Jamrud Aminuddin, Umi Pratiwi, Mirda Prisma Wijayanto, Tika Ayunda Vita, Maskhiyatus Shokib, Ulil Azmi, Dian Rizqi Saputra, Nunung Nurhayati, Budi Pratikno, Eca Indah Anggraeni, Syahrul Fadholi Gumelar, Albertus Sulaiman, Pakhrur Razi, Indra Riyanto
Monitoring illicit fishing in Indonesia’s extensive oceans requires a significant amount of time and work. Remote sensing technology is an ideal solution for monitoring, controlling, and surveying (MCS) systems. The problem of pictures with varying locations is of particular importance to the ship identification process utilizing a remote sensing system due to the ship’s rectangular and narrow dimensions and the high viewing angle from space. This circumstance changes if the firing is done close to items on the Earth’s surface. Given the difficulty of capturing data from a remote location, the convolutional neural network (CNN) approach for processing remote sensing picture data is still being refined, with a focus on recognizing ships. The most recent version of the CNN algorithm demonstrates its capacity to see vessels by accounting for the orientation of objects at sea level with near-perfect accuracy. The issue is that the system must be able to recognize ship movements and kinds in detail as recorded by remote sensing satellite sensors. An online platform for automated ship identification based on satellite imaging data is required to safeguard Indonesia’s maritime sovereignty. Image data were obtained from arbitrary data and captured The image processing step then includes reading and dividing the dataset, building a CNN, and then training and evaluating the CNN model on online platform. The accuracy values for detecting ship and non-ship objects using the CNN model are 99.78% and 99.24%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Jenderal Soedirman, Jl. dr. Suparno 61, Grendeng Purwokerto Utara, Jawa Tengah, 53122, Indonesia; Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Jenderal Soedirman, Jl. dr. Suparno 61, Grendeng Purwokerto Utara, Jawa Tengah, 53122, Indonesia; Departent of Sensing Technology, Faculty of Defense Technology, Universitas Pertahanan Indonesia, Jl. Salemba Raya No.14, Senen, Jakarta Pusat, 10430, Indonesia; Engineering Division of PT Griya Kreasi Teknologi, Jl. Pertamina, Grand Mahkota 2 Blok AA4 No 6, Kab, Jawa Barat, Bekasi, 17610, Indonesia; Research Center for Climate and Atmospheric, National Research and Innovation Agency, Jl. Sangkuriang, Dago, Kecamatan Coblong, Bandung, 40135, Indonesia; Center of Disaster Monitoring and Earth Observation, Physics Department, Universitas Negeri Padang, Padang, 25131, Indonesia; Center for Environmental Studies, Universitas Budi Luhur, Jl. Cileduk Raya 99, Jakarta Selatan, 12260, Indonesia
