Digital Transformation in National Defense: A Bibliometric Review of Distributed Image Analysis Technology
Keywords:
Deep Learning;, Distributed System;, Image Analysis; IMINT; Network-Centric
Abstract
As remote sensing technology continues to advancing, image analysis systems must efficiently manage substantial volumes of data. The incorporation of deep learning into distributed computing systems has become essential for addressing this demand, facilitating important tasks like object detection and real-time classification. This research methodically outlines the landscape of distributed machine learning for image analysis to pinpoint technological trends that can enhance network-centric imagery intelligence (IMINT). By employing a PRISMA-guided literature review and a bibliometric analysis of 59 publications from Scopus (2015–2026), we chart the field's progression from fundamental distributed computing to optimized, real-time analytical systems. Analyzing keyword co-occurrence and thematic synthesis uncovers significant clusters, including distributed deep learning, privacy-preserving collaboration, and edge-based inference. Based on this examination, we identify and assess key adaptable technologies like split computing, federated learning, and lightweight model optimization for their applicability in operational contexts defined by limited connectivity and decentralized resources. By aligning global research trends with specific national requirements, this review provides a strategic framework for developing resilient, efficient, and adaptive IMINT capabilities within a network-centric paradigm.
References
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Xie, W., Ma, J., Lu, T., Li, Y., Lei, J., Fang, L., & Du, Q. (2025). Distributed Deep Learning With Gradient Compression for Big Remote Sensing Image Interpretation. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14328–14342. https://doi.org/10.1109/TNNLS.2024.3517535
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Balti, H., Mellouli, N., Chebbi, I., Farah, I., & Lamolle, M. (2019). Deep Semantic Feature Detection from Multispectral Satellite Images. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 458–466. https://doi.org/10.5220/0008350004580466
Binquan, L., & Xiaohui, H. (2019). Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach. Journal of Systems Engineering and Electronics, 30(2), 238–244. https://doi.org/10.21629/JSEE.2019.02.02
Boutellier, J., Tan, B., Nurmi, J., & Bhattacharyya, S. S. (2025). Edge-PRUNE: A Dataflow-Based Framework for Distributed Signal Processing and Machine Learning. IEEE Transactions on Signal Processing, 73, 3302–3315. https://doi.org/10.1109/TSP.2025.3598453
Chen, J., & Ran, X. (2019). Deep Learning With Edge Computing: A Review. Proceedings of the IEEE, 107(8), 1655–1674. https://doi.org/10.1109/JPROC.2019.2921977
Chen, X., Li, W., Hsu, C.-Y., Arundel, S. T., & Higman, B. (2025). Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions. Remote Sensing, 17(11), 1856. https://doi.org/10.3390/rs17111856
Cheng, M.-H., Sun, Q., & Tu, C.-H. (2018). An Adaptive Computation Framework of Distributed Deep Learning Models for Internet-of-Things Applications. 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 85–91. https://doi.org/10.1109/RTCSA.2018.00019
Do, N.-T., Nguyen, N. N.-Y., Nguyen, D.-P., & Do, T.-H. (2024). RAMOTS: A Real-Time System for Aerial Multi-Object Tracking Based on Deep Learning and Big Data Technology. 2024 16th International Conference on Knowledge and System Engineering (KSE), 01–06. https://doi.org/10.1109/KSE63888.2024.11063545
Gandor, T., & Nalepa, J. (2022). First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning. Sensors, 22(3), 1104. https://doi.org/10.3390/s22031104
Gomes, D., Hasan, M., & Philip, S. R. (2025). Enhancing Capabilities and Security Features of Drone Networks Through Machine Learning: A Comprehensive Overview (pp. 81–105). https://doi.org/10.1007/978-3-031-80961-3_5
Gomez, L., Wilhelm, M., Márquez, J., & Duverger, P. (2019). Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection. ArXiv, abs/1907.04246. https://api.semanticscholar.org/CorpusID:195847875
Han, R., Si, M., Demmel, J., & You, Y. (2021). Dynamic scaling for low-precision learning. Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 480–482. https://doi.org/10.1145/3437801.3441624
Haq, B., Kasi, B., Kasi, M., & Haider, S. K. (2025). Spatial-Temporal Deforestation Forecasting via Remote Sensing and Artificial Intelligence Driven Sensor Networks. Mobile Networks and Applications, 30(3–4), 414–421. https://doi.org/10.1007/s11036-025-02462-9
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv, abs/1704.04861. https://api.semanticscholar.org/CorpusID:12670695
Karpiuk, N., & Klym, H. (2024). Streamlined Near Real-Time Image Processing with Apache Kafka. 2024 14th International Conference on Dependable Systems, Services and Technologies (DESSERT), 1–5. https://doi.org/10.1109/DESSERT65323.2024.11122032
Liu, X., Dong, X., Jia, N., & Zhao, W. (2024). Federated Learning-Oriented Edge Computing Framework for the IIoT. Sensors, 24(13), 4182. https://doi.org/10.3390/s24134182
Liu, X., Li, H., Xu, G., Lu, R., & He, M. (2020). Adaptive privacy-preserving federated learning. Peer-to-Peer Networking and Applications, 13(6), 2356–2366. https://doi.org/10.1007/s12083-019-00869-2
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/j.ijsu.2010.02.007
Ntousis, O., Makris, E., Tsanakas, P., & Pavlatos, C. (2025). A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations. Technologies, 13(1), 35. https://doi.org/10.3390/technologies13010035
Qiu, J., Yin, F., Wang, B., & Liang, B. (2025). Edge-Cloud Cooperative Inference for Semantic Segmentation Models (pp. 1–13). https://doi.org/10.1007/978-981-96-1698-5_1
Seo, J., Jang, S., Cha, J., Choi, H., Kim, D., & Kim, S. (2023). MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing. Sensors, 23(10), 4712. https://doi.org/10.3390/s23104712
Shen, Y., Sowmya, A., Luo, Y., Liang, X., Shen, D., & Ke, J. (2023). A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN. IEEE Transactions on Medical Imaging, 42(7), 1969–1981. https://doi.org/10.1109/TMI.2022.3221724
Siddique, H., Neves, M., Kuzniar, C., & Haque, I. (2021). Towards Network-accelerated ML-based Distributed Computer Vision Systems. 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), 122–129. https://doi.org/10.1109/ICPADS53394.2021.00021
Song, L., Zhao, K., Pan, P., Liu, Y., Zhang, Y., Xu, Y., & Jin, R. (2021). Communication Efficient SGD via Gradient Sampling with Bayes Prior. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12060–12069. https://doi.org/10.1109/CVPR46437.2021.01189
Tu, C.-H., Sun, Q., & Cheng, M.-H. (2021). On designing the adaptive computation framework of distributed deep learning models for Internet-of-Things applications. The Journal of Supercomputing, 77(11), 13191–13223. https://doi.org/10.1007/s11227-021-03795-4
Xie, W., Ma, J., Lu, T., Li, Y., Lei, J., Fang, L., & Du, Q. (2025). Distributed Deep Learning With Gradient Compression for Big Remote Sensing Image Interpretation. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14328–14342. https://doi.org/10.1109/TNNLS.2024.3517535
Yang, A., & Guo, C. (2024). GraphFed-Swarms: Large-Scale Satellite Constellations Space-Based Distributed Computing with Graph Information Aggregation Federated Learning. IAF Space Operations Symposium, 887–896. https://doi.org/10.52202/078367-0095
Zhang, K., Alqahtani, S., & Demirbas, M. (2017). A Comparison of Distributed Machine Learning Platforms. 2017 26th International Conference on Computer Communication and Networks (ICCCN), 1–9. https://doi.org/10.1109/ICCCN.2017.8038464
Zwane, S., Tarwireyi, P., & Adigun, M. (2018). Performance Analysis of Machine Learning Classifiers for Intrusion Detection. 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 1–5. https://doi.org/10.1109/ICONIC.2018.8601203
Published
2026-04-15
How to Cite
Rahmawati, A., Trismadi, & Asep Adang Supriyadi. (2026). Digital Transformation in National Defense: A Bibliometric Review of Distributed Image Analysis Technology. International Journal of Education, Vocational and Social Science , 5(02), 204-216. https://doi.org/10.63922/ijevss.v5i02.3170
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
