Update README.md

README.md

@@ -1,34 +1,34 @@
----
-license: mit
-task_categories:
-- object-detection
-- image-feature-extraction
-tags:
-- aviation
-- remote-sensing
-- satellite-imagery
-- computer-vision
-size_categories:
-- 1K<n<10K
----
-
-# Runway Piano Markings Dataset
-
-A specialized computer vision dataset featuring 8,000 high-resolution satellite images of airport runway threshold markings (piano keys), optimized for detection, localization, and regression tasks.
-
-
-
-## Overview
-This dataset contains 8,000 standardized 640×640 satellite images captured from Google Maps. Each image focuses on "piano markings"—the critical threshold indicators at the start of a runway. To facilitate easier training for detection and regression models, all images share a consistent heading and orientation, minimizing the need for complex rotational data augmentation.
-
-## Dataset Structure
-The dataset is organized into two primary directories:
-
-```text
-dataset/
-├── images/
-│   ├── LFPG_09L.png  # Naming: {airport_ident}_{runway_ident}.png
-│   └── ...           # 8,000 PNG files
-└── labels/
-    ├── LFPG_09L.txt  # Corresponding annotation file
-    └── ...           # 8,000 TXT files
+---
+license: mit
+task_categories:
+- object-detection
+- image-feature-extraction
+tags:
+- aviation
+- remote-sensing
+- satellite-imagery
+- computer-vision
+size_categories:
+- 1K<n<10K
+---
+
+# Runway Piano Markings Dataset
+
+A computer vision dataset of airport runway threshold markings (piano keys), for object detection.
+
+
+
+## Overview
+This dataset contains 8,000 satellite images of size 640×640 captured from Google Maps. Each image focuses on "piano markings", the threshold indicators at the start of a runway. To make the task easier, all runways are oriented up.
+
+## Dataset Structure
+The dataset is organized into two primary directories:
+
+```text
+dataset/
+├── images/
+│   ├── LFPG_09L.png  # Naming: {airport_ident}_{runway_ident}.png
+│   └── ...           # 8,000 PNG files
+└── labels/
+    ├── LFPG_09L.txt  # Corresponding annotation file
+    └── ...           # 8,000 TXT files