README.md

---
pretty_name: CASID
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- remote-sensing
- satellite-imagery
- land-cover
- semantic-segmentation
- domain-adaptation
- climate
- earth-observation
size_categories:
- 100<n<1K
---

# CASID: A Large-scale Climate-Aware Satellite Image Dataset for Domain Adaptive Land-Cover Semantic Segmentation

<p align="center">
  <a href="https://doi.org/10.1016/j.isprsjprs.2023.09.007"><img src="https://img.shields.io/badge/ISPRS_JPRS-205%3A98--114%2C_2023-blue" alt="Paper"></a>
  <a href="https://arxiv.org/abs/2610.21238"><img src="https://img.shields.io/badge/arXiv-2610.21238-b31b1b" alt="arXiv"></a>
  <a href="https://github.com/Linwei-Chen/CASID"><img src="https://img.shields.io/badge/GitHub-Linwei--Chen%2FCASID-black" alt="GitHub"></a>
</p>

This is the **official full release** of the **CASID** dataset.

## 📄 Associated Paper

| | |
|---|---|
| **Title** | *A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation* |
| **Venue** | **ISPRS Journal of Photogrammetry and Remote Sensing**, vol. 205, pp. 98–114, 2023 (top journal in photogrammetry & remote sensing) |
| **Authors** | Songlin Liu¹·², Linwei Chen³ *(co-first authors)*, Li Zhang², Jun Hu⁴, Ying Fu³ *(corresponding)* |
| **Affiliations** | ¹ Wuhan University · ² State Key Laboratory of Geo-information Engineering, Xi'an · ³ Beijing Institute of Technology · ⁴ Sun Yat-sen University |
| **DOI** | [10.1016/j.isprsjprs.2023.09.007](https://doi.org/10.1016/j.isprsjprs.2023.09.007) |
| **arXiv** | [arXiv:2610.21238](https://arxiv.org/abs/2610.21238) |
| **Code** | https://github.com/Linwei-Chen/CASID |

Please cite the paper if you use this dataset (see [Citation](#citation) below).

## 🌍 What is CASID?

CASID is the **first climate-aware land-cover semantic segmentation dataset with multiple domains**. It is designed to study — and to benchmark methods against — the **climate domain gap**: the severe performance degradation that segmentation models suffer when applied to a different climate zone than the one they were trained on.

- **980 satellite images at 5000×5000 px** (1 m/px, from Google Earth; two Singapore images are 5000×4656), collected from **30 regions across Asia** (Tokyo, Seoul, Taipei/Yilan, Chongqing, Jinhua, Chiang Mai, Vientiane, Bhopal, Kuala Lumpur, Singapore, Palembang, …), covering **more than 24,500 km²**;
- **4 sub-datasets (domains) by climate zone** — the first dataset to expose this axis:

| Climate zone | Regions | Images | Forest share (paper stats → this release) |
|---|---|---|---|
| Temperate monsoon (温带季风) | 7 | 284 | 45.4% → 45.5% |
| Subtropical monsoon (亚热带季风) | 7 | 284 | 51.4% → 55.1% |
| Tropical monsoon (热带季风) | 6 | 202 | 46.0% → 53.1% |
| Tropical rainforest (热带雨林) | 10 | 210 | 76.2% → 76.3% |
| **Total** | **30** | **980** | |

> The paper's published per-class statistics were computed with the same exact-color-matching script that caused the tile bug below, so two zones under-counted forest. This release restores those pixels; the difference equals the rescued forest exactly (see [Data Verification](#-data-verification)).

- **Pixel-wise annotations, 5 classes** — annotated by experts:

| id | class | palette color |
|---|---|---|
| 0 | background | black (0,0,0) |
| 1 | building | yellow (255,255,0) |
| 2 | forest | green (85,255,0) |
| 3 | road | red (255,0,0) |
| 4 | water | blue (0,112,255) |

> The original color coding is counter-intuitive (building = yellow, road = red); we keep it so visualizations match the original dataset and paper figures.

## 📉 Why it matters: the climate domain gap

Training and testing DeepLabV2 within/between climate zones (mIoU):

| Train \ Test | TemMs | SubMs | TroMs | TroRf |
|---|---|---|---|---|
| TemMs | **39.9** | 9.4 | 10.9 | **12.2** |
| SubMs | 10.0 | **60.9** | 12.8 | 15.3 |
| TroMs | 12.2 | 12.8 | **56.5** | 9.1 |
| TroRf | **8.5** | 10.2 | 12.8 | **49.0** |

A model trained in one climate zone **collapses** in another. The paper analyzes the gap from five angles (t-SNE global features, GLCM texture, category distribution, spectral values, object shapes) and benchmarks 12 segmentation + 7 unsupervised domain adaptation methods (up to DAFormer) as baselines. CASID is both a training resource and a **challenging benchmark** for generalizable segmentation.

## ⬇️ Download

```bash
pip install -U "huggingface_hub[cli]"

# full dataset (~65 GB)
hf download Linwei-Chen/CASID --repo-type dataset --local-dir CASID-dataset

# or in Python
python -c "
from huggingface_hub import snapshot_download
snapshot_download('Linwei-Chen/CASID', repo_type='dataset', local_dir='CASID-dataset')"
```

> Mainland China mirror: `export HF_ENDPOINT=https://hf-mirror.com`
> Baidu Netdisk mirrors: temperate [Link](https://pan.baidu.com/s/1dyaWq63osAJbgwPbPDIX6Q) · subtropical [Link](https://pan.baidu.com/s/1aSEy_xZP3pxzxglia1J0xw) · tropical monsoon [Link](https://pan.baidu.com/s/1WEZ4daJKPwbvUa0preofAg) · rainforest [Link](https://pan.baidu.com/s/1OJTeQRKd9NOjUGJZMMzHfQ) (code: `casi`)

**Verify integrity** — every file ships with its MD5 in `release_file_hashes.csv`:

```bash
python scripts/verify_download.py --root CASID-dataset
```

## 📁 Dataset Structure

```
CASID/
├── images/{climate_zone}/{region_dir}/{region_dir}_{idx:03d}_img.tif   # 5000×5000 RGB, LZW TIFF
├── labels/{climate_zone}/{region_dir}/{region_dir}_{idx:03d}_gt.png    # palette PNG, values = class ids
├── splits/train.txt  (681)          splits/test.txt  (299)
├── regions.csv            # 30 regions, bilingual (English slug ↔ 中文原名, country, counts, split)
├── file_mapping.csv       # original (Chinese) filenames → released filenames, full provenance
├── colormap.json          # class id ↔ name ↔ RGB
├── release_file_hashes.csv# size + MD5 of every released file
├── verification_report.md # full data audit behind this release
└── scripts/               # casid_split.py (tile cutter) + verify_download.py (integrity check)
```

- `climate_zone` ∈ `temperate_monsoon | subtropical_monsoon | tropical_monsoon | tropical_rainforest`
- `region_dir` = `region{NN}_{english_name}`, all-ASCII; original Chinese names preserved in `regions.csv` / `file_mapping.csv`
- Train/test split follows the paper: **whole regions are either train or test** (22 regions → 681 images; 8 test regions → 299 images), so there is no cross-region leakage.

## 🏷️ Labels

Palette-mode PNGs — one line of code returns the class-id map:

```python
import numpy as np
from PIL import Image

img = Image.open("images/temperate_monsoon/region20_north_of_daqing/region20_north_of_daqing_000_img.tif")
ids = np.array(Image.open(
    "labels/temperate_monsoon/region20_north_of_daqing/region20_north_of_daqing_000_gt.png"))
print(ids.shape, ids.dtype, np.unique(ids))   # (5000, 5000) uint8 [0..4]
```

## ✂️ Cropping 1024×1024 tiles (DOTA-style)

`scripts/casid_split.py` reproduces the paper's training setup and supports the common DOTA conventions:

```bash
# paper-faithful default: 1024 tiles, stride 994 (30 px overlap) → 5×5 = 25 tiles per image
python scripts/casid_split.py --root CASID/ --out TILES/

# DOTA paper setting: stride 512, zero-pad edges
python scripts/casid_split.py --root CASID/ --out TILES_DOTA/ --gap 512 --pad

# mmrotate-style: overlap 200
python scripts/casid_split.py --root CASID/ --out TILES_gap200/ --gap 200 --pad

# multi-scale (DOTA convention 0.5/1.0/1.5); images bilinear, labels nearest
python scripts/casid_split.py --root CASID/ --out TILES_MS/ --rates 0.5 1.0 1.5
```

The default grid was verified **pixel-exact** against the authors' original cropped tiles.

## ✅ Data Verification

Before release we audited all 980 image/label pairs (full details in `verification_report.md`):

- every image fully decodes (978 at 5000×5000, two Singapore images at 5000×4656);
- the original labels are RGB color-coded TIFFs. Every color was mapped to a class id and **all pixels in canonical annotation colors were verified pixel-perfectly (0 mismatches) against the original 1024×1024 training tiles** used in the paper experiments (the tiles cover each image completely: 5×5 grid, stride 994);
- 77 images (mostly subtropical/tropical-monsoon regions) store forest in a slightly different green `(76,230,0)` instead of `(85,255,0)`; the original tile-cutter matched colors exactly and silently dropped those pixels to background, so the authors' own training tiles lost forest/road/water in those regions. For this release those pixels were restored to their intended class (nearest canonical color, cross-checked against tiles and satellite imagery); per-zone class shares now match the paper's published statistics to ±0.0005 in the unaffected zones;
- every released file is hashed in `release_file_hashes.csv` for independent verification.

## 🔎 Known Quirks (kept for provenance, documented in regions.csv)

- Region ids run 02–25 plus six later rainforest regions without original ids (numbered 26–31 here); id 01 was never used.
- Original filenames mixed full-width and ASCII dashes with Chinese names; all names are ASCII now, with the complete original mapping in `file_mapping.csv`.
- Region 12 was named "马来西亚-斯里巴加湾南部" (Malaysia – south of Bandar Seri Begawan); BSB is the capital of Brunei, so the country is recorded as Brunei.
- Region 22 was named "朝鲜-首尔东南" (DPRK – southeast of Seoul); Seoul is in South Korea, recorded as South Korea.
- Two images in the Singapore region (`region26_lim_chu_kang_036/_040`) are 5000×4656; their tiles still form a complete 5×5 grid with the last row clipped to 680 px.
- The source `label/` folders contain 35 stray palette PNGs (Taiwan region) with a BGR-ordered palette and unclear id semantics; they are inconsistent with the label TIFFs and are **not** part of this release.
- The source data also contains the authors' rendered 1024×1024 tiles and GID-style previews; these intermediates are not re-released — regenerate tiles with `scripts/casid_split.py`.

## 📄 License

The dataset is released under **CC BY 4.0**. The code/tools at https://github.com/Linwei-Chen/CASID are MIT.

## Citation

```bibtex
@article{LIU202398,
	title = {A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	volume = {205},
	pages = {98-114},
	year = {2023},
	issn = {0924-2716},
	doi = {10.1016/j.isprsjprs.2023.09.007},
	url = {https://www.sciencedirect.com/science/article/pii/S0924271623002484},
	author = {Songlin Liu and Linwei Chen and Li Zhang and Jun Hu and Ying Fu},
}
```

---

# 中文简介

CASID 是**首个按气候带划分多域的地表覆盖语义分割数据集**，官方完整发布版。对应论文发表于遥感顶级期刊 **ISPRS Journal of Photogrammetry and Remote Sensing**（205 卷，98–114 页，2023）：*A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation*（刘松林、陈林蔚为共同第一作者，付莹教授为通讯作者，[DOI](https://doi.org/10.1016/j.isprsjprs.2023.09.007)）。

- **980 张 5000×5000** 卫星影像（1 米分辨率，Google Earth）+ 逐像素标注，来自亚洲 **30 个区域**（东京、首尔、台湾宜兰、重庆、金华、清迈、万象、博帕尔、吉隆坡、新加坡、巨港……），覆盖超 **24,500 平方公里**；
- **按气候带分四个域**：温带季风（7 区 284 张）、亚热带季风（7 区 284 张）、热带季风（6 区 202 张）、热带雨林（10 区 210 张）——跨气候带训练测试，DeepLabV2 的 mIoU 会从 50–60 崩到 10 左右（温带→雨林仅 12.2）；
- 标注 5 类：0=背景、1=建筑、2=森林、3=道路、4=水体（调色板 PNG，`np.array` 一行读出类别 id）；
- 划分沿用论文实验：**按区域整体分 train（681）/ test（299）**，同区域的图不会一边进训练一边进测试；
- 全 ASCII 文件名，`regions.csv` 保留**中文原名**对照，`file_mapping.csv` 记录原始文件名到发布文件名的完整映射；
- `scripts/casid_split.py` DOTA 式大图滑窗裁切：默认 **1024、步距 994（重叠 30）**，每张图恰好 25 块、与论文实验逐像素一致；也支持 `--gap 512 --pad`（DOTA 论文设置）、`--gap 200`（mmrotate 常用）与 `--rates 0.5 1.0 1.5` 多尺度；
- `scripts/verify_download.py` 一条命令校验下载完整性（`release_file_hashes.csv` 含每个文件的 MD5）；
- 发布前已对全部 980 对数据做完整性与逐像素一致性校验（详见 `verification_report.md`），并修复了原始数据中 77 张图因调色板变体导致的图块森林丢失问题。

使用数据集请引用论文（BibTeX 见上）。GitHub 使用指南：https://github.com/Linwei-Chen/CASID