Reflectance spectral dataset for ship coating condition classification using portable Vis/NIR spectroscopy

This dataset contains multispectral reflectance measurements of ship coating surfaces collected using a portable Visible/Near-Infrared (Vis/NIR) spectroscopy system equipped with an AS7265X multispectral sensor. The sensor captures spectral responses across 18 discrete wavelength bands ranging from 410 nm to 940 nm, covering the ultraviolet (UV), visible (VIS), and near-infrared (NIR) spectral regions.

The dataset was developed to support the analysis and classification of ship coating conditions using non-destructive sensing techniques combined with machine learning approaches. Each sample represents a coating inspection point and includes a coating condition label based on the Condition Assessment Program (CAP) classification, along with spectral reflectance intensity values recorded from the AS7265X sensor channels.

The dataset includes four coating condition categories:
- Very Good Condition
- Good Condition
- Class Condition
- Poor Condition

These categories represent different levels of coating integrity and corrosion protection performance. Spectral data were collected under controlled measurement conditions to minimize ambient light interference. Each measurement was repeated multiple times. The resulting spectral dataset enables the investigation of spectral patterns associated with coating degradation, corrosion risk, and variations in surface condition, providing a foundation for developing machine learning models for automated ship coating inspection and condition monitoring.

Data collection was conducted at PT Dok dan Perkapalan Kodja Bahari, specifically at the Galangan Jakarta II shipyard facility in 2024, where coating inspection measurements were conducted on the surfaces of operational vessels.

This research was funded by the Directorate of Research, Technology, and Community Service under the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, through Fundamental Research Grant Number: 105/E5/PG.02.00.PL/2024.