| ---
|
| pretty_name: EORSSD
|
| task_categories:
|
| - image-segmentation
|
| - mask-generation
|
| size_categories:
|
| - 1K<n<10K
|
| tags:
|
| - salient-object-detection
|
| - remote-sensing
|
| - optical-remote-sensing
|
| - background-removal
|
| configs:
|
| - config_name: default
|
| data_files:
|
| - path: data/train-*
|
| split: train
|
| - path: data/test-*
|
| split: test
|
| dataset_info:
|
| features:
|
| - dtype: string
|
| name: image_name
|
| - dtype: image
|
| name: image
|
| - dtype: image
|
| name: mask
|
| splits:
|
| - name: train
|
| num_examples: 1400
|
| - name: test
|
| num_examples: 600
|
| ---
|
|
|
| # EORSSD
|
|
|
| The **Extended Optical Remote Sensing Saliency Detection** dataset — 2000 nadir
|
| satellite images with binary saliency masks, **1400 train / 600 test**,
|
| as published.
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| ds = load_dataset("nobg/EORSSD", split="test") # 600 rows
|
| ds[0]["image"] # PIL, the original JPEG
|
| ds[0]["mask"] # PIL, the binary label PNG
|
| ds[0]["image_name"] # '0004' — the numeric stem the two are joined on
|
| ```
|
|
|
| ## Why this mirror exists
|
|
|
| EORSSD is the cheapest way to add a genuinely different **imaging geometry** to a
|
| background-removal or salient-object benchmark: nadir view, no horizon, no perspective
|
| size cue, and objects that are often a few dozen pixels across. Sets built from
|
| ground-level photography — DIS5K, DUTS, HRSOD, COD10K and the rest — all share a manifold
|
| that this one does not.
|
|
|
| The authors distribute it as a single 63 MB zip with a flat four-folder layout. This mirror
|
| is that zip in parquet, joined on the numeric filename stem, with **image and label bytes
|
| passed through unmodified** — verified by SHA-256 on all 4000 members. Nothing is
|
| decoded or re-encoded, which matters for the labels specifically: re-encoding a binary
|
| mask can introduce intermediate grey values and quietly change what every downstream metric
|
| measures.
|
|
|
| Published comparator: SAM2-UNeXT reports `S_α` **0.948** on the 600-image
|
| test split.
|
|
|
| ## Two upstream properties to know before you score it
|
|
|
| **70 masks are entirely black** — 52 in
|
| train, 18 in test (3.0 %
|
| of the test split). These are EORSSD's deliberate *no salient object* scenes, not corrupt rows,
|
| and they are kept so the split stays the published 600. But they interact badly
|
| with the usual metrics: with empty ground truth, `S_α` and `MAE` still behave sensibly, while
|
| **IoU, boundary-IoU and F-max are 0 for any non-empty prediction**. A model whose output is
|
| never empty by construction — most matting models — is pinned at 0 on those three metrics for
|
| these rows, so quote `S_α`/`MAE` here and treat the rest with care.
|
|
|
| **The test labels are not uniformly 8-bit binary.** 595 are mode `L`, **4 are `RGB` and 1 is
|
| `RGBA`**, and **5 carry anti-aliased edges** (142–237 grey levels rather than 2). Call
|
| `.convert("L")` and binarize rather than assuming a single-channel two-level PNG. Nothing here
|
| re-encodes the labels, so this is exactly what the authors shipped.
|
|
|
| ## Licensing
|
|
|
| **No license is declared** by the authors (the GitHub API reports `license: null`). It is
|
| left unset here rather than guessed; check with the original authors before any use beyond
|
| research.
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @article{zhang2020dense,
|
| title={Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images},
|
| author={Zhang, Qijian and Cong, Runmin and Li, Chongyi and Cheng, Ming-Ming and Fang, Yuming and Cao, Xiaochun and Zhao, Yao and Kwong, Sam},
|
| journal={IEEE Transactions on Image Processing},
|
| volume={30},
|
| pages={1305--1317},
|
| year={2021}
|
| }
|
| ```
|
|
|
