CASID: richer dataset card (paper info, usage, verification), add verify script

README.md

@@ -1,27 +1,62 @@
+---
+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
 
-Official full release of the **CASID** dataset (ISPRS 2023 / IEEE TGRS).
-Paper: *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://github.com/Linwei-Chen/CASID"><img src="https://img.shields.io/badge/GitHub-Linwei--Chen%2FCASID-black" alt="GitHub"></a>
+</p>
 
-**中文说明见下方 [中文简介](#中文简介)。**
+This is the **official full release** of the **CASID** dataset.
 
----
+## 📄 Associated Paper
 
-## Dataset Overview
+| | |
+|---|---|
+| **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) |
+| **Code** | https://github.com/Linwei-Chen/CASID |
 
-CASID contains **980 satellite images at 5000×5000 px** (two images in the Singapore region are 5000×4656) collected from **30 regions across Asia**, covering more than 24,500 km². The images are divided into **four sub-datasets (domains) by climate zone**:
+Please cite the paper if you use this dataset (see [Citation](#citation) below).
 
-| Climate zone | Regions | Images |
-|---|---|---|
-| Temperate monsoon (温带季风) | 7 | 284 |
-| Subtropical monsoon (亚热带季风) | 7 | 284 |
-| Tropical monsoon (热带季风) | 6 | 202 |
-| Tropical rainforest (热带雨林) | 10 | 210 |
-| **Total** | **30** | **980** |
+## 🌍 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:
 
-Every image has a pixel-wise land-cover annotation with **5 categories**:
+| 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** | |
 
-| id | class | color in palette PNG |
+> 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) |
@@ -29,88 +64,133 @@ Every image has a pixel-wise land-cover annotation with **5 categories**:
 | 3 | road | red (255,0,0) |
 | 4 | water | blue (0,112,255) |
 
-> Note: the original annotation color coding is counter-intuitive (building = yellow, road = red). We keep it so that visualizations match the original dataset and the paper figures. Labels are palette-mode PNGs — `np.array(Image.open(p))` returns class ids `0..4` directly.
+> 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 | 10.0 | 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.
 
-## Dataset Structure
+## ⬇️ 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-compressed TIFF
-├── labels/{climate_zone}/{region_dir}/{region_dir}_{idx:03d}_gt.png    # 5000×5000, palette PNG, values = class ids
-├── splits/train.txt  (681 images)   splits/test.txt (299 images)
-├── regions.csv            # bilingual region table (English slug ↔ original Chinese name, country, counts, split)
-├── file_mapping.csv       # original file names → released file names (full provenance)
+├── 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
-├── verification_report.md # data audit performed for this release
-└── scripts/casid_split.py # DOTA-style sliding-window tile cutter (single & multi scale)
+├── 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` examples: `region20_north_of_daqing`, `region07_southwest_of_tokyo` — the mapping to the original Chinese region names is in `regions.csv`.
-- The train/test split follows the original paper experiments: **whole regions are either train or test** (681/299 images).
+- `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.
 
-## Usage
+## 🏷️ Labels
 
-### Loading
+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")
-lbl = Image.open("labels/temperate_monsoon/region20_north_of_daqing/region20_north_of_daqing_000_gt.png")
-ids = np.array(lbl)   # (5000, 5000) uint8, values 0..4
+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)
+## ✂️ Cropping 1024×1024 tiles (DOTA-style)
 
-The default reproduces the paper's training setup: **1024×1024 tiles with stride 994 (30 px overlap)** — each 5000×5000 image yields exactly 5×5 = 25 tiles with no padding.
+`scripts/casid_split.py` reproduces the paper's training setup and supports the common DOTA conventions:
 
 ```bash
-# paper-faithful default (25 tiles per image)
+# 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: 1024 tiles, stride 512 (overlap 512), zero-pad edges
+# 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)
+# 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
 ```
 
-## Data Verification
+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 whose color is one of the five 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);
+- 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;
-- per-color mapping decisions are listed in `verification_report.md` and `colormap.json`.
+- every released file is hashed in `release_file_hashes.csv` for independent verification.
 
-## Known Quirks (kept for provenance, documented in regions.csv)
+## 🔎 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) in the source data; BSB is the capital of Brunei, so the country is recorded as Brunei.
+- 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 instead of 5000×5000; their tiles still form a complete 5×5 grid with the last row clipped to 680 px.
+- 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 image/label tiles and GID-style previews; these intermediate products are not re-released — regenerate tiles with `scripts/casid_split.py`.
+- 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
+## 📄 License
 
-The dataset is released under CC-BY-4.0. The code repository (https://github.com/Linwei-Chen/CASID) is MIT.
+The dataset is released under **CC BY 4.0**. The code/tools at https://github.com/Linwei-Chen/CASID are MIT.
 
 ## Citation
 
 ```bibtex
-@article{chen2023casid,
-  title={A Large-scale Climate-aware Satellite Image Dataset for Domain Adaptive Land-cover Semantic Segmentation},
-  author={Chen, Linwei and others},
-  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
-  year={2023}
+@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 = {https://doi.org/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},
 }
 ```
 
@@ -118,12 +198,15 @@ The dataset is released under CC-BY-4.0. The code repository (https://github.com
 
 # 中文简介
 
-CASID 是一个面向**域自适应地表覆盖语义分割**的大规模气候感知卫星影像数据集（ISPRS 2023 / TGRS 论文官方完整版）。
+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。
 
-- **980 张 5000×5000** 卫星影像 + 逐像素标注，来自亚洲 **30 个区域**，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）；
-- 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` 直接读出类别 id）；
-- 划分沿用论文实验：**按区域整体分 train（681）/ test（299）**，见 `splits/`；
-- `regions.csv` 保留每个区域的**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` 参考 DOTA 的大图滑窗裁切：默认 **1024、步距 994（重叠 30）**，每张图恰好 25 块、无填充，与论文实验完全一致；也支持 DOTA 论文的步距 512、mmrotate 常用的重叠 200，以及 0.5/1.0/1.5 多尺度；
-- 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`。
+使用数据集请引用论文（BibTeX 见上）。GitHub 使用指南：https://github.com/Linwei-Chen/CASID