dataset, WHU Building Dataset | MIoU = 87.19, MIoU = 89.69, MIoU = 92.12 | Time and space complexity issues. Future work can be to automatically fine-tune the parameters to attain the optimal performance of the model. |
Model’s Structures | Batch Size | Epochs | Learning Rate | Data Augmentation | Backbone | Popular Optimizer | Parameters | Evaluation Metrics |
---|
Encoder/decoder-based | 4, 8, 16, 64 | 100–500 | 0.01 | Yes | ResNet | SGD | Low–High | MIoU, OA, F1 |
Transformer-based | 6, 8 | 100–200 | 0.0006 | Yes | ResNet/Swintiny | Adam | High | MIoU, OA, F1 |
Hybrid models | 8, 16 | 40–100 | 0.0006 | Yes | ResNet | Adam | Low–High | MIoU, OA, F1 |
| The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Share and Cite
Ajibola, S.; Cabral, P. A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping. Remote Sens. 2024 , 16 , 2222. https://doi.org/10.3390/rs16122222
Ajibola S, Cabral P. A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping. Remote Sensing . 2024; 16(12):2222. https://doi.org/10.3390/rs16122222
Ajibola, Segun, and Pedro Cabral. 2024. "A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping" Remote Sensing 16, no. 12: 2222. https://doi.org/10.3390/rs16122222
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When you submit your article to a journal, you often need to include a cover letter. This is a great opportunity to highlight to the journal editor what makes your research new and important. The cover letter should explain why your work is perfect for their journal and why it will be of interest to the journal's readers.
A cover letter is often the first thing an editor reads when reviewing your submission. As your first pitch to the editor, the cover letter helps them gauge the suitability of your manuscript for publication in their journal. Imagine your work shaping the future of your field, gathering citations, and sparking discussions.
7 Journal submission tips and hacks from the experts. 7.1 Be personal, use the editor's name. 7.2 Tell them what you want to publish. 7.3 Summarize the highlights of your work. 7.4 Sell yourself. 7.5 Don't forget your "must have" statements. 8 See it in action: Edanz video on writing cover letters. 9 Get a cover letter template.
Then, write a letter that explains why the editor would want to publish your manuscript. The following structure covers all the necessary points that need to be included. If known, address the editor who will be assessing your manuscript by their name. Include the date of submission and the journal you are submitting to.
When writing a cover letter for journal submission, it's important to use appropriate and professional language. Here are some common expressions that can be used in cover letters: "We are pleased to submit our manuscript…". "The research reported in this manuscript addresses a significant gap in the literature…".
A cover letter should be written like a standard business letter: Address the editor formally by name, if known. Include your contact information, as well. This information is probably available through the journal's online submission system, but it is proper to provide it in the cover letter, too. Begin your cover letter with a paragraph that ...
Keep all text left justified. Use spelling and grammar check software. If needed, use a proofreading service or cover letter editing service such as Wordvice to review your letter for clarity and concision. Double-check the editor's name. Call the journal to confirm if necessary.
Keep your cover letter concise, ideally to a maximum of one page. Every sentence should serve a purpose, whether it's establishing the significance of your research, demonstrating its fit with the journal, or ensuring ethical compliance. Remember, a well-written cover letter can make a significant difference in how your manuscript is perceived.
Your cover letter should include. The objective and approach of your research. Any novel contributions reported. Why your manuscript should be published in this journal. Any special considerations about your submission. Related papers by you and/or your fellow authors (published or under consideration) Previous reviews of your submission.
3. Motivation for submitting to the journal: After the short summary, add a sentence regarding the suitability of your study for the journal.Write about how it matches the journal scope and why the readers will find it interesting. 4. Ethical approval: The cover letter for your research paper should mention whether the study was approved by the institutional review board, in case of any ...
3.1. First Cover Letter (Submit Letter) One point of view is that the cover letter's content should be covered in the manuscript's abstract ().A typical cover letter includes the name of editor (s) and the journal, date of submission, the characteristics of the manuscript (i.e., title, type of the manuscript, e.g., review, original, case report), the importance of the work and its ...
Here are nine steps to help you compose a cover letter when submitting your research paper to a professional journal: 1. Set up the formatting. Set up your word processor to format your cover letter correctly. Formatting standards for research paper cover letters usually include: Using single spacing between each line.
Here's how to articulate this alignment in your cover letter: Research the Journal's Aims and Scope. Before you even pen that cover letter, dive deep into the journal's website. Understand its aims, scope, and the audience it serves. This isn't just about ensuring your research fits; it's about tailoring your message to resonate with ...
Cover Letters. The cover letter is a formal way to communicate with journal editors and editorial staff during the manuscript submission process. Most often, a cover letter is needed when authors initially submit their manuscript to a journal and when responding to reviewers during an invitation to revise and resubmit the manuscript.
1. Start With the Proper Cover Letter for Journal Submission Template. Appearances matter. You wouldn't wear a baggy T-shirt and shorts to an academic conference. In the same way, you don't want your cover letter for journal submission to look sloppy. Follow these steps to create a professional template: Cover Letter for Journal Submission ...
Research Square's Free Journal Cover Letter Writing Guide and template can help you produce the most effective cover letter possible. Posted February 9, 2022 by Ben Mudrak. The importance of journal cover letters. Journal cover letters are not just a formality. They give authors the chance to argue that your manuscript is a close fit with ...
This handout offers guidance on the composition of a cover letter for journal manuscripts. A journal article cover letter, also known as a journal publication letter, is a letter written to a peer-reviewed journal to advocate for the publication of an academic manuscript. ... Research journals strive to remain relevant. In order to do so ...
Don't address your cover letter to "Dear Sir.". This is antiquated language, not to mention often incorrect, given that two-thirds of Nature Methods' editors are women. Stick to the gender-neutral "Dear Editor" in cases where you are not addressing a specific editor. Don't miss parts 2 and 3 of this series of posts covering ...
You may be required to submit a cover letter with your submission. Individual journals may have specific requirements regarding the cover letter's contents, so please consult the individual journal's Guide for Authors. A cover letter is a simple, brief business letter, designed to introduce your manuscript to a prospective Editor.
Personalise the cover letter by addressing the journal's editor by their name. State the article type of your manuscript at the beginning of your cover letter (original research article, methodology, case report, etc.) Provide the full details of all the authors, including email address and phone number, in your cover letter. Explain briefly ...
A cover letter is the first point of contact between you and the target journal's editors. As such, your cover letter functions as a sales pitch to the journal editors. In other words, you cover letter needs to sell the notion of why your manuscript deserves to be published in and how it matches the scope of the target journal.
Proofread your letter by checking the spelling, grammar, and syntax. A well-written letter indicates that you take your submission seriously and that you are an author who pays attention to detail. 8. Check every detail. Avoid mistakes such as directing the cover letter to the editor (s) of a different journal, or to a different journal altogether.
A cover letter can be used to help convey a work's importance to the editors. It should also be used to highlight any potential issues such as related manuscripts currently under consideration in any other Springer Nature publication, as well as indicating whether you have had any prior discussions with a Springer Nature editor about the work described in the manuscript.
this research is important or significant. Do not simply insert your abstract into your cover letter. Include specific reference to the journal's Aims & Scope and why you think the readers of the journal would be interested in your manuscript. and is not currently under consideration . Also confirm that you have no
Researcher cover letter example To help you learn more about cover letters for researcher positions, here is a sample: Marcus Ong Beng Chin Singapore +65 9555 5555 [email protected] 8 March 2024 Mr. Robert Chan Wavewood Pte Ltd Dear Mr. Robert Chan, I am writing to you with a keen interest in the Researcher position at Wavewood Pte Ltd. With a master's degree in marine science and five ...
在投稿过程中,随论文一起提交的cover letter(投稿信)往往是编辑们首先读到的内容。作为学术发表之路上的一块敲门砖,cover letter作用不容小觑——通过在简短的篇幅中快速展示出研究的重点及亮点,帮助编辑快速对论文进行初步评估并建立第一印象。
*Reproduced from Brand et al. (2015), Learned Publishing 28(2), with permission of the authors. Sample CRediT author statement. Zhang San: Conceptualization, Methodology, Software Priya Singh.: Data curation, Writing- Original draft preparation.Wang Wu
A great cover letter serves as a bridge between your resume and a job posting. Find examples of how to showcase skills and work experience in cover letters. Find Talent. Post a job and hire a pro. ... Before customizing your cover letter template, thoroughly research the company. This can include:
Geophysical Research Letters is an AGU journal publishing high-impact, ... We advocate toward a better characterization of the surficial cover to assess the wet or dry nature for the base, and possibly reconcile most of the literature on the topic. Key Points.
Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover ...