--- pretty_name: Reef Support Marine Data license: other license_name: per-source-open-licences license_link: https://huggingface.co/datasets/reefsupport/marine-data#licensing language: - en task_categories: - image-segmentation - object-detection task_ids: - semantic-segmentation - instance-segmentation annotations_creators: - expert-generated - found source_datasets: - original - extended|EPFL-ECEO/coralscapes - extended|LiamLian0727/UIIS - extended|LiamLian0727/UIIS10K - extended|LiamLian0727/USIS10K size_categories: - 10K Reef Support

# Reef Support Marine Data **Underwater imagery with segmentation masks and bounding boxes for coral-reef computer vision, with the licence and attribution of every row recorded.**

GitHub reef.support Hugging Face organisation Licence: per source Rows: 58,803

Preview of the coral-masks configuration

## Dataset summary Reef Support Marine Data v1.0 (October 2026) collects eight openly licensed sources into four ready-to-train configurations: benthic and scene semantic segmentation, instance segmentation and fish detection. Two of the sources contain Reef Support's own annotations, and one of them is Reef Support's own reef-survey imagery. Every row carries its `licence` and `attribution`, so provenance stays attached to the pixels. The release is built with the [marine-data](https://github.com/reefsupport/marine-data) registry, which records the licence of every source before it is admitted. All sources in this release are distributed by their authors under open licences (Apache-2.0, MIT, CC BY 4.0 or CC BY 3.0), and each requires attribution. Sources without a licence that permits redistribution are not part of it. The licences differ per source, so read [Licensing](#licensing) before you share or publish results. That section records the licences as Reef Support read them from the primary sources and is not legal advice. | | | |---|---| | Configurations | 4: `coral-masks`, `scene-masks`, `instance-masks`, `fish-boxes` | | Rows | 58,803 (train 44,142, validation 6,489, test 8,172). `instance-masks` and `fish-boxes` share images. | | Tasks | semantic segmentation, instance segmentation, object detection | | Format | Parquet shards (up to about 0.5 GB each) with images and masks embedded, streamable | | Size | 15.5 GB | | Sources | 8: Coralscapes, SUIM, UIIS, UIIS10K, USIS10K, Roboflow Aquarium Dataset, Seaview labels, Reef Support benthic | | Version | v1.0 (October 2026), built 2026-10-06 | | Code | [github.com/reefsupport/marine-data](https://github.com/reefsupport/marine-data) | ## Supported tasks - **Semantic segmentation** of coral-reef benthos (`coral-masks`) and of underwater scenes (`scene-masks`). - **Instance segmentation** of underwater objects and animals (`instance-masks`). - **Object detection** of fish and other fauna (`fish-boxes`). Labels are in English. Where a source and its labels differ in vocabulary, the native labels are kept. ## Quick start Load a configuration with the `datasets` library, in one of three ways. Every row carries its `licence` and `attribution`; keep them when you redistribute. Quick look in a notebook: stream the split and read the first row. ```python from datasets import load_dataset stream = load_dataset("reefsupport/marine-data", "coral-masks", split="train", streaming=True) row = next(iter(stream)) print(row["licence"], row["attribution"]) ``` Scripts and training: download the config once, cached afterwards. `coral-masks` is 12.6 GB (all splits are fetched); to try the pipeline first, use `scene-masks` (0.2 GB) or the one-source form below. ```python from datasets import load_dataset ds = load_dataset("reefsupport/marine-data", "coral-masks", split="train") ``` One source only: download just that source's shards (file names are `---of-.parquet`). ```python from datasets import load_dataset ds = load_dataset( "reefsupport/marine-data", "coral-masks", split="train", data_files={"train": "data/coral-masks/train-reef-support-seaview-labels-*.parquet"}, ) ``` In a plain Python script, stopping a stream after a few rows can keep the process from exiting (an upstream issue in pyarrow's dataset scanner). Download the config or a single source instead. Decode a mask and look up class names (semantic masks are `uint8` index PNGs, `255` = ignore): ```python import numpy as np from datasets import load_dataset stream = load_dataset("reefsupport/marine-data", "coral-masks", split="train", streaming=True) row = next(iter(stream)) mask = np.array(row["mask"]) # (H, W) class ids names = {c["id"]: c["label_native"] for c in row["class_map"]} # per-image id -> the source's own label ``` Boxes in `fish-boxes` are normalised to 0-1: ```python from datasets import load_dataset stream = load_dataset("reefsupport/marine-data", "fish-boxes", split="train", streaming=True) row = next(iter(stream)) print(row["boxes"][0]) # {"x_min": .., "y_min": .., "x_max": .., "y_max": .., "label": "fish"} ``` Select one source inside a configuration: `ds.filter(lambda s: s == "coralscapes", input_columns="source")`. ## Label mapping The masks keep the native ids of each source, and those ids differ between sources, so `marinedata.labels` maps every source onto one of three shared schemes with fixed class ids. 255 is the ignore value in every scheme and, for partially annotated sources, it means "not annotated" rather than background, so mask the loss to `labels.supervised_classes(source, scheme)` for those sources. Requires Python 3.10 or newer and git. The package is not on PyPI; install the tagged release. The `hf` extra adds `datasets` and Pillow: ```bash pip install "marinedata[hf] @ git+https://github.com/reefsupport/marine-data@v1.0.0" ``` Stream a row and remap it with `remap_row`, which adds `label`, an HxW `uint8` array: ```python from datasets import load_dataset from marinedata import labels stream = load_dataset("reefsupport/marine-data", "coral-masks", split="train", streaming=True) row = labels.remap_row(next(iter(stream)), "benthic-coarse") # adds `label`, an HxW uint8 array names = labels.class_names("benthic-coarse") present = sorted({int(v) for v in row["label"].ravel()} - {labels.IGNORE_INDEX}) print(row["source"], [names[i] for i in present]) ``` In a script, download the config or one source as described under [Quick start](#quick-start) and pass its rows to `remap_row` the same way: ```python from datasets import load_dataset from marinedata import labels ds = load_dataset("reefsupport/marine-data", "scene-masks", split="train") row = labels.remap_row(ds[0], "scene") ``` ```python from datasets import load_dataset from marinedata import labels ds = load_dataset( "reefsupport/marine-data", "coral-masks", split="train", data_files={"train": "data/coral-masks/train-reef-support-seaview-labels-*.parquet"}, ) row = labels.remap_row(ds[0], "benthic-coarse") ``` The other configurations use the `scene` scheme. `scene-masks` rows work with `remap_row` as above: ```python from datasets import load_dataset from marinedata import labels stream = load_dataset("reefsupport/marine-data", "scene-masks", split="train", streaming=True) row = labels.remap_row(next(iter(stream)), "scene") names = labels.class_names("scene") present = sorted({int(v) for v in row["label"].ravel()} - {labels.IGNORE_INDEX}) print(row["source"], [names[i] for i in present]) ``` `instance-masks` rows carry a list of `instances` instead of one semantic mask, so map each instance label with `labels.map_label` (it returns `None` for a label the scheme does not cover): ```python from datasets import load_dataset from marinedata import labels stream = load_dataset("reefsupport/marine-data", "instance-masks", split="train", streaming=True) row = next(iter(stream)) ids = [labels.map_label(row["source"], inst["label_native"], "scene") for inst in row["instances"]] print(row["source"], ids) ``` `fish-boxes` rows have `boxes` and name their source in `source_id`: ```python from datasets import load_dataset from marinedata import labels stream = load_dataset("reefsupport/marine-data", "fish-boxes", split="train", streaming=True) row = next(iter(stream)) ids = [labels.map_label(row["source_id"], box["label"], "scene") for box in row["boxes"]] print(row["source_id"], ids) ``` Ids are the position in the class list, and 255 (ignore) applies to every scheme. | Scheme | Classes (id, name, meaning) | Configurations | |---|---|---| | `benthic-coarse` | 0 `HC` hard coral; 1 `MIL` fire coral; 2 `SC` soft coral; 3 `ALGAE` algae; 4 `ABIOTIC` abiotic; 5 `OTHER_FAUNA` other fauna | `coral-masks` | | `coral-binary` | 0 `NOT_CORAL` every other mapped label; 1 `CORAL` hard, soft and fire coral | `coral-masks` | | `scene` | 0 `BW` background waterbody; 1 `HD` human divers; 2 `PF` plants and sea-grass; 3 `WR` wrecks and ruins; 4 `RO` robots and instruments; 5 `RI` reefs and invertebrates; 6 `FV` fish and vertebrates; 7 `SR` sea-floor and rocks | `scene-masks`, `instance-masks`, `fish-boxes` | **Label policy.** The schemes are set up for training on several sources at once. Dead coral is ignored by default: in `benthic-coarse` and `coral-binary`, a label whose condition is dead goes to 255 (the five dead coral classes of Coralscapes, and `dead clam`), because Coralscapes separates alive, dead and bleached coral while the Reef Support and Seaview masks record no condition. Bleached coral is alive and stays `HC` / `CORAL`. The Coralscapes class `unknown hard substrate` mixes rock with octocorals and is ignored in both schemes. A few Coralscapes labels the registry leaves open are mapped: `sponge` and `anemone` to `OTHER_FAUNA`, `algae covered substrate` to `ALGAE`. To opt out of the dead-coral default, pass `exclude_conditions=()`; `("bleached",)` ignores bleached coral but keeps dead coral: ```python from marinedata import labels table = labels.lut("coralscapes", "benthic-coarse", exclude_conditions=()) # dead coral is HC again ``` Partially annotated sources label a subset of the classes. For `reef-support-benthic-own` and `reef-support-seaview-labels` (`HC` and `SC`, or `CORAL` in `coral-binary`) everything else in the image, including real coral of other kinds, is 255. In `coral-binary` they supply positives and no `NOT_CORAL` examples. The instance and box sources (`uiis`, `uiis10k`, `usis10k`, `roboflow-aquarium`) are partial too: pixels outside the annotated objects are unannotated, not background. `labels.is_dense(source)` returns the flag. **Seaview masks include whole-frame hard coral.** About 8 percent of the Seaview rows label the whole frame as hard coral. These frames are close-ups of dense coral, so their masks carry little boundary information. Weight or sample the source accordingly when you mix it with sources that outline individual colonies. The class lists, the mapping of every native label and the full label policy are in [`docs/LABELS.md`](https://github.com/reefsupport/marine-data/blob/v1.0.0/docs/LABELS.md). ## Dataset structure ### Configurations | config | task | train | validation | test | total | GB | |---|---|---|---|---|---|---| | [`coral-masks`](#coral-masks) | Benthic semantic segmentation | 4,585 | 569 | 843 | 5,997 | 12.6 | | [`scene-masks`](#scene-masks) | Underwater scene semantic segmentation | 1,181 | 163 | 254 | 1,598 | 0.2 | | [`instance-masks`](#instance-masks) | Underwater instance segmentation | 19,409 | 2,284 | 3,603 | 25,296 | 1.4 | | [`fish-boxes`](#fish-boxes) | Object detection (fish and fauna boxes) | 18,967 | 3,473 | 3,472 | 25,912 | 1.4 | Splits are the upstream splits where the source provides them, otherwise a deterministic 80/10/10 hash of `split_group`. ### coral-masks

Preview of the coral-masks configuration

Benthic semantic segmentation of coral-reef imagery: `image`, a `uint8` index `mask` (`255` = ignore) and a per-image `class_map`. The 5,997 images come from three sources with different label vocabularies. Native labels are kept and mapped to a shared taxon node (`taxon_node`) and a coarse group (`coarse`) where a mapping exists. | source | rows | train / val / test | licence | |---|---|---|---| | `coralscapes` | 2,075 | 1,517 / 166 / 392 | Apache-2.0 | | `reef-support-benthic-own` | 1,226 | 930 / 129 / 167 | CC BY 4.0 | | `reef-support-seaview-labels` | 2,696 | 2,138 / 274 / 284 | images CC BY 3.0, masks CC BY 4.0 | ### scene-masks

Preview of the scene-masks configuration

Underwater scene segmentation from SUIM (8 classes including plants and seagrass, fish, reef, wrecks and divers): 1,598 images at 640x480 with `uint8` index masks. Source: `suim`, MIT. ### instance-masks

Preview of the instance-masks configuration

Underwater instance segmentation: `image`, a `uint16` instance-id `mask` (`0` = background) and `instances`, a list with the label and a normalised box for every instance id. 25,296 images. | source | rows | train / val / test | licence | |---|---|---|---| | `uiis` | 4,622 | 3,932 / 690 / 0 | Apache-2.0 | | `uiis10k` | 10,042 | 8,035 / 0 / 2,007 | Apache-2.0 | | `usis10k` | 10,632 | 7,442 / 1,594 / 1,596 | Apache-2.0 | Images in this configuration originate from UIIS, UIIS10K and USIS10K, distributed by their authors under the Apache License 2.0. The original publications state that the images were gathered from public sources and earlier datasets, so copyright in individual images may rest with third parties. Rights holders can request removal via the [issue tracker](https://github.com/reefsupport/marine-data/issues) or the [contact page](https://www.reef.support/contact). ### fish-boxes

Preview of the fish-boxes configuration

Fish and fauna photos with bounding boxes (`x_min`, `y_min`, `x_max`, `y_max` normalised to 0-1, native `label`). 25,912 images (mostly 640x480; sizes vary) with about 3.6 boxes per image (1.8 for usis10k, 7.6 for roboflow-aquarium). For the UIIS-family sources the boxes are derived from the upstream instance annotations, one box per instance. Labels are the upstream vocabularies (for example `fish`, `corals`, `reefs`, `jellyfish`, `human divers`). The build ran a deduplication gate: no image spans two splits, and no upstream test image is in train. | source | images | licence | |---|---|---| | `uiis10k` | 10,042 | Apache-2.0 | | `usis10k` | 10,611 | Apache-2.0 | | `uiis` | 4,622 | Apache-2.0 | | `roboflow-aquarium` | 637 | CC BY 4.0 | Images from `uiis`, `uiis10k` and `usis10k` originate from UIIS, UIIS10K and USIS10K, distributed by their authors under the Apache License 2.0. The original publications state that the images were gathered from public sources and earlier datasets, so copyright in individual images may rest with third parties. Rights holders can request removal via the [issue tracker](https://github.com/reefsupport/marine-data/issues) or the [contact page](https://www.reef.support/contact). ### Data fields `coral-masks`, `scene-masks`, `instance-masks`: | field | type | description | |---|---|---| | `image_sha256` | string | SHA-256 of the image bytes. Stable id and join key. | | `image` | Image | RGB photo. | | `mask` | Image (PNG) | Semantic: `uint8` class id, `255` = ignore. Instance: `uint16` instance id, `0` = background. | | `class_map` | list of struct | `coral-masks` and `scene-masks`. Per image: `id` (value in the mask), `label_native`, `taxon_node`, `coarse` (may be null). | | `instances` | list of struct | `instance-masks` only. `id`, `label_native`, `taxon_node`, `coarse`, `bbox_xyxy_norm` [x_min, y_min, x_max, y_max] in 0-1. | | `source` | string | Source id, see [Licensing](#licensing). | | `licence` | string | Licence of the row's data under its source. | | `annotation_licence` | string | Licence of the annotations. | | `attribution` | string | Text to reproduce when you use or share the row. | | `split`, `split_group` | string | Split, and the group that is never divided across splits. | | `annotator_type` | string | `human` for every configuration in this release. | | `width`, `height`, `lat`, `lon` | int, float | Pixel size; WGS84 degrees (null unless the source provides them). | `fish-boxes` (this configuration uses the US spelling `license`): | field | type | description | |---|---|---| | `image_sha256`, `image` | string, Image | As above. | | `source_id` | string | Source id. | | `license`, `licence_class`, `attribution` | string | Licence, its class (`open` for every row) and the attribution text. | | `boxes` | list of struct | `x_min`, `y_min`, `x_max`, `y_max` (0-1 of width and height), `label`. | | `points` | list | Point annotations; empty in this release. | | `split`, `split_group` | string | Split and non-splittable group. | | `upstream_id`, `upstream_url`, `lat`, `lon`, `depth_m`, `meow_realm`, `habitat` | mixed | Optional context; null unless the source provides it. | | `width`, `height` | int | Pixel size. | Note on the Seaview rows: the `licence` column of `reef-support-seaview-labels` rows reads exactly `CC-BY-3.0-AU (images); CC-BY-4.0 (masks, Reef Support)`, and the `attribution` column cites `CC-BY-3.0-AU` for the images. The images are CC BY 3.0 (unported), as stated by the University of Queensland, and the attribution in the [Licensing](#licensing) table is the one to reproduce. ## Dataset creation ### Sources Eight sources contribute images and annotations; [Licensing](#licensing) lists them with their licences and attribution. Coralscapes, the Seaview labels and Reef Support's benthic set cover coral-reef benthos. SUIM provides general underwater scenes. UIIS, UIIS10K, USIS10K and the Roboflow Aquarium Dataset provide fish, fauna and other underwater objects. ### Annotation process Annotations are the sources' own except where noted. Reef Support drew the masks for the Seaview images, and the benthic set is Reef Support's own imagery and annotation. The Seaview masks are decoded from stitched RGB renders with a fixed 5-colour palette (Hard Coral = 1, Soft Coral = 2, `0` = unlabelled). Boxes for the UIIS-family sources are derived from the upstream instance annotations. Every `annotator_type` in the mask configurations is `human`. ### Build and verification 1. **Source check.** Each source is fetched and its licence is checked against the registry before it is admitted. 2. **Conversion.** The release builder writes sharded Parquet with images and masks embedded, per-row licence and attribution, class maps and splits. Each configuration ends with a `CHECKSUMS.sha256`. 3. **Upload.** Every file is checked (size and sha256 against `CHECKSUMS.sha256`) before and after upload, and row counts are compared with the build. Each configuration has release notes in `docs//`: `README.md`, `LICENSE`, `NOTICE` and `CHECKSUMS.sha256`. To verify a local snapshot: `grep ' data/' docs//CHECKSUMS.sha256 | sha256sum -c -` (macOS: `shasum -a 256 -c`). The code is in the [GitHub repository](https://github.com/reefsupport/marine-data) (`src/marinedata`, release builder in `hf_export.py`). ## Personal and sensitive information Some images show divers or aquarium visitors, and `fish-boxes` has `human` and `human divers` labels. Location fields (`lat`, `lon`) are null unless the source provides them. ## Considerations and limitations - **Dropped rows.** 35 `coral-masks` images were dropped because no class map could be built (`reef-support-benthic-own` 24 of 1,250, `reef-support-seaview-labels` 11 of 2,707). 12 `instance-masks` rows were dropped (`uiis` 6 of 4,628, `uiis10k` 6 of 10,048). - **Splits are not independently audited.** The mask configurations were not checked for near-duplicates, so near-identical frames can fall on both sides of a split. `fish-boxes` ran a deduplication gate. The Seaview `split_group` (site partition plus the first 5 characters of the image id) is provisional. - **One image, several configurations.** `instance-masks` and `fish-boxes` are built from the same UIIS, UIIS10K and USIS10K images and are split per configuration. Join on `image_sha256` before evaluating across configurations. - **Vocabularies differ per source.** `label_native` is the source's own label. The shared `taxon_node` and `coarse` mappings are partial and `coarse` is often null. - **Image provenance.** For the UIIS-family sources, image rights are described under [Licensing](#licensing). - **Geographic and habitat coverage** follows the sources (for example, the Coralscapes images come from Red Sea dive sites) and is uneven. Models trained here may not transfer to other regions or imaging conditions. ## Licensing Each source keeps its own licence. The table lists what is included from each source, the licence, and the attribution to reproduce. The full notices are in `docs//LICENSE` and `docs//NOTICE`. The per-row `licence` and `attribution` columns record the same information at row level. This is a statement of the licences Reef Support recorded, not legal advice. | Source | What is included | Licence | Required attribution | |---|---|---|---| | `coralscapes` | Images and masks, `coral-masks` | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | Sauder et al. 2025, [The Coralscapes Dataset](https://huggingface.co/datasets/EPFL-ECEO/coralscapes), ICCV 2025 Marine Vision Workshop | | `reef-support-benthic-own` | Images and masks, `coral-masks` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Reef Support, CC BY 4.0, https://reef.support | | `reef-support-seaview-labels` | Images, `coral-masks` | [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/) | Gonzalez-Rivero et al., XL Catlin Seaview Survey, Seaview Survey Photo-quadrat and Image Classification Dataset, The University of Queensland, [doi:10.14264/UQL.2019.930](https://doi.org/10.14264/UQL.2019.930), CC BY 3.0. Changes: re-encoded into Parquet | | `reef-support-seaview-labels` | Masks drawn by Reef Support, `coral-masks` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Masks: Reef Support, CC BY 4.0 | | `suim` | Images and masks, `scene-masks` | [MIT](https://github.com/IRVLab/SUIM/blob/master/LICENSE) | Islam et al. 2020, SUIM. Copyright (c) 2020 Md Jahidul Islam. The MIT notice is reproduced in `docs/scene-masks/LICENSE` | | `uiis` | Images and instance masks, `instance-masks`; images and derived boxes, `fish-boxes` | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | Lian et al., WaterMask, ICCV 2023, [UIIS](https://huggingface.co/datasets/LiamLian0727/UIIS) | | `uiis10k` | as `uiis` | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | Li et al. 2025, UWSAM, [UIIS10K](https://huggingface.co/datasets/LiamLian0727/UIIS10K) | | `usis10k` | as `uiis` | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | Lian et al., ICML 2024, [USIS10K](https://huggingface.co/datasets/LiamLian0727/USIS10K) | | `roboflow-aquarium` | Images and boxes, `fish-boxes` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Roboflow, Aquarium Dataset, 2020, CC BY 4.0, https://public.roboflow.com/object-detection/aquarium | Changes made by Reef Support to every source: images and masks were re-encoded into Parquet, `class_map` or `instances` were added, and splits and `split_group` were assigned. Where a row's annotations are Reef Support's own, they are licensed CC BY 4.0. **UIIS, UIIS10K and USIS10K.** Images in these configurations originate from UIIS, UIIS10K and USIS10K, distributed by their authors under the Apache License 2.0. The original publications state that the images were gathered from public sources and earlier datasets, so copyright in individual images may rest with third parties. Rights holders can request removal via the [issue tracker](https://github.com/reefsupport/marine-data/issues) or the [contact page](https://www.reef.support/contact). **Seaview images.** The University of Queensland record for the Seaview Survey dataset (UQ eSpace UQ:734799) states the licence as Creative Commons Attribution 3.0 International (CC BY 3.0). The Seaview rows of this release read `CC-BY-3.0-AU (images); CC-BY-4.0 (masks, Reef Support)` in their `licence` column, and `CC-BY-3.0-AU` in their `attribution` column for the images. The images are CC BY 3.0 (unported) as stated by the University of Queensland, and the attribution in the table above is the one to reproduce. ## Citation If you use this dataset, cite the release and the sources you use. ```bibtex @misc{reefsupport_marine_data_v1, title = {Reef Support Marine Data, v1.0}, author = {{Reef Support B.V.}}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/reefsupport/marine-data}}, note = {Version 1.0 (October 2026). Licences differ per source; see the dataset card.} } ```
BibTeX for the upstream sources ```bibtex @inproceedings{sauder2025coralscapes, title = {The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs}, author = {Sauder, Jonathan and Domazetoski, Viktor and Banc-Prandi, Guilhem and Perna, Gabriela and Meibom, Anders and Tuia, Devis}, booktitle = {Proceedings of the International Conference on Computer Vision Joint Workshop on Marine Vision}, year = {2025}, eprint = {2503.20000}, archivePrefix = {arXiv} } @article{gonzalezrivero2020monitoring, title = {Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach}, author = {Gonz{\'a}lez-Rivero, Manuel and Beijbom, Oscar and Rodriguez-Ramirez, Alberto and others}, journal = {Remote Sensing}, volume = {12}, number = {3}, pages = {489}, year = {2020}, doi = {10.3390/rs12030489} } @misc{gonzalezrivero2019seaview, title = {Seaview Survey Photo-quadrat and Image Classification Dataset}, author = {Gonz{\'a}lez-Rivero, Manuel and others}, year = {2019}, publisher = {The University of Queensland}, doi = {10.14264/UQL.2019.930}, note = {XL Catlin Seaview Survey, UQ eSpace UQ:734799. CC BY 3.0} } @inproceedings{islam2020suim, title = {Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}, author = {Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2020}, eprint = {2004.01241}, archivePrefix = {arXiv} } @inproceedings{lian2023watermask, title = {WaterMask: Instance Segmentation for Underwater Imagery}, author = {Lian, Shijie and others}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, pages = {1305--1315}, year = {2023} } @misc{li2025uwsam, title = {Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation}, author = {Li, Hua and Lian, Shijie and Li, Zhiyuan and Cong, Runmin and Li, Chongyi and Yang, Laurence T. and Zhang, Weidong and Kwong, Sam}, year = {2025}, eprint = {2505.15581}, archivePrefix = {arXiv} } @inproceedings{lian2024usis10k, title = {Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset}, author = {Lian, Shijie and Zhang, Ziyi and Li, Hua and Li, Wenjie and Yang, Laurence Tianruo and Kwong, Sam and Cong, Runmin}, booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML)}, series = {PMLR}, volume = {235}, pages = {29545--29559}, year = {2024} } @misc{roboflow2020aquarium, title = {Aquarium Dataset}, author = {{Roboflow}}, year = {2020}, howpublished = {\url{https://public.roboflow.com/object-detection/aquarium}}, note = {CC BY 4.0} } ```
## Contact - Questions, corrections and rights-holder requests: [GitHub issues](https://github.com/reefsupport/marine-data/issues) or the [Reef Support contact page](https://www.reef.support/contact). - Website: [reef.support](https://reef.support) | Hugging Face: [reefsupport](https://huggingface.co/reefsupport) - Code, registry and release builder: [github.com/reefsupport/marine-data](https://github.com/reefsupport/marine-data) Reef Support builds tools to protect and restore marine ecosystems.