GeoRas2Vec name, figures, link to the code repository

README.md

@@ -10,7 +10,7 @@ tags:
 - instance-segmentation
 - remote-sensing
 - synthetic
-pretty_name: GeoCarto – geological map scans to vector units (4 continents)
+pretty_name: GeoRas2Vec – geological map scans to vector units (4 continents)
 size_categories:
 - 1K<n<10K
 configs:
@@ -24,7 +24,12 @@ configs:
     path: data/test-*.parquet
 ---
 
-# GeoCarto – from geological map images to vector units and faults
+# GeoRas2Vec dataset – from geological map images to vector units and faults
+
+**Code, model and documentation:** [GeoRas2Vec on GitHub](https://github.com/PerfectWin7777/georas2vec) · trained model: [PerfectWin/georas2vec-model](https://huggingface.co/PerfectWin/georas2vec-model)
+
+![Dataset overview](https://huggingface.co/datasets/PerfectWin/georas2vec/resolve/main/figures/dataset_overview.png)
+
 
 512×512 tiles of **geological maps** with pixel-exact ground truth for **map-unit instances** and **faults**,
 covering **4 continents** (North America, Europe, Africa, Oceania). Built to train models that turn a scanned
@@ -67,11 +72,15 @@ only if it lies entirely inside blocks of its split. Real and synthetic GMNA til
 ```python
 from datasets import load_dataset
 import numpy as np
-ds = load_dataset("PerfectWin/geocarto", split="train")
+ds = load_dataset("PerfectWin/georas2vec", split="train")
 x = ds[0]
 rgb = np.array(x["image"]); inst = np.array(x["instances"]).astype(np.int32); faults = np.array(x["faults"]) > 0
 ```
 
+## Test tiles of every source
+
+![Test tiles](https://huggingface.co/datasets/PerfectWin/georas2vec/resolve/main/figures/dataset_thumbnails.png)
+
 ## Sources & licences (attribution required)
 | region | dataset | licence |
 |---|---|---|
@@ -82,12 +91,14 @@ rgb = np.array(x["image"]); inst = np.array(x["instances"]).astype(np.int32); fa
 | Oceania | Geoscience Australia, *Surface geology of Australia 1:1M* (dataset 149179) | CC BY 4.0 |
 
 Because the Ethiopian map is CC BY-SA, the whole dataset is released under **CC BY-SA 4.0**.
-The generator code (`geocarto.py`) and its configuration (`build_config.json`) are included.
+The generator code (`code/build_tiles.py`, also in the [GitHub repository](https://github.com/PerfectWin7777/georas2vec) under `dataset/`) and its configuration (`build_config.json`) are included.
 
 ## Versions
 | version | date | change |
 |---|---|---|
 | **v2.0** (current) | 2026-11-12 | **Minimum mapping unit** applied to the labels (and to the rendering of synthetic tiles): polygons < 40 px or with half-width < 1.5 px are merged into their neighbours, at render and at final resolution. In v1 ~58 % of the "small" instances were rasterisation slivers or fragments hidden under contact lines, i.e. not visible in the image; they made small-polygon metrics meaningless. Same sources, same spatial splits, same seed. |
-| v1.0 | 2026-11-07 | first release (6,600 tiles). Still available: `load_dataset("PerfectWin/geocarto", revision="v1.0")` |
+| v1.0 | 2026-11-07 | first release (6,600 tiles). Still available: `load_dataset("PerfectWin/georas2vec", revision="v1.0")` |
+
+The repository was renamed from `PerfectWin/geocarto` to `PerfectWin/georas2vec` (old links redirect).
 
-Trained model and vectorisation pipeline (polygons + faults → GeoPackage for QGIS/ArcGIS): [PerfectWin/geocarto-model](https://huggingface.co/PerfectWin/geocarto-model).
+Trained model: [PerfectWin/georas2vec-model](https://huggingface.co/PerfectWin/georas2vec-model) · code: [GeoRas2Vec on GitHub](https://github.com/PerfectWin7777/georas2vec).

code/{geocarto.py → build_tiles.py}

@@ -1,5 +1,5 @@
 # =====================================================================================
-#  GEOCARTO : sources vectorielles -> tuiles (scan réaliste, instances, failles)
+#  GEORAS2VEC : sources vectorielles -> tuiles (scan réaliste, instances, failles)
 #  - tuiles RÉELLES  : scan GMNA + vecteurs USGS alignés
 #  - tuiles SYNTHÉTIQUES : la carte est "imprimée" à partir des polygones, dans le style
 #    d'une vraie carte géologique (couleurs chronostratigraphiques, motifs, contacts,