JL1-CUP-2024-Second-Format

JL1 CUP 2024 — Second-track format for semantic change detection Bi-temporal 256×256 RGB patches with **per-pixel semantic maps** at times T1/T2 and a **binary change map**, aligned with the data split described in the literature for the JL1 cropland change-detection benchmark (*Second Track* / JL1-Second style layout). Source | Resource | URL | | -------- | --- | | JL1 Mall contest information | contest page | | JL1 data / resources portal | resrepo | Data are provided by the JL1 / Jilin-1 ecosystem and the **“耕地变化检测” (cropland change detection)** competition. This Hub snapshot organizes released splits into a single **semantic change detection (SCD)** directory layout (T1/T2 + semantic labels + change mask). Credit & citation The **train / validation** labeling and split follow the convention used in remote-sensing SCD work on this benchmark. If you use this dataset in research, please cite: Please also **acknowledge the JL1 competition and data provider** in-line (e.g. “JL1 CUP / JL1 Mall”) whenever you report results. Dataset summary | Item | Value | | ---- | ----- | | Task | Semantic change detection (semantic maps at T1 & T2 + change mask) | | Patch size | 256 × 256 × 3 (uint8 PNG) | | Train samples | 4 050 | | Validation samples | 1 950 (with labels) | | Test samples | 2 000 (**bi-temporal images only**, no public ground truth) | Splits are identified by **full paths** (`train/…`, `val/…`, `test/…`). The same numeric filename stem (e.g. `00001`) **does not** denote the same geographic patch across splits. Folder layout Resolving paths:** for `train`, append `.png` to each line of `train.txt` under `T1/` and `T2/`. For `val` and `test`, use each line as the filename under `T1/` and `T2/`. Hugging Face Dataset Viewer Patches are **multi-image samples** (bi-temporal RGB + up to three label maps). If the Hub scanned the modality folders directly, `T1` / `T2` / `GT_*` would be misread as **image-class subfolders** and the table would not group one patch per row. T