High-Resolution Mapping of Port Dynamics from Open-Access AIS Data in Tokyo Bay

• URL: http://arxiv.org/abs/2601.20211v1
• Date: Wed, 28 Jan 2026 03:22:13 GMT
• Title: High-Resolution Mapping of Port Dynamics from Open-Access AIS Data in Tokyo Bay
• Abstract summary: We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan's most densely populated urban region.<n>We find an average of $35pm17_textstat$ vessels moving within the bay at any given time, and $293pm22_textstat+65_textsyst-10_textsyst$ vessels entering or leaving the bay daily.<n>These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay's commercial traffic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan's most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at $\sim\,$30~m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of $35\pm17_{\text{stat}}$ vessels moving within the bay at any given time, and $293\pm22_{\text{stat}}+65_{\text{syst}}-10_{\text{syst}}$ vessels entering or leaving the bay daily, with an average gross tonnage of $11{,}860^{+280}_{-\;\,50}$. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay's commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations.

Related papers

• WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects [68.44670476527155]
  WUTDet is a large-scale ship detection dataset with 100,576 images and 381,378 annotated ship instances.<n>We systematically evaluate 20 baseline models from three mainstream detection architectures, namely CNN, Transformer, and Mamba.<n>Results on Ship-GEN show that models trained on WUTDet exhibit stronger generalization under different data distributions.
  arXiv  Detail & Related papers  (2026-04-09T03:28:05Z)
• NASTaR: NovaSAR Automated Ship Target Recognition Dataset [1.8957631297817699]
  This dataset comprises of 3415 ship patches extracted from NovaSAR S-band imagery.<n>It includes distinctive features such as 23 unique classes, inshore/offshore separation, and an auxiliary wake dataset for patches where ship wakes are visible.<n>We validated the dataset applicability across prominent ship-type classification scenarios using benchmark deep learning models.
  arXiv  Detail & Related papers  (2025-12-20T20:42:30Z)
• Maritime Activities Observed Through Open-Access Positioning Data: Moving and Stationary Vessels in the Baltic Sea [0.0]
  We show that coastal vessel activity can be reconstructed from open access data with high accuracy.<n>Vessel counts are provided, along with their uncertainties, for both moving and stationary activity.<n>We infer the most crowded and busiest coastal areas in the Baltic Sea.
  arXiv  Detail & Related papers  (2025-11-28T09:30:33Z)
• Towards Foundational Models for Single-Chip Radar [49.896124982717716]
  mmWave radars are compact, inexpensive, and durable sensors that work regardless of environmental conditions, such as weather and darkness.<n>This comes at the cost of poor angular resolution, especially for inexpensive single-chip radars, which are typically used in automotive and indoor sensing applications.<n>We train a foundational model for 4D single-chip radar, which can predict 3D occupancy and semantic segmentation with quality that is typically only possible with much higher resolution sensors.
  arXiv  Detail & Related papers  (2025-09-15T22:06:17Z)
• CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation [50.2938859756212]
  Port operators need to rapidly synthesize diverse forecast products, such as probabilistic wind maps, into clear, actionable guidance as cyclones approach.<n>We introduce CyPortQA, the first multimodal benchmark tailored to port operations under cyclone threat.<n>CyPortQA assembles 2,917 realworld disruption scenarios from 2015 through 2023, spanning 145 U.S. principal ports and 90 named storms.
  arXiv  Detail & Related papers  (2025-08-19T21:56:54Z)
• The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region [64.30214722988666]
  This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.<n>The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points.
  arXiv  Detail & Related papers  (2025-06-06T09:22:21Z)
• Predicting Barge Presence and Quantity on Inland Waterways using Vessel Tracking Data: A Machine Learning Approach [0.0]
  This study presents a machine learning approach to predict the number of barges transported by vessels on inland waterways.<n> AIS tracks the location of tug and tow vessels, it does not monitor the presence or number of barges transported by those vessels.<n>This insight is also valuable for waterway management and infrastructure operations impacting areas such as targeted dredging operations.
  arXiv  Detail & Related papers  (2024-12-31T19:28:21Z)
• DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps [29.946592817320973]
  DuMapper is an automatic system for large-scale POI verification with the multimodal street-view data at Baidu Maps.<n>DuMapper takes the signboard image and the coordinates of a real-world place as input to generate a low-dimensional vector.<n>It can significantly increase the throughput of POI verification by $50$ times.
  arXiv  Detail & Related papers  (2024-11-27T05:54:33Z)
• Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning [0.0]
  This paper develops a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models, specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet are employed.
  arXiv  Detail & Related papers  (2024-01-05T21:32:25Z)
• xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery [52.67592123500567]
  Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. It is now possible to automate detection of dark vessels day or night, under all-weather conditions. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission.
  arXiv  Detail & Related papers  (2022-06-02T06:53:45Z)
• Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification [4.293083690039338]
  We propose a temporal graph neural network (TGNN) based port classification method to enable vessels to discover gateway ports efficiently. The proposed method processes vessel data to build dynamic graphs capturing-temporal dependencies between a set of static and dynamic navigational features in the data. The experimental results indicate that our TGNN-based port classification method provides an f-score of 95% in classifying ports.
  arXiv  Detail & Related papers  (2022-04-25T12:02:18Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)
• Real-Time target detection in maritime scenarios based on YOLOv3 model [65.35132992156942]
  A novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset.
  arXiv  Detail & Related papers  (2020-02-10T15:25:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.