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Journal articles on the topic 'Collaborative annotation'

Author: Grafiati

Published: 20 February 2023

Last updated: 31 July 2025

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1

Islamaj, Rezarta, Dongseop Kwon, Sun Kim, and Zhiyong Lu. "TeamTat: a collaborative text annotation tool." Nucleic Acids Research 48, W1 (2020): W5—W11. http://dx.doi.org/10.1093/nar/gkaa333.

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Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (loca

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Park, Jinkyung Katie, Rahul Dev Ellezhuthil, Pamela Wisniewski, and Vivek Singh. "Collaborative human-AI risk annotation: co-annotating online incivility with CHAIRA." Information Research an international electronic journal 30, iConf (2025): 992–1008. https://doi.org/10.47989/ir30iconf47146.

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Introduction. Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. Method. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system com

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Wiktorin, Thomas, Daniel Grigutsch, Felix Erdfelder, et al. "Collaborative Semantic Annotation Tooling (CoAT) to Improve Efficiency and Plug-and-Play Semantic Interoperability in the Secondary Use of Medical Data: Concept, Implementation, and First Cross-Institutional Experiences." Applied Sciences 14, no. 2 (2024): 820. http://dx.doi.org/10.3390/app14020820.

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The cross-institutional secondary use of medical data benefits from structured semantic annotation, which ideally enables the matching and merging of semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambig

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Xia, Meng, Shradha Maharjan, Tammy Le, Will Taylor, and Myoungkyu Song. "SYNCode: Synergistic Human–LLM Collaboration for Enhanced Data Annotation in Stack Overflow." Information 16, no. 5 (2025): 392. https://doi.org/10.3390/info16050392.

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Large language models (LLMs) have rapidly advanced natural language processing, showcasing remarkable effectiveness as automated annotators across various applications. Despite their potential to significantly reduce annotation costs and expedite workflows, annotations produced solely by LLMs can suffer from inaccuracies and inherent biases, highlighting the necessity of maintaining human oversight. In this article, we present a synergistic human–LLM collaboration approach for data annotation enhancement (SYNCode). This framework is designed explicitly to facilitate collaboration between human

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Rydell, Christopher, and Joakim Lindblad. "CytoBrowser: a browser-based collaborative annotation platform for whole slide images." F1000Research 10 (March 22, 2021): 226. http://dx.doi.org/10.12688/f1000research.51916.1.

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We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in

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Karimzadeh, Morteza, and Alan MacEachren. "GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 161. http://dx.doi.org/10.3390/ijgi8040161.

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Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consu

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Li, Zhenduo. "An exploration and practice of an optimized online annotation solution based on PDF slicing and SVG." Applied and Computational Engineering 43, no. 1 (2024): 180–92. http://dx.doi.org/10.54254/2755-2721/43/20230831.

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With the continuous development of network technology, online annotation has become an essential tool in fields such as education, training, and document collaboration. However, traditional annotation techniques are plagued by several limitations, including low security, non-erasable annotations after uploading, and difficulties in communication across different systems. To address these issues, this paper proposes an SVG-based online annotation technique that is built upon PDF file slicing. This approach aims to enhance the deficiencies of traditional annotation techniques and improve user in

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Goy, Annamaria, Diego Magro, Giovanna Petrone, Claudia Picardi, Marco Rovera, and Marino Segnan. "An Integrated Support to Collaborative Semantic Annotation." Advances in Human-Computer Interaction 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7219098.

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Everybody experiences every day the need to manage a huge amount of heterogeneous shared resources, causing information overload and fragmentation problems. Collaborative annotation tools are the most common way to address these issues, but collaboratively tagging resources is usually perceived as a boring and time consuming activity and a possible source of conflicts. To face this challenge, collaborative systems should effectively support users in the resource annotation activity and in the definition of a shared view. The main contribution of this paper is the presentation and the evaluatio

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Chen, Chih-Ming, and Ming-Yueh Tsay. "Applications of collaborative annotation system in digital curation, crowdsourcing, and digital humanities." Electronic Library 35, no. 6 (2017): 1122–40. http://dx.doi.org/10.1108/el-08-2016-0172.

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Purpose Collaboratively annotating digital texts allow users to add valued information, share ideas and create knowledge. Most importantly, annotated content can help users obtain a deeper and broader understanding of a text compared to digital content without annotations. This work proposes a novel collaborative annotation system (CAS) with four types of multimedia annotations including text annotation, picture annotation, voice annotation and video annotation which can embedded into any HTML Web pages to enable users to collaboratively add and manage annotations on these pages and provide a

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FERNÁNDEZ, N., J. A. FISTEUS, D. FUENTES, L. SÁNCHEZ, and V. LUQUE. "A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 847–86. http://dx.doi.org/10.1142/s0218213011000413.

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The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert

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Cebrián-de-la-Serna, Manuel, María Jesús Gallego-Arrufat, and Violeta Cebrián-Robles. "Multimedia Annotations for Practical Collaborative Reasoning." Journal of New Approaches in Educational Research 10, no. 2 (2021): 264. http://dx.doi.org/10.7821/naer.2021.7.664.

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University education requires students to be trained both at university and at external internship centres. Because of Covid-19, the availability of multimedia resources and examples of practical contexts has become vital. Multimedia annotation can help students reflect on the professional world, collaborating and interacting with colleagues online. This study aims to encourage collaborative practical thinking by using new video annotation technologies. 274 students participated in an experiment of task design focusing on the analysis of a technology-based, award-winning educational innovation

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Zhu, Zhen, Yibo Wang, Shouqing Yang, et al. "CORAL: Collaborative Automatic Labeling System Based on Large Language Models." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4401–4. http://dx.doi.org/10.14778/3685800.3685885.

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In the era of big data, data annotation is integral to numerous applications. However, it is widely acknowledged as a laborious and time-consuming process, significantly impeding the scalability and efficiency of data-driven applications. To reduce the human cost, we demonstrate CORAL, a collaborative automatic labeling system driven by large language models (LLMs), which achieves high-quality annotation with the least human effort. Firstly, CORAL employs LLM to automatically annotate vast datasets, generating coarse-grained labels. Subsequently, a weakly-supervised learning module trains smal

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Schmer-Galunder, Sonja, Ruta Wheelock, Zaria Jalan, Alyssa Chvasta, Scott Friedman, and Emily Saltz. "Annotator in the Loop: A Case Study of In-Depth Rater Engagement to Create a Prosocial Benchmark Dataset." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1319–28. http://dx.doi.org/10.1609/aies.v7i1.31726.

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With the growing prevalence of large language models, it is increasingly common to annotate datasets for machine learning using pools of crowd raters. However, these raters often work in isolation as individual crowdworkers. In this work, we regard annotation not merely as inexpensive, scalable labor, but rather as a nuanced interpretative effort to discern the meaning of what is being said in a text. We describe a novel, collaborative, and iterative annotator-in-the-loop methodology for annotation, resulting in a 'Bridging Benchmark Dataset' of comments relevant to bridging divides, annotated

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Rahmawati, Maya, Nia Fujiawati, and Een Nurhasanah. "Analysis of student engagement and critical thinking skills in digital annotation-assisted collaborative reading activities." BIS Education 1 (April 15, 2025): V125003. https://doi.org/10.31603/bised.148.

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This research is conducted to describe how digital social annotation media engages students and develops their critical thinking skills in collaborative reading activities. The research used a qualitative approach with a case study design, in which participants were fifth-semester college students. The data were collected through observations and reflections on students' digital annotations via online platforms, such as Google Docs. The results show that digital annotation-based collaborative reading engages students along behavioural, cognitive, and affective dimensions. Students evidenced ac

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Nguyen Hong Duc, Paul, Maëlle Torterotot, Flore Samaran, et al. "Assessing inter-annotator agreement from collaborative annotation campaign in marine bioacoustics." Ecological Informatics 61 (March 2021): 101185. http://dx.doi.org/10.1016/j.ecoinf.2020.101185.

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Wu, Dan, Xiaomei Xu, and Wenting Yu. "Comparing collaborative annotations on books between libraries and social community sites." Electronic Library 34, no. 2 (2016): 178–95. http://dx.doi.org/10.1108/el-09-2014-0171.

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Purpose – Based on the study of overall situation of the tagging function in the provincial public libraries and library of major colleges and universities, this paper aims to examine the difference of tagging behaviour of its users in library and social community sites. The authors also want to understand the causes of a variety of annotation behavior in social community sites and libraries. Design/methodology/approach – The authors collected all system log data of tags, comments and ratings users added in Wuhan University library, and then found the tags, comments and rating of corresponding

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Corbett, Derek J., and Daniel Cutting. "AD LOC: Collaborative Location-based Annotation." IPSJ Digital Courier 3 (2007): 280–92. http://dx.doi.org/10.2197/ipsjdc.3.280.

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OLADIPO, Francisca, Dayo AKINBAMI, and Gabriel ABRAHAM. "Annotation Toolkit for Collaborative Document Editing." Journal of Applied Computer Science & Mathematics 12, no. 2 (2018): 37–40. http://dx.doi.org/10.4316/jacsm.201802006.

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Baclawski, Kenneth. "Semantic annotation of collaborative work environments." Applied Ontology 12, no. 3-4 (2017): 313–22. http://dx.doi.org/10.3233/ao-170186.

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Brusilovsky, Peter. "Collective Class Wisdom: Collaborative Lecture Annotation." Computer 47, no. 3 (2014): 6. http://dx.doi.org/10.1109/mc.2014.55.

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Hunter, J., M. Henderson, and I. Khan. "Collaborative Annotation of 3D Crystallographic Models." Journal of Chemical Information and Modeling 47, no. 6 (2007): 2475–84. http://dx.doi.org/10.1021/ci700173y.

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Diaper, Dan, and Martin Beer. "Collaborative document annotation using electronic mail." Computer Supported Cooperative Work (CSCW) 3, no. 3-4 (1995): 297–325. http://dx.doi.org/10.1007/bf00750744.

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Hunter, Jane. "Collaborative semantic tagging and annotation systems." Annual Review of Information Science and Technology 43, no. 1 (2009): 1–84. http://dx.doi.org/10.1002/aris.2009.1440430111.

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Chen, Jia, Cui-xia Ma, Hong-an Wang, Hai-yan Yang, and Dong-xing Teng. "Sketch Based Video Annotation and Organization System in Distributed Teaching Environment." International Journal of Distributed Systems and Technologies 1, no. 4 (2010): 27–41. http://dx.doi.org/10.4018/jdst.2010100103.

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As the use of instructional video is becoming a key component of e-learning, there is an increasing need for a distributed system which supports collaborative video annotation and organization. In this paper, the authors construct a distributed environment on the top of NaradaBrokering to support collaborative operations on video material when users are located in different places. The concept of video annotation is enriched, making it a powerful media to improve the instructional video organizing and viewing. With panorama based and interpolation based methods, all related users can annotate

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Berger, Arne, Sören Totzauer, Kevin Lefeuvre, Michael Storz, Albrecht Kurze, and Andreas Bischof. "Wicked, Open, Collaborative: Why Research through Design Matters for HCI Research." i-com 16, no. 2 (2017): 131–42. http://dx.doi.org/10.1515/icom-2017-0014.

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AbstractIn contrast to the first and second wave of Human Computer Interaction, the third wave grapples with wicked problems. However, re-solutions to wicked problems embodied in artifacts frame and change the understanding of the problem itself. Research through Design (RtD) is a constructive methodology to understand this interplay of problem framing through designing artifacts. RtD is also suited to resurface the theory within those artifacts through annotation. These annotations expose and emphasize qualities, values and assumptions held within artifacts by its creators. In addition to tho

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Sharron, Kelly. "Cover Annotation." TSQ 10, no. 3-4 (2023): 307–11. http://dx.doi.org/10.1215/23289252-10900830.

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Abstract The inaugural issue of TSQ featured a selfie of Chelsea Manning, marking the way that Chelsea Manning, and the attendant and related controversies of treason and trans identity, began a newly formed figure in the trans imaginary. This tenth anniversary issue returns Manning to the cover in a multitude of forms, courtesy of Heather Dewey-Hagborg's Probably Chelsea (2017). This annotation explores the themes of Probably Chelsea and the larger collaborative efforts between Dewey-Hagborg and Manning, including Radical Love: Chelsea Manning (2015).

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H. Ashourzadeh, Shahrzad. "Comparing Net Interactive Document System with Non-Interactive Digital Annotation Approaches." IPSI Transactions on Internet Research 20, no. 1 (2024): 5–17. http://dx.doi.org/10.58245/ipsi.tir.2401.02.

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Interactivity is an essential part of learning and development, as it teaches people how to collaborate, communicate respectfully, exchange information, and hone their skills. Social networks and computer games are a few examples of applications that allow interactivity and foster more effective learning environments. However, while technology has enabled the creation of large websites, e-books, and online educational systems, many digital libraries and annotation approaches do not provide users with the opportunity to interact with the material or other readers. Users can only share annotated

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Crasson, Aurèle, and Janeta Ouzounova-maspero. ""Annotation collaborative en ligne de l'archive manuscrite"." Genesis 27, no. 1 (2006): 165–66. http://dx.doi.org/10.3406/item.2006.1398.

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Howard, Craig D. "Participatory Media Literacy in Collaborative Video Annotation." TechTrends 65, no. 5 (2021): 860–73. http://dx.doi.org/10.1007/s11528-021-00632-6.

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Yamamoto, Daisuke, Tomoki Masuda, Shigeki Ohira, and Katashi Nagao. "Collaborative Video Annotation by Sharing Tag Clouds." Transactions of the Japanese Society for Artificial Intelligence 25 (2010): 243–51. http://dx.doi.org/10.1527/tjsai.25.243.

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Goy, Anna, Diego Magro, Giovanna Petrone, Claudia Picardi, and Marino Segnan. "Ontology-driven collaborative annotation in shared workspaces." Future Generation Computer Systems 54 (January 2016): 435–49. http://dx.doi.org/10.1016/j.future.2015.04.013.

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Zigkolis, Christos, Symeon Papadopoulos, George Filippou, Yiannis Kompatsiaris, and Athena Vakali. "Collaborative event annotation in tagged photo collections." Multimedia Tools and Applications 70, no. 1 (2012): 89–118. http://dx.doi.org/10.1007/s11042-012-1154-5.

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Hendley, Robert J., Russell Beale, Chris P. Bowers, et al. "CASAM: collaborative human-machine annotation of multimedia." Multimedia Tools and Applications 70, no. 2 (2013): 1277–308. http://dx.doi.org/10.1007/s11042-012-1255-1.

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Dodero, Juan, Paloma Díaz, and Ignacio Sarasa. "Integrating Ontologies into the Collaborative Authoring of Learning Objects." JUCS - Journal of Universal Computer Science 11, no. (9) (2005): 1568–76. https://doi.org/10.3217/jucs-011-09-1568.

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Authoring learning material is a multi-disciplinary undertaking where different people can play their role. Any support that can be provided for the collaboration of instructional designers, pedagogues, media designers, and students, among others, is welcome. In particular, metadata annotation of learning objects is an important task within the whole authoring process. This work presents the first resulting products and approaches from the MD2 project, consisting of a service-oriented framework and a tool to support the integrated, ontology-based collaborative annotation of learning objects.

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Sato, Takafumi, Makoto Goto, Fuminori Kimura, and Akira Maeda. "Developing a Collaborative Annotation System for Historical Documents by Multiple Humanities Researchers." International Journal of Computer Theory and Engineering 8, no. 1 (2016): 88–93. http://dx.doi.org/10.7763/ijcte.2016.v8.1025.

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Stucky, Brian, Laura Brenskelle, and Robert Guralnick. "Methods, New Software Tools, and Best Practices for Developing High-quality Training Data for Machine Learning-based Image Analysis in Biodiversity Research." Biodiversity Information Science and Standards 3 (July 2, 2019): e37615. https://doi.org/10.3897/biss.3.37615.

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Recent progress in using deep learning techniques to automate the analysis of complex image data is opening up exciting new avenues for research in biodiversity science. However, potential applications of machine learning methods in biodiversity research are often limited by the relative scarcity of data suitable for training machine learning models. Development of high-quality training data sets can be a surprisingly challenging task that can easily consume hundreds of person-hours of time. In this talk, we present the results of our recent work implementing and comparing several different me

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Huang, Xiaoshan, Juan Zheng, Shan Li, et al. "Investigating the effect of emotional tone on learners’ reading engagement and peer acknowledgement in social annotation." Australasian Journal of Educational Technology 40, no. 6 (2024): 92–107. https://doi.org/10.14742/ajet.9122.

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Social annotation fosters collaborative learning by encouraging knowledge sharing and a community of inquiry. However, research has primarily focused on the cognitive aspect of social annotation. This study aims to contribute an emotional perspective to the existing literature on social annotation. Specifically, we used the valence-aware dictionary for sentiment reasoning algorithm to measure students’ emotional tones in 1,954 comments posted during social annotation. We then utilised linear mixed-effect models to examine the effect of emotional tone on students’ reading engagement and peer ac

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Baur, Tobias, Alexander Heimerl, Florian Lingenfelser, et al. "eXplainable Cooperative Machine Learning with NOVA." KI - Künstliche Intelligenz 34, no. 2 (2020): 143–64. http://dx.doi.org/10.1007/s13218-020-00632-3.

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Abstract In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA. The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annota

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Sillitoe, Ian, Antonina Andreeva, Tom L. Blundell, et al. "Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation." Nucleic Acids Research 48, no. D1 (2019): D314—D319. http://dx.doi.org/10.1093/nar/gkz967.

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Abstract Genome3D (https://www.genome3d.eu) is a freely available resource that provides consensus structural annotations for representative protein sequences taken from a selection of model organisms. Since the last NAR update in 2015, the method of data submission has been overhauled, with annotations now being ‘pushed’ to the database via an API. As a result, contributing groups are now able to manage their own structural annotations, making the resource more flexible and maintainable. The new submission protocol brings a number of additional benefits including: providing instant validation

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ZHU, SONGHAO, ZHIWEI LIANG, and XIAOYUAN JING. "VIDEO RETRIEVAL VIA LEARNING COLLABORATIVE SEMANTIC DISTANCE." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (2011): 475–90. http://dx.doi.org/10.1142/s0218001411008944.

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Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of labeled data in many real-world application areas, such as video annotation. However, the pairwise similarity metric, a significant factor of existing approaches, has not been fully investigated. That is, these graph-based semi-supervised approaches estimate the pairwise similarity between samples mainly according to the spatial property of video data. On the other hand, temporal property, an essential characteristic of video data, is not embedded into the pair

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Wilk, Stefan, Stephan Kopf, and Wolfgang Effelsberg. "Collaborative Annotation of Videos Relying on Weak Consistency." ACM Transactions on Multimedia Computing, Communications, and Applications 12, no. 3 (2016): 1–23. http://dx.doi.org/10.1145/2907983.

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Leiserson, Mark D. M., Connor C. Gramazio, Jason Hu, Hsin-Ta Wu, David H. Laidlaw, and Benjamin J. Raphael. "MAGI: visualization and collaborative annotation of genomic aberrations." Nature Methods 12, no. 6 (2015): 483–84. http://dx.doi.org/10.1038/nmeth.3412.

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Weekes, Dana, S. SRI Krishna, Constantina Bakolitsa, Ian A. Wilson, Adam Godzik, and John Wooley. "TOPSAN: a collaborative annotation environment for structural genomics." BMC Bioinformatics 11, no. 1 (2010): 426. http://dx.doi.org/10.1186/1471-2105-11-426.

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Abiodun Robert, Charles. "Annotation for knowledge sharing in a collaborative environment." Journal of Knowledge Management 13, no. 1 (2009): 111–19. http://dx.doi.org/10.1108/13673270910931206.

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ZOMAHOUN, Damien E. "Collaborative Semantic Annotation of Images : Ontology-Based Model." Signal & Image Processing : An International Journal 4, no. 6 (2013): 71–81. http://dx.doi.org/10.5121/sipij.2013.4606.

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Glover, Ian, Glenn Hardaker, and Zhijie Xu. "Collaborative annotation system environment (CASE) for online learning." Campus-Wide Information Systems 21, no. 2 (2004): 72–80. http://dx.doi.org/10.1108/10650740410529501.

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Nasirifard, Peyman, Vassilios Peristeras, and Stefan Decker. "Annotation-based access control for collaborative information spaces." Computers in Human Behavior 27, no. 4 (2011): 1352–64. http://dx.doi.org/10.1016/j.chb.2010.07.030.

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Borne, Kirk. "Collaborative annotation for scientific data discovery and reuse." Bulletin of the American Society for Information Science and Technology 39, no. 4 (2013): 44–45. http://dx.doi.org/10.1002/bult.2013.1720390414.

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Traester, Mary, Chris Kervina, and Noel Holton Brathwaite. "Pedagogy to Disrupt the Echo Chamber." Pedagogy 21, no. 2 (2021): 329–49. http://dx.doi.org/10.1215/15314200-8811517.

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Abstract This article addresses the challenges of fake news and echo chambers in the digital age by exploring the possibility that susceptibility to misinformation derives not from an inevitable fault in the medium of digital publishing but, rather, from the slower development and adoption of pedagogies that leverage digital tools for reading. The authors examined student annotations and argue that focusing on reading using collaborative digital annotation can stimulate knowledge acquisition and personal belief formation and, further, can assist educators to evaluate the effectiveness of instr

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Wang, Zhibao, Jie Zhang, Lu Bai, et al. "A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning." Sensors 24, no. 5 (2024): 1509. http://dx.doi.org/10.3390/s24051509.

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Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach

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