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  2. LICENSE.md +12 -0
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.gitattributes CHANGED
@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ # PolyTopoBench images
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+ *.tif filter=lfs diff=lfs merge=lfs -text
LICENSE.md ADDED
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+ # PolyTopoBench data license
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+
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+ PolyTopoBench combines data from several sources. Each component keeps the terms of its source.
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+
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+ | Component | Files | License |
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+ |---|---|---|
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+ | Inria vector annotations, derived from OpenStreetMap | `inria/building/*`, `inria/splits.csv`, `raw/inria/raw/train/gt_polygonized/*` | [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1-0/) |
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+ | Inria imagery and official raster masks | `inria/images/*`, `raw/inria/train/images/*`, `raw/inria/train/gt/*` | Public domain, as stated by the [Inria Aerial Image Labeling dataset](https://project.inria.fr/aerialimagelabeling/); please cite Maggiori et al. (2017) |
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+ | Deventer-512 imagery, masks and annotations | `deventer/*`, `raw/deventer/*` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), following the [upstream release](https://huggingface.co/datasets/HeinzJiao/Deventer-512) |
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+ | Dataset metadata | `tasks.json`, `croissant.json`, `README.md`, `LICENSE.md` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
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+
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+ The Inria vector annotations contain information from OpenStreetMap, © OpenStreetMap contributors, which is made available under the ODbL (https://www.openstreetmap.org/copyright). If you publicly use or redistribute an adapted version of these annotations, the ODbL requires you to release it under the ODbL as well.
README.md CHANGED
@@ -1,3 +1,215 @@
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  ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ pretty_name: PolyTopoBench
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+ license:
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+ - odbl
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+ - cc-by-4.0
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+ task_categories:
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+ - image-segmentation
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+ - object-detection
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+ tags:
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+ - remote-sensing
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+ - aerial-imagery
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+ - geospatial
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+ - polygon
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+ - vectorization
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+ - building-footprint
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+ - land-cover
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+ - topology
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+ size_categories:
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+ - 100K<n<1M
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+ configs:
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+ - config_name: inria_building
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+ default: true
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+ data_files:
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+ - split: train
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+ path: inria/building/train.parquet
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+ - split: validation
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+ path: inria/building/val.parquet
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+ - config_name: deventer_road
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+ data_files:
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+ - split: train
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+ path: deventer/road/train.parquet
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+ - split: validation
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+ path: deventer/road/val.parquet
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+ - config_name: deventer_vegetation
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+ data_files:
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+ - split: train
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+ path: deventer/vegetation/train.parquet
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+ - split: validation
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+ path: deventer/vegetation/val.parquet
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+ - config_name: deventer_unvegetated
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+ data_files:
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+ - split: train
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+ path: deventer/unvegetated/train.parquet
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+ - split: validation
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+ path: deventer/unvegetated/val.parquet
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+ ---
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+
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+ # PolyTopoBench
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+
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+ **PolyTopoBench** is a benchmark for **complex vector polygon generation** from remote-sensing imagery. It evaluates whether a model can produce complete polygon topology, including **interior rings (holes)**, rather than only exterior boundaries.
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+
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+ - **Paper:** *PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery*, NeurIPS 2026 (Evaluations and Datasets Track)
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+ - **Code** (evaluator, baselines, data conversion): https://github.com/seai-lab/PolyTopoBench
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+
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+ The benchmark has four single-class tasks on 512 × 512 aerial image patches, with nearly 300K polygon instances, more than 10K complex polygons and over 42K interior rings.
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+
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+ | Task | Train images | Val images | Instances | Complex instances | Interior rings |
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+ |---|---:|---:|---:|---:|---:|
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+ | `inria/building` | 11,860 | 3,500 | 254,133 | 5,512 | 19,854 |
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+ | `deventer/road` | 1,716 | 432 | 4,533 | 1,066 | 9,992 |
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+ | `deventer/vegetation` | 1,716 | 432 | 23,517 | 859 | 1,477 |
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+ | `deventer/unvegetated` | 1,716 | 432 | 16,552 | 2,942 | 10,749 |
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+
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+ A *complex instance* is a polygon with at least one interior ring.
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+
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+ ## Repository layout
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+
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+ ```
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+ PolyTopoBench/
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+ ├── tasks.json # task registry: file paths, SHA-256 checksums, statistics
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+ ├── inria/
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+ │ ├── images/{train,val}/<city>/*.tif # 512×512 RGB patches, grouped by city
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+ │ ├── splits.csv # patch → split, source tile, patch origin
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+ │ └── building/{train,val}.{json,parquet} # annotations (COCO JSON and GeoParquet)
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+ ├── deventer/
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+ │ ├── images/{train,val}/*.png # 512×512 RGB patches, shared by all three tasks
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+ │ ├── splits.csv # patch → split, upstream image id
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+ │ └── {road,vegetation,unvegetated}/{train,val}.{json,parquet}
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+ └── raw/ # source data before patch extraction (optional)
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+ ├── inria/
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+ │ ├── train/images/*.tif # 180 Inria tiles, 5000×5000
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+ │ ├── train/gt/*.tif # official Inria binary masks (not the PolyTopoBench ground truth)
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+ │ └── raw/train/gt_polygonized/*.geojson # full-tile vector ground truth (OSM-aligned, manually corrected)
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+ └── deventer/{train,val}/
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+ ├── images/*.png # same images as deventer/images
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+ ├── masks/*.png # multi-class masks (values 0–4)
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+ └── annotations/{building,road,vegetation,unvegetated,water}.json # upstream per-class COCO
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+ ```
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+
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+ ### Which files do I need?
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+
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+ | Use case | Download | Size |
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+ |---|---|---:|
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+ | Evaluate your method on the validation sets | `inria/**/val*`, `deventer/**/val*`, `tasks.json` | ~3 GB |
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+ | Train and evaluate your method | `inria/`, `deventer/`, `tasks.json` | ~13 GB |
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+ | Re-tile Inria, run Frame Field Learning, or work with Deventer building/water | add `raw/` | +14 GB |
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ # Benchmark data only (recommended)
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+ snapshot_download(
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+ repo_id="PingL/PolyTopoBench",
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+ repo_type="dataset",
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+ local_dir="PolyTopoBench",
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+ allow_patterns=["tasks.json", "inria/*", "deventer/*"],
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+ )
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+ ```
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+
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+ Add `"raw/*"` to `allow_patterns`, or drop `allow_patterns`, to download everything.
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+
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+ ## Annotation format
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+
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+ Every task split is provided in two equivalent formats. They contain the same polygons, point for point.
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+
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+ ### COCO JSON (`<task>/<split>.json`)
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+
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+ The files follow the COCO layout (`images`, `annotations`, `categories`). Each task has a single category with `category_id = 100`. Image `file_name` is relative to `<dataset>/images/<split>/`, for example `austin/austin1-x0000-y0000.tif`.
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+
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+ > **Important: holes are encoded differently from standard COCO.** `segmentation` is a list of rings `[exterior, hole_1, hole_2, ...]`. Ring 0 is the exterior boundary and every further ring is a **hole** of that exterior. Standard COCO tools such as `pycocotools` treat multiple rings as a union of parts, so they will **fill the holes**. Build geometries yourself as shown below.
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+
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+ Each ring is a flat list `[x1, y1, x2, y2, ...]` in pixel coordinates of the 512 × 512 patch (origin at the top-left, y pointing down), closed so that the first point is repeated at the end. Each annotation is one polygon.
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+
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+ ```python
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+ import json
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+ from pathlib import Path
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+ from PIL import Image
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+ from shapely.geometry import Polygon
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+
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+ root = Path("PolyTopoBench")
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+ coco = json.loads((root / "inria/building/val.json").read_text())
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+
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+ def to_polygon(segmentation):
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+ rings = [list(zip(r[0::2], r[1::2])) for r in segmentation]
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+ return Polygon(rings[0], rings[1:]) # exterior, holes
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+
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+ image_info = coco["images"][0]
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+ image = Image.open(root / "inria/images/val" / image_info["file_name"])
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+ polygons = [to_polygon(a["segmentation"]) for a in coco["annotations"] if a["image_id"] == image_info["id"]]
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+ ```
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+
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+ ### GeoParquet (`<task>/<split>.parquet`)
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+
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+ One row per polygon, with the columns `annotation_id`, `image_id`, `file_name`, `category`, `area`, `hole_count`, and `geometry`. `geometry` is a WKB `Polygon` whose first ring is the exterior and whose remaining rings are holes. Coordinates are in pixels and no CRS is set. The files open directly in GeoPandas:
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+
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+ ```python
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+ import geopandas as gpd
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+
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+ gdf = gpd.read_parquet("PolyTopoBench/deventer/road/val.parquet")
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+ gdf[gdf.hole_count > 0].head()
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+ ```
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+
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+ ### Splits and task registry
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+
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+ - `inria/splits.csv` lists every patch with `file_name, image_id, split, city, source_tile, x0, y0`. `source_tile` and the pixel offset `(x0, y0)` locate the patch inside its 5000 × 5000 Inria tile.
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+ - `deventer/splits.csv` lists every patch with `file_name, image_id, split, raw_image_id`. `raw_image_id` is the image id in the upstream Deventer-512 annotations.
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+ - `tasks.json` lists, for each task and split, the image directory, the annotation files with their SHA-256 checksums, and the image, instance and hole counts.
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+
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+ `image_id` values are contiguous from 1 within each split. The three Deventer tasks share the same images and the same `image_id` values.
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+
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+ ## Evaluation and baselines
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+
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+ The unified evaluator, baseline wrappers and the scripts that convert this release into each baseline's native input format are in the [code repository](https://github.com/seai-lab/PolyTopoBench). The evaluator scores exterior geometry, interior-ring recovery and full ring topology; see the paper for the metric definitions.
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+
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+ ## Construction details
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+
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+ **Inria building.** Vector ground truth was built by aligning OpenStreetMap building footprints to the 180 training tiles of the [Inria Aerial Image Labeling dataset](https://project.inria.fr/aerialimagelabeling/) (Austin, Chicago, Kitsap County, Western Tyrol, Vienna), manually correcting residual mismatches, and restoring interior rings from the official raster masks. The full-tile result is in `raw/inria/raw/train/gt_polygonized/`.
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+ - Tiles are split by city, 80/20: in each city, 29 tiles go to train and 7 to validation.
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+ - Each tile is cut into a 10 × 10 grid of 512 × 512 patches. The last row and column start at pixel 4488, so they overlap their neighbours.
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+ - Polygons are clipped to each patch, and clipped parts whose bounding box is 5 px or less on a side are removed.
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+ - Training patches without buildings are dropped. All validation patches are kept, including 512 without buildings.
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+
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+ **Deventer land cover.** The Deventer tasks use the road, vegetation and unvegetated classes of [Deventer-512](https://huggingface.co/datasets/HeinzJiao/Deventer-512). The upstream validation and test splits are merged into the PolyTopoBench validation split. Image ids are renumbered contiguously; `deventer/splits.csv` maps them back to the upstream ids. 292 Deventer polygons (0.65%), mostly with self-touching rings, are not valid under the OGC simple-feature rules. They are kept as released upstream so that the benchmark ground truth stays unchanged.
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+
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+ ## License
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+
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+ This dataset combines sources with different terms; see [LICENSE.md](LICENSE.md) for the file-by-file breakdown.
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+
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+ - **Inria vector annotations** are derived from OpenStreetMap and are released under the [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1-0/). © OpenStreetMap contributors.
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+ - **Inria imagery and official masks** come from the [Inria Aerial Image Labeling dataset](https://project.inria.fr/aerialimagelabeling/), which is built from public-domain imagery and building footprints. Please cite Maggiori et al. (2017).
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+ - **Deventer-512 imagery and annotations** are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), following the [upstream release](https://huggingface.co/datasets/HeinzJiao/Deventer-512).
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @inproceedings{liu2026polytopobench,
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+ title = {PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery},
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+ author = {Liu, Zeping and Lao, Ni and Sun, Weiwei and Wolff, Gil and Xie, Yiqun and Zhao, Liang and Jiao, Junfeng and Mai, Gengchen},
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+ booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
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+ year = {2026}
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+ }
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+ ```
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+
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+ Please also cite the source datasets:
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+
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+ ```bibtex
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+ @inproceedings{maggiori2017can,
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+ title = {Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark},
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+ author = {Maggiori, Emmanuel and Tarabalka, Yuliya and Charpiat, Guillaume and Alliez, Pierre},
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+ booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
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+ pages = {3226--3229},
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+ year = {2017}
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+ }
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+
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+ @inproceedings{jiao2026acpv,
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+ title = {ACPV-Net: All-Class Polygonal Vectorization for Seamless Vector Map Generation from Aerial Imagery},
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+ author = {Jiao, Weiqin and Cheng, Hao and Vosselman, George and Persello, Claudio},
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+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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+ pages = {13244--13253},
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+ year = {2026}
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+ }```
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+
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+ ## Contact
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+
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+ Please open an issue in the [code repository](https://github.com/seai-lab/PolyTopoBench/issues). Corresponding author: Gengchen Mai (gengchen.mai@austin.utexas.edu).
croissant.json ADDED
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+ },
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+ {
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+ "@type": "cr:Field",
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+ "@id": "deventer-unvegetated-val/geometry",
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+ "name": "geometry",
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+ "description": "WKB-encoded Polygon in 512x512 pixel coordinates (origin top-left, y down, no CRS); ring 0 is the exterior and further rings are holes.",
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+ "dataType": "sc:Text",
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+ "source": {
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+ "fileObject": {
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+ "@id": "deventer-unvegetated-val-parquet"
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+ },
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+ "extract": {
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+ "column": "geometry"
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+ }
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+ }
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+ }
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+ ]
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+ }
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+ ],
1574
+ "rai:dataLimitations": "Inria building: This dataset is intended for evaluating building-footprint polygon generation from high-resolution aerial imagery. It is limited to the geographic and visual distribution of the Inria Aerial Image Labeling data and may not generalize to other regions, sensors, seasons, resolutions, or building styles. The vector annotations are derived from aligned OpenStreetMap footprints and raster masks, so they may inherit incompleteness, temporal mismatch, alignment errors, or residual topology errors. The dataset is not intended for legal, cadastral, surveying-grade, operational mapping, emergency response, surveillance, or other high-stakes uses without independent validation. Deventer land cover: This dataset is intended for evaluating polygonal vectorization of selected Deventer land-cover classes from 512 x 512 aerial image patches. Its applicability is limited to the source city, sensor conditions, spatial resolution, and land-cover categories represented in Deventer-512. The benchmark uses road, vegetation, and unvegetated classes and should not be assumed to generalize to other regions, seasons, sensors, resolutions, or land-cover taxonomies without validation. It is not intended for legal, cadastral, operational mapping, emergency response, surveillance, or other high-stakes uses without independent quality control.",
1575
+ "rai:dataBiases": "Inria building: The dataset reflects selection bias from the Inria city coverage and OpenStreetMap availability. Urban areas, building styles, mapping completeness, and imagery conditions represented in the source cities are likely overrepresented, while rural areas, informal settlements, regions with sparse OpenStreetMap coverage, different sensors, seasons, and lower-resolution imagery may be underrepresented. Annotation bias may arise from OpenStreetMap incompleteness, temporal mismatch with imagery, raster-mask errors, and manual correction decisions. Models trained or evaluated on this dataset may perform better on well-mapped urban buildings similar to the source cities and worse on underrepresented geographies, building types, or annotation conditions. Deventer land cover: The dataset has selection bias toward the Deventer geographic area, its aerial imagery conditions, urban layout, and land-cover patterns. Other regions, rural settings, different climates, sensors, seasons, resolutions, and land-cover distributions may be underrepresented. Annotation bias may arise from the upstream vector annotations, class definitions, split construction, and any residual geometry errors. Models evaluated on this dataset may perform better on Deventer-like scenes and worse on underrepresented geographies or land-cover patterns.",
1576
+ "rai:personalSensitiveInformation": "Inria building: The dataset contains geographic information in the form of aerial imagery and building footprint annotations. It does not intentionally include personal attributes such as gender, age, language, health or medical data, political or religious beliefs, experience or seniority, or individual-level socio-economic status. No personally identifying information is annotated. However, because aerial imagery and building footprints describe physical locations and built environments, downstream use should consider location privacy and potential sensitivity of geospatial information. Deventer land cover: The dataset contains geographic information in the form of aerial imagery and land-cover polygon annotations. It does not intentionally include personal attributes such as gender, age, language, health or medical data, political or religious beliefs, experience or seniority, or individual-level socio-economic status. No personally identifying information is annotated. However, because the data describes physical locations and land-cover structure, downstream use should consider the sensitivity of geospatial information.",
1577
+ "rai:dataUseCases": "Inria building: The dataset represents building footprints and their polygon topology in high-resolution aerial imagery, including exterior boundaries and interior rings. It is intended for evaluating remote-sensing building polygon generation and topology-aware vectorization methods. Validation is limited to the benchmark setting described in the accompanying paper: building-footprint extraction from Inria-style aerial imagery using the provided polygon annotations and evaluation metrics. Validation does not necessarily hold for legal mapping, cadastral boundaries, operational GIS updates, other object classes, other sensors, other resolutions, or geographic regions outside the source distribution. Deventer land cover: The dataset represents land-cover polygons in aerial imagery, with emphasis on road, vegetation, and unvegetated regions and their polygon topology. It is intended for evaluating remote-sensing polygonal vectorization and topology-aware polygon generation methods. Validation is limited to the PolyTopoBench benchmark setting and the selected Deventer-512 classes. Validation does not necessarily hold for other regions, sensors, resolutions, land-cover taxonomies, operational GIS updates, cadastral mapping, or high-stakes decision making.",
1578
+ "rai:dataSocialImpact": "Inria building: This dataset may support positive applications such as better evaluation of building-footprint extraction, map updating, urban analysis, and disaster-response research. Potential risks include misuse for surveillance, privacy-invasive geospatial analysis, or unsupported high-stakes mapping decisions. Because coverage is geographically limited, models may perform unevenly across regions and communities with different building styles or mapping completeness. Users should validate models on target regions and avoid operational or high-stakes use without independent quality control. Deventer land cover: This dataset may support positive applications such as evaluating land-cover vectorization, map updating, urban analysis, and environmental monitoring. Potential risks include misuse for surveillance, privacy-invasive geospatial analysis, or unsupported operational mapping decisions. Because the data is geographically limited to Deventer, models may perform unevenly across other regions or communities with different land-cover patterns. Users should validate models on target regions and avoid high-stakes use without independent quality control.",
1579
+ "rai:hasSyntheticData": false,
1580
+ "prov:wasDerivedFrom": [
1581
+ {
1582
+ "@id": "https://project.inria.fr/aerialimagelabeling/",
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+ "prov:label": "Inria Aerial Image Labeling Dataset",
1584
+ "sc:license": "Public domain imagery and building footprints (per the dataset page)"
1585
+ },
1586
+ {
1587
+ "@id": "https://www.openstreetmap.org",
1588
+ "prov:label": "OpenStreetMap",
1589
+ "sc:license": "ODbL-1.0"
1590
+ },
1591
+ {
1592
+ "@id": "https://huggingface.co/datasets/HeinzJiao/Deventer-512",
1593
+ "prov:label": "Deventer-512",
1594
+ "sc:license": "CC-BY-4.0"
1595
+ }
1596
+ ],
1597
+ "prov:wasGeneratedBy": [
1598
+ {
1599
+ "@type": "prov:Activity",
1600
+ "prov:type": {
1601
+ "@id": "https://www.wikidata.org/wiki/Q4929239"
1602
+ },
1603
+ "prov:label": "Download",
1604
+ "sc:description": "The source imagery and raster building masks come from the Inria Aerial Image Labeling dataset. Building footprint vectors are derived from OpenStreetMap."
1605
+ },
1606
+ {
1607
+ "@type": "prov:Activity",
1608
+ "prov:type": {
1609
+ "@id": "https://www.wikidata.org/wiki/Q5227332"
1610
+ },
1611
+ "prov:label": "Correct Annotation",
1612
+ "sc:description": "OpenStreetMap footprints were aligned to the imagery, residual mismatches were manually reviewed, and interior rings were restored using the raster masks. Automated geometry cleaning was applied, including self-intersection repair and checks for interior-ring containment. The final annotations are released as image-coordinate GeoJSON polygons."
1613
+ },
1614
+ {
1615
+ "@type": "prov:Activity",
1616
+ "prov:type": {
1617
+ "@id": "https://www.wikidata.org/wiki/Q4929239"
1618
+ },
1619
+ "prov:label": "Download",
1620
+ "sc:description": "Download data from Hugging Face"
1621
+ },
1622
+ {
1623
+ "@type": "prov:Activity",
1624
+ "prov:type": {
1625
+ "@id": "https://www.wikidata.org/wiki/Q5227332"
1626
+ },
1627
+ "prov:label": "Expert Examination",
1628
+ "sc:description": "Our human expert examine the annotations and correct some wrong labels"
1629
+ },
1630
+ {
1631
+ "@type": "prov:Activity",
1632
+ "prov:label": "Patch extraction and packaging",
1633
+ "sc:description": "Inria tiles were split by city (29 train / 7 val tiles per city) and cut into a 10x10 grid of 512x512 patches; polygons were clipped to each patch and clipped parts with a bounding-box side of 5 px or less were removed; training patches without buildings were dropped. Deventer-512 val and test splits were merged into val and image ids were renumbered. Each task split was exported as COCO-layout JSON and GeoParquet."
1634
+ }
1635
+ ]
1636
+ }
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