CrackAirport: A dataset for segmentation for airport pavements in General Aviation

Citation Author(s):
Zefeng
Lyu
Christopher
Starr
Andrew
Yu
Submitted by:
zefeng lyu
Last updated:
Fri, 08/16/2024 - 18:52
DOI:
10.21227/v49r-8092
License:
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Abstract 

CrackAirport features images containing unique elements such as aircraft, T-hangars, vegetation, airport markings and signs, as well as evidence of previous maintenance. The dataset was captured using a Sony ILCE-7RM4A camera mounted on a drone flying at an altitude of 100 feet AGL. The imagery was sourced from various local airports in Tennessee and includes common pavement distresses and environmental patterns typical of airport surfaces. The images were annotated and then cropped into 512x512 pixel segments for training. To ensure a balanced ratio of crack pixels to background pixels, more than 2,000 representative images were selected, specifically excluding those with minimal crack presence.

Instructions: 

The raw images are placed in train_images and the masks are placed in train_masks. The image and mask are paired by name.

Funding Agency: 
Tennessee Department of Transportation

Comments

Dataset Submitted on 08/16/2024

Submitted by zefeng lyu on Fri, 08/16/2024 - 18:47

Thumbnail Updated on 08/16/2024

Submitted by zefeng lyu on Fri, 08/16/2024 - 18:50
Good morning
I am a computer science teacher, I need this database for my student who is working on setting up a calculation and classification model using the service index. THANKS
Submitted by Hicham AMAKDOIF on Tue, 01/21/2025 - 17:01