add ReadMe

README.md

@@ -1,48 +1,34 @@
-# Runway Piano Markings Dataset
-
-## Overview
-This dataset contains 8,000 satellite images of runway piano markings captured from Google Maps (satellite view). All images share a consistent heading and orientation, making the dataset suitable for detection, localization, and regression tasks involving runway markings.
-
-## Dataset Structure
-images/
-├── {airport_ident}_{runway_ident}.png
-└── ... (8,000 images, 640×640)
-
-labels/
-├── {airport_ident}_{runway_ident}.txt
-└── ... (one label file per image)
-
-- images/: PNG images of size 640 × 640 pixels
-- labels/: Text files containing the corresponding bounding box annotations
-
-Each image has exactly one corresponding label file with the same base filename.
-
-## File Naming Convention
-Images and labels follow the naming scheme:
-{airport_ident}_{runway_ident}
+---
+license: mit
+task_categories:
+- object-detection
+- image-feature-extraction
+tags:
+- aviation
+- remote-sensing
+- satellite-imagery
+- computer-vision
+size_categories:
+- 1K<n<10K
+---
 
-Example:
-LFPG_09L.png
-LFPG_09L.txt
+# Runway Piano Markings Dataset
 
-## Annotation Format
-Each label file contains a single line with four comma-separated values:
-x0,y0,x1,y1
+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.
 
-Where:
-- (x0, y0): Top-left corner of the runway piano marking
-- (x1, y1): Bottom-right corner of the runway piano marking
 
-All coordinates are expressed in pixel space, relative to the top-left corner of the image.
 
-## Notes
-- Each image contains one runway piano marking
-- All runways are oriented in the same direction due to consistent map heading
-- Suitable for object detection, bounding-box regression, and localization 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.
 
----
-license: mit
-tags:
-  - image-classification
-  - cnn
----
+## 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