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imagewidth (px)
148
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30
116
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148
2.05k
height
int64
130
2.05k
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stringclasses
3 values
augmentation
stringclasses
12 values
parent_image
stringclasses
30 values
region
stringclasses
5 values
acquisition_date
timestamp[s]date
2020-01-14 00:00:00
2025-12-25 00:00:00
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objects
dict
gavin_augships__2020-01-24_scene1_tile_3_3__auto-0005__bright085
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358
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gavin_raw__2020-01-24_scene1_tile_3_3
texas_gulf
2020-01-24T00:00:00
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gavin_augships__2020-01-24_scene1_tile_3_3__auto-0005__bright115
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gavin_augships__2020-01-24_scene1_tile_3_3__auto-0005__contrast090
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2020-01-24T00:00:00
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gavin_augships__2020-01-24_scene1_tile_3_3__auto-0005__crop
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gavin_augships__2020-01-24_scene1_tile_3_3__auto-0005__rot180
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OpenDarkSTS V4 Balanced Ship and Ship-to-Ship Training Dataset

Overview

This dataset is the training, validation and test set used for all eleven detector runs reported in the accompanying SNAME 2026 paper, including the YOLO11s model with the highest reported held-out test score (mAP50 of 0.895). It contains 1,124 Sentinel-2 true-colour images with 3,982 bounding boxes in two classes: single vessels (ship) and pairs of vessels lying alongside one another in the configuration of a ship-to-ship (STS) transfer (ships). The dataset was assembled to give the two classes approximately equal representation in the training partition. It is distributed in the YOLO object-detection format exactly as used for training, with a per-image metadata file for use with the Hugging Face datasets library.

Related resources. Software: github.com/gavingavinchan/OpenDarkSTS. Default detector: gavingavinchan/OpenDarkSTS-YOLO11s. Companion dataset: gavingavinchan/OpenDarkSTS-annotations.

Using the dataset

With YOLO training software.

yolo detect train data=data.yaml model=yolo11s.pt imgsz=960

The training configuration used in the paper (300 epochs, early stopping after 60 epochs without improvement, batch size 8, seed 42, and the online augmentation listed in the paper) can be reproduced with this data.yaml.

With the Hugging Face datasets library.

from datasets import load_dataset
ds = load_dataset("gavingavinchan/OpenDarkSTS-v4-balanced")

Each record contains the image, its pixel dimensions, its source component, the augmentation applied (if any), the parent image for derived crops, the region and acquisition date where known, and an objects field holding the boxes in pixels as [x_min, y_min, width, height].

Composition

The dataset combines three components.

  1. Roboflow-sourced images (381 images). An earlier annotated Sentinel-2 set prepared by a member of the author team on the Roboflow annotation platform (workspace fleetdatascience, project data-science-dark-fleet, version 7, exported on 15 April 2025). The images are Sentinel-2 Level-2A true-colour renderings acquired between 2020 and 2024. The original annotations included polygon outlines, which were converted to enclosing rectangular boxes by the OpenDarkSTS conversion script (932 polygons converted, 19 existing boxes retained). Roboflow generated three augmented copies of each training source image (random horizontal and vertical flip with probability 0.5, crop of 0 to 20%, rotation between -45 and +45 degrees, and brightness adjustment of -15 to +15%); the 336 training images in this component are those augmented copies. The validation and test images in this component are not augmented. The original Roboflow export notes are reproduced in provenance/README.roboflow.txt. File names begin with madhulika__.
  2. OpenDarkSTS reviewed images (222 images). Every completed review in the companion dataset opendarksts-annotation-corpus that contains at least one STS-pair box, with all of its positive boxes. These images and labels are byte-identical to the corresponding files in that dataset. File names begin with gavin_raw__.
  3. STS-pair crops (521 images, training partition only). Focused crops generated offline around STS-pair boxes in the training images of component 2, in order to bring the number of ships boxes up to the number of ship boxes. Each crop is between 256 and 512 pixels per side, and one of the following variants is applied: no change, horizontal flip, vertical flip, rotation by 90, 180 or 270 degrees, brightness factor 0.85 or 1.15, or contrast factor 0.90 or 1.10. Blur, mosaic, mixing and copy-paste were not used. A box is retained in a crop only if at least 50% of its area lies inside the crop. File names begin with gavin_augships__ and record the parent image, the source box and the variant.

Validation and test images of components 1 and 2 are unaugmented source images. The build recipe, counts and file checksums are recorded in provenance/build_record.json.

Class definitions

YOLO id Class Definition
0 ship A single vessel.
1 ships Two vessels lying alongside one another, in the configuration of a ship-to-ship transfer. One box encloses both hulls.

The ships class describes a visual configuration only. It has not been confirmed against vessel tracking records and does not, by itself, establish that a cargo transfer took place.

Partitions and counts

Box counts per partition (these agree with the table "V4 balanced dataset --- split counts" in the paper):

Class Train Validation Test
ship 1,888 41 62
ships 1,925 36 30
Images 1,074 29 21

Counts by component:

Partition Component Images ship ships
Train Roboflow-sourced 336 442 371
Train OpenDarkSTS reviewed 217 876 393
Train STS-pair crops 521 570 1,161
Validation Roboflow-sourced 27 32 29
Validation OpenDarkSTS reviewed 2 9 7
Test Roboflow-sourced 18 51 26
Test OpenDarkSTS reviewed 3 11 4

The OpenDarkSTS reviewed images in the training partition and their crops cover the Strait of Gibraltar (81 images; 220 crops), the Singapore Strait (81; 150), the Gulf of Mexico off Texas (32; 101) and the Gulf of Suez (23; 50). The region of the Roboflow-sourced images is not recorded in the export; file names that include coordinates indicate the Gulf of Mexico off Texas. Three training images contain no boxes.

File layout

data.yaml                                YOLO dataset definition (classes 0: ship, 1: ships)
images/{train,val,test}/                 images (JPEG for the Roboflow-sourced component, PNG otherwise)
images/{train,val,test}/metadata.jsonl   per-image metadata and boxes
labels/{train,val,test}/*.txt            YOLO label files
provenance/build_record.json             build recipe, counts and SHA-256 checksums
provenance/README.roboflow.txt           original Roboflow export notes

Source and attribution

Contains modified Copernicus Sentinel data 2020, 2021, 2022, 2023, 2024 and 2025, processed by the authors. Access to Sentinel data is governed by the Copernicus terms and conditions (https://dataspace.copernicus.eu/terms-and-conditions) and the Sentinel data legal notice. Annotations of the Roboflow-sourced component were prepared on the Roboflow platform (https://roboflow.com); the remaining annotations were produced by the authors with the OpenDarkSTS review tool.

Licence

Creative Commons Attribution 4.0 International (CC BY 4.0). The Roboflow-sourced component was annotated by a co-author, who agreed to its publication under this licence. Users must also attribute the Copernicus Sentinel data as stated above. The OpenDarkSTS software and default detector are licensed separately under AGPL-3.0-only.

Citation

@inproceedings{opendarksts2026,
  title     = {OpenDarkSTS: Open-Source Dark Ship-to-Ship Transfers Detector},
  author    = {Chan, Pou Hei and Dey, Madhulika and Ansari, Sohail and Koola, Paul M.},
  booktitle = {Proceedings of the SNAME Maritime Convention},
  year      = {2026},
  note      = {Citation details to be completed on publication}
}

Limitations

  • Shared source scenes between partitions. The Roboflow-sourced component was augmented before export, and its partition was assigned by Roboflow. Eleven source files of that component appear under the same original file name in both the training partition and the validation or test partition, and several of these pairs are visually near-identical. In addition, the OpenDarkSTS reviewed images were partitioned image by image rather than by scene, so neighbouring chips from the same acquisition (for example the Gulf of Mexico scene of 24 January 2020 and the Singapore Strait scene of 14 May 2024) appear in both training and test images. Reported test scores may therefore be higher than would be obtained on entirely unseen scenes.
  • Small test partition. The test partition contains 21 images and 92 boxes (62 ship, 30 ships), and 18 of the 21 images come from the Roboflow-sourced component. Test metrics derived from it carry substantial statistical uncertainty and are dominated by that component.
  • Incomplete labelling of single vessels. In the OpenDarkSTS reviewed images, the reviewer stopped adding new ship boxes once enough had been collected; some single vessels are not labelled.
  • Class balance is engineered. The approximately 1:1 ratio of ship to ships boxes in the training partition results from the offline STS-pair crops and does not reflect the frequency of STS events at sea.
  • Converted annotations. Boxes in the Roboflow-sourced component were derived from polygon outlines and may differ slightly from boxes drawn directly.
  • Rendered imagery. All images are 8-bit true-colour renderings without georeferencing or the near-infrared band.
  • Overlap with the companion dataset. All 222 OpenDarkSTS reviewed images also appear in opendarksts-annotation-corpus under a different partition rule; the two datasets should not be combined without first removing shared images.
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Models trained or fine-tuned on gavingavinchan/OpenDarkSTS-v4-balanced