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README.md

@@ -2,22 +2,16 @@
 license: cc-by-4.0
 pretty_name: Pixels Point Polygons
 size_categories:
-- 100K<n<1M
+- 10B<n<100B
 task_categories:
 - image-segmentation
-- object-detection
 tags:
+- IGN
 - Aerial
+- Satellite
 - Environement
 - Multimodal
-- Building
-- Polygon
-- Vectorization
-- LIDAR
-- ALS
-- Image
-language:
-- en
+- Earth Observation
 ---
 
 
@@ -32,9 +26,6 @@ language:
     <b>Figure 1</b>: A view of our dataset of Zurich, Switzerland
 </div>
 
-
-<!-- [[Project Webpage]()]    [[Paper](https://arxiv.org/abs/2412.07899)]    [[Video]()] -->
-
 ## Abstract:
 
 We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. The dataset contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 cm. While many existing datasets primarily focus on the image modality, P$^3$ offers a complementary perspective by also incorporating dense 3D information. We demonstrate that LiDAR point clouds serve as a robust modality for predicting building polygons, both in hybrid and end-to-end learning frameworks. Moreover, fusing aerial LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P<sup>3</sup> dataset is publicly available, along with code and pretrained weights of three state-of-the-art models for building polygon prediction at https://github.com/raphaelsulzer/PixelsPointsPolygons.
@@ -147,4 +138,4 @@ This repository benefits from the following open-source work. We thank the autho
 
 1. [Frame Field Learning](https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning)
 2. [HiSup](https://github.com/SarahwXU/HiSup)
-3. [Pix2Poly](https://github.com/yeshwanth95/Pix2Poly)
+3. [Pix2Poly](https://github.com/yeshwanth95/Pix2Poly)