--- 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 Paper arXiv GitHub

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