Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS
This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/6/1082 |
| _version_ | 1797239459531456512 |
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| author | Kodai Yasuda Ryuichi Shibasaki Riku Yasuda Hiroki Murata |
| author_facet | Kodai Yasuda Ryuichi Shibasaki Riku Yasuda Hiroki Murata |
| author_sort | Kodai Yasuda |
| collection | DOAJ |
| description | This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data. |
| first_indexed | 2024-04-24T17:51:52Z |
| format | Article |
| id | doaj.art-8f61f4c10692481c9267c3508df9a389 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T17:51:52Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8f61f4c10692481c9267c3508df9a3892024-03-27T14:02:49ZengMDPI AGRemote Sensing2072-42922024-03-01166108210.3390/rs16061082Terminal Congestion Analysis of Container Ports Using Satellite Images and AISKodai Yasuda0Ryuichi Shibasaki1Riku Yasuda2Hiroki Murata3School of Engineering, The University of Tokyo, Tokyo 113-8656, JapanSchool of Engineering, The University of Tokyo, Tokyo 113-8656, JapanSchool of Engineering, The University of Tokyo, Tokyo 113-8656, JapanResearch Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, JapanThis study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.https://www.mdpi.com/2072-4292/16/6/1082container portterminal congestionsatellite image analysisautomatic identification system (AIS)annotation toolconvolutional neural network (CNN) |
| spellingShingle | Kodai Yasuda Ryuichi Shibasaki Riku Yasuda Hiroki Murata Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS Remote Sensing container port terminal congestion satellite image analysis automatic identification system (AIS) annotation tool convolutional neural network (CNN) |
| title | Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS |
| title_full | Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS |
| title_fullStr | Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS |
| title_full_unstemmed | Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS |
| title_short | Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS |
| title_sort | terminal congestion analysis of container ports using satellite images and ais |
| topic | container port terminal congestion satellite image analysis automatic identification system (AIS) annotation tool convolutional neural network (CNN) |
| url | https://www.mdpi.com/2072-4292/16/6/1082 |
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