UTOSD: UAV-based Terrestrial Oil Spill Dataset UTOSD is a UAV-based terrestrial oil spill semantic segmentation dataset designed for developing and evaluating deep learning methods for land oil spill detection. The dataset contains 665 high-resolution RGB images captured using DJI Matrice-series UAVs over oilfields and their surrounding environments. The images were collected under diverse environmental conditions, including multiple flight altitudes, illumination conditions, and acquisition times. Representative terrestrial scenes include grasslands, bare soil, snow-covered areas, and oilfield facilities, providing a wide variety of challenging backgrounds for semantic segmentation. Pixel-level annotations were manually created using LabelMe and subsequently converted into Pascal VOC binary segmentation masks. In the annotation masks, a pixel value of 0 represents the background, while 1 represents the oil spill class. The dataset is organized into three corresponding folders: 1. JPEGImages/: Original RGB images. 2. labels/: Original LabelMe annotation files (.json). 3. SegmentationClass/: Pascal VOC binary segmentation masks (.png). Each image, JSON annotation, and binary mask share the same filename to ensure one-to-one correspondence. UTOSD is intended for research on UAV remote sensing, semantic segmentation, lightweight vision models, and intelligent monitoring of terrestrial oil spills.
This dataset is associated with the study: "First use of visible-thermal fusion network approach for robust species monitoring in the tropics".A total of 797 fused visible-thermal drone images were collected at Baluran National Park, East Java, Indonesia. The park is a tropical s…
This project includes the model code and observed heat flux data involved in the manuscript "A Heteroscedastic Neural Network-based Turbulent Heat Flux Parameterization and Its Applications to an Ocean Modeling of the Tropical Pacific".
This dataset contains the complete supplementary research logs and operational protocols supporting the paper: "Toward a Universal Definition of the Digital Twin: A Property-Based Position"Authors: Marian Tcaciuc, Olivier Bouriaud (Ștefan cel Mare University of Suceava) Dataset C…
Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…
This dataset contains the data used in the paper "Hybrid Machine Learning Framework for Microstructure-Based Composition Reconstruction and Hardness Prediction of Al–Si Die-Casting Alloys". The dataset (approximately 3GB) is divided into two main parts: OMtoEDS: Contains the data…
Accurate wood species identification is crucial for biodiversity preservation and forest management. Because traditional identification methods are time-consuming and heavily rely on expert knowledge, automated image-based solutions have become more and more important. This resea…