{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T05:52:39Z","timestamp":1767765159047,"version":"3.48.0"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"MSIT (Ministry of Science, ICT), Korea, under the Global Research Support Program in the Digital Field program","doi-asserted-by":"publisher","award":["RS-2024-00431049"],"award-info":[{"award-number":["RS-2024-00431049"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002597","name":"Kyung Hee University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002597","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The availability of nighttime satellite imagery provides unique opportunities for monitoring fishing activity in data-sparse ocean regions. This study leverages Visible Infrared Imaging Radiometer Suite (VIIRS) Day\/Night Band monthly composite imagery to identify and classify recurring spatial patterns of fishing activity in the Korean Exclusive Economic Zone from 2014 to 2024. While prior research has primarily produced static hotspot maps, our approach advances geospatial fishing activity identification by employing machine learning techniques to group similar spatiotemporal configurations, thereby capturing recurring fishing patterns and their temporal variability. A convolutional autoencoder and a Gaussian Mixture Model (GMM) were used to cluster the VIIRS imagery. Results revealed seven major nighttime light hotspots. Results also identified four cluster patterns: Cluster 0 dominated in December, January, and February, Cluster 1 in March, April, and May, Cluster 2 in July, August, and September, and Cluster 3 in October and November. Interannual variability was also identified. In particular, Clusters 0 and 3 expanded into later months in recent years (2022\u20132024), whereas Cluster 1 contracted. These findings align with environmental changes in the region, including ocean temperature rise and declining primary productivity. By integrating autoencoders with probabilistic clustering, this research demonstrates a framework for uncovering recurrent fishing activity patterns and highlights the utility of satellite imagery with GeoAI in advancing marine fisheries monitoring.<\/jats:p>","DOI":"10.3390\/ijgi15010025","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T10:53:50Z","timestamp":1767610430000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering Fishing Area Patterns Using Convolutional Autoencoder and Gaussian Mixture Model on VIIRS Nighttime Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-8063","authenticated-orcid":false,"given":"Jeong Chang","family":"Seong","sequence":"first","affiliation":[{"name":"School of Field Investigations and Experimental Sciences, Dr. James \u2018Earl\u2019 Perry College of Mathematics, Computing, and Sciences, University of West Georgia, Carrollton, GA 30118, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jina","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Climate-Social Science Convergence, Kyung Hee University, Seoul 02447, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiwon","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Climate-Social Science Convergence, Kyung Hee University, Seoul 02447, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9652-9536","authenticated-orcid":false,"given":"Seung Hee","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Field Investigations and Experimental Sciences, Dr. James \u2018Earl\u2019 Perry College of Mathematics, Computing, and Sciences, University of West Georgia, Carrollton, GA 30118, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9933-2432","authenticated-orcid":false,"given":"Chul Sue","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Climate-Social Science Convergence, Kyung Hee University, Seoul 02447, Republic of Korea"},{"name":"Department of Geography, Kyung Hee University, Seoul 02447, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"579","DOI":"10.5194\/essd-17-579-2025","article-title":"A Comprehensive Global Mapping of Offshore Lighting","volume":"17","author":"Elvidge","year":"2025","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106539","DOI":"10.1016\/j.fishres.2022.106539","article-title":"Nighttime Fishing Vessel Observation in Bohai Sea Based on VIIRS Fishing Vessel Detection Product (VBD)","volume":"258","author":"Li","year":"2023","journal-title":"Fish. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tsuda, M.E., Miller, N.A., Saito, R., Park, J., and Oozeki, Y. (2023). Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea. Remote Sens., 15.","DOI":"10.3390\/rs15112911"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s12601-024-00145-2","article-title":"Analysis of Unmatched Fishing Activities Between VIIRS and Field Data (AIS and V-Pass) Around Korean Peninsula","volume":"59","author":"Lee","year":"2024","journal-title":"Ocean Sci. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, D., Zheng, W., Tang, S., Zhang, L., Liu, Y., and Yu, J. (2025). Nighttime Remote Sensing Analysis of Lit Fishing Boats: Fisheries Management Challenges in the South China Sea (2013\u20132022). Remote Sens., 17.","DOI":"10.3390\/rs17172967"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/s41586-023-06825-8","article-title":"Satellite Mapping Reveals Extensive Industrial Activity at Sea","volume":"625","author":"Paolo","year":"2024","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Geronimo, R.C., Franklin, E.C., Brainard, R.E., Elvidge, C.D., Santos, M.D., Venegas, R., and Mora, C. (2018). Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling. Remote Sens., 10.","DOI":"10.3390\/rs10101604"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, H., Liu, Y., Sun, C., Dong, Y., and Zhang, S. (2021). Satellite Observation of the Marine Light-Fishing and Its Dynamics in the South China Sea. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9121394"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1080\/01431161.2018.1524605","article-title":"Spatiotemporal Pattern Analysis of Potential Light Seine Fishing Areas in the East China Sea Using VIIRS Day\/Night Band Imagery","volume":"40","author":"Luo","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112312","DOI":"10.1016\/j.rse.2021.112312","article-title":"Satellite Observation of a Newly Developed Light-Fishing \u201cHotspot\u201d in the Open South China Sea","volume":"256","author":"Li","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2322762","DOI":"10.1080\/17538947.2024.2322762","article-title":"Satellite Nighttime Remote Sensing Promotes the Spatially Refined Monitoring and Assessment of Offshore Fishery","volume":"17","author":"Tian","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1002\/rse2.368","article-title":"A New Way to Understand Migration Routes of Oceanic Squid (Ommastrephidae) from Satellite Data","volume":"10","author":"Ji","year":"2024","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, R., Yang, X., and Tian, S. (2023). Use of Space-Time Cube Model and Spatiotemporal Hot Spot Analyses in Fisheries\u2014A Case Study of Tuna Purse Seine. Fishes, 8.","DOI":"10.3390\/fishes8100525"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jayeprokash, D., and Gonski, J. (2025). Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies. Information, 16.","DOI":"10.3390\/info16080690"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8063","DOI":"10.1109\/JSTARS.2023.3296485","article-title":"Artificial Intelligence Based On-Board Image Compression for the \u03a6-Sat-2 Mission","volume":"16","author":"Guerrisi","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","first-page":"140","article-title":"Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities","volume":"140","author":"Yang","year":"2025","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113714","DOI":"10.1016\/j.rse.2023.113714","article-title":"Improved Gaussian Mixture Model to Map the Flooded Crops of VV and VH Polarization Data","volume":"295","author":"Guan","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Zong, B., Song, Q., Renqiang Min, M., Cheng, W., Lumezanu, C., Cho, D., and Chen, H. (May, January 30). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. Proceedings of the International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada."},{"key":"ref_20","unstructured":"United Nations (2025, October 09). Exclusive Economic Zone Act No. 5151, Promulgated on 8 August 1996. Available online: https:\/\/www.un.org\/depts\/los\/LEGISLATIONANDTREATIES\/PDFFILES\/KOR_1996_EEZAct.pdf."},{"key":"ref_21","unstructured":"National Geographic Information Institute (NGII) (2025, October 09). The National Atlas of Korea II. Available online: http:\/\/nationalatlas.ngii.go.kr\/pages\/page_2372.php."},{"key":"ref_22","unstructured":"Choi, Y. (2025). Impact of Climate Change on Ocean and Fisheries: Briefing Book 2025, National Institute of Fisheries Science (NIFS). Available online: https:\/\/nifs.go.kr\/cmmn\/file\/climatechange_05.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"639","DOI":"10.47853\/FAS.2023.e56","article-title":"Long-Term Pattern Changes of Sea Surface Temperature during Summer and Winter Due to Climate Change in the Korea Waters","volume":"26","author":"Han","year":"2023","journal-title":"Fish. Aquat. Sci."},{"key":"ref_24","unstructured":"Choi, Y. (2024). 2024 Climate Change Impacts and Research in the Fisheries Sector Report, National Institute of Fisheries Science. Available online: https:\/\/www.nifs.go.kr\/cmmn\/file\/climatechange_04.pdf."},{"key":"ref_25","unstructured":"National Institute of Fisheries Science (NIFS) (2005). Ecology and Fishing Grounds of Major Coastal and Offshore Fisheries Resources in Korea, National Institute of Fisheries Science. Available online: https:\/\/scienceon.kisti.re.kr\/commons\/util\/originalView.do?cn=TRKO201800015682&dbt=TRKO&rn=."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11508","DOI":"10.1002\/jgrd.50873","article-title":"Suomi NPP VIIRS Prelaunch and On-Orbit Geometric Calibration and Characterization","volume":"118","author":"Wolfe","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_27","unstructured":"U.S. Department of Commerce (NOAA NESDIS) (2013). NOAA Technical Report NESDIS 142 Visible\/Infrared Imager Radiometer Suite (VIIRS) Sensor Data Record (SDR) User\u2019s Guide Version 1.1, U.S. Department of Commerce (NOAA NESDIS)."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5860","DOI":"10.1080\/01431161.2017.1342050","article-title":"VIIRS Night-Time Lights","volume":"38","author":"Elvidge","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Elvidge, C.D., Baugh, K., Ghosh, T., Zhizhin, M., Hsu, F.C., Sparks, T., Bazilian, M., Sutton, P.C., Houngbedji, K., and Goldblatt, R. (2022). Fifty Years of Nightly Global Low-Light Imaging Satellite Observations. Front. Remote Sens., 3.","DOI":"10.3389\/frsen.2022.919937"},{"key":"ref_30","unstructured":"(2025, September 27). MarineRegions.Org. Available online: https:\/\/www.marineregions.org\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Talpur, K., Hasan, R., Gocer, I., Ahmad, S., and Bhuiyan, Z. (2025). AI in Maritime Security: Applications, Challenges, Future Directions, and Key Data Sources. Information, 16.","DOI":"10.20944\/preprints202507.0113.v1"},{"key":"ref_32","first-page":"52","article-title":"Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction","volume":"Volume 6791","author":"Masci","year":"2011","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Artificial Neural Networks (ICANN 2011), Espoo, Finland, 14\u201317 June 2011"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","article-title":"A Study of Deep Convolutional Auto-Encoders for Anomaly Detection in Videos","volume":"105","author":"Ribeiro","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhang, X., Liu, F., and Jiao, L. (2018). Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval. Remote Sens., 10.","DOI":"10.3390\/rs10081243"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rahimzad, M., Homayouni, S., Naeini, A.A., and Nadi, S. (2021). An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE). Remote Sens., 13.","DOI":"10.3390\/rs13132501"},{"key":"ref_36","unstructured":"Bishop, C., and Nasrabadi, N. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shi, X., Li, Y., and Zhao, Q. (2020). Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12071219"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106983","DOI":"10.1016\/j.compag.2022.106983","article-title":"Reconstruction of Sentinel-2 Derived Time Series Using Robust Gaussian Mixture Models\u2014Application to the Detection of Anomalous Crop Development","volume":"198","author":"Mouret","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, H., He, X., Bai, Y., Liu, X., Wu, Y., Zhao, Y., and Yang, H. (2021). Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13112067"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"J\u00fcnger, M., Liebling, T.M., Naddef, D., Nemhauser, G.L., Pulleyblank, W.R., Reinelt, G., Rinaldi, G., and Wolsey, L.A. (2010). The Hungarian Method for the Assignment Problem. 50 Years of Integer Programming 1958\u20132008: From the Early Years to the State-of-the-Art, Springer.","DOI":"10.1007\/978-3-540-68279-0"},{"key":"ref_42","unstructured":"Ministry of Oceans and Fisheries (2025, December 18). Comprehensive Fisheries Distribution Information: Statistics on Hairtail (Trichiurus lepturus) and Squid (Todarodes pacificus). Available online: https:\/\/www.data.go.kr."},{"key":"ref_43","unstructured":"Ministry of Oceans and Fisheries (2025, December 18). Statistics on Registered Fishing Vessels (By Fishery and Industry). Available online: https:\/\/kosis.kr."},{"key":"ref_44","first-page":"353","article-title":"Long-Term Changes in Biological Characteristics of Common Squid Todarodes pacificus in Response to Environmental and Stock Variability in the Korean Jigging Fishery","volume":"58","author":"Jo","year":"2025","journal-title":"Korean J. Fish. Aquat. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"233","DOI":"10.7837\/kosomes.2020.26.3.233","article-title":"Change the Annual Amplitude of Sea Surface Temperature Due to Climate Change in a Recent Decade around the Korean Peninsula","volume":"26","author":"Han","year":"2020","journal-title":"J. Korean Soc. Mar. Environ. Saf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104135","DOI":"10.1016\/j.jmarsys.2025.104135","article-title":"Rapid Decline of Nutrients in the Subsurface of the East Sea (Japan Sea) over the Past 28 Years","volume":"252","author":"Park","year":"2025","journal-title":"J. Mar. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Joo, H.T., Son, S.H., Park, J.W., Kang, J.J., Jeong, J.Y., Lee, C.I., Kang, C.K., and Lee, S.H. (2016). Long-Term Pattern of Primary Productivity in the East\/Japan Sea Based on Ocean Color Data Derived from MODIS-Aqua. Remote Sens., 8.","DOI":"10.3390\/rs8010025"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/1\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T05:18:12Z","timestamp":1767763092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/1\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["ijgi15010025"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15010025","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2026,1,5]]}}}