---
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}
}
```