This repository contains the data products and code required to reproduce the results presented in the article "Deep Learning Models for Estimating Volume and Lorey's Height Across Nordic Countries Using Optical and SAR Satellite Images". The study investigates the use of U-Net deep learning models for estimating forest volume and Lorey's height. The models were first pretrained using wall-to-wall forest resource maps derived from Airborne Laser Scanning (ALS) data in Finland and Norway and subsequently fine-tuned using National Forest Inventory (NFI) plots from Norway. The performance of the U-Net models was compared against baseline methods, including k-nearest neighbours (kNN) and XGBoost. This repository includes: Analysis_of_models_plot.R – code used for model-level accuracy assessment and result visualisation; Analysis_of_stands.R – code used for stand-level accuracy assessment; combined_predictions_df_UNet_kNN_XGB_warea.csv – the observed and predicted dataset used for the accuracy assessments presented in the article. In addition, we provide the trained U-Net model weights used in the study for both forest volume and Lorey's height estimation in the Finnish and Norwegian study areas.
This reproducibility package supports the manuscript “Explainable and Leakage-Conscious Machine Learning for Athlete Injury Risk Modeling Across Heterogeneous Datasets.” It contains the executed and clean analysis notebooks, the corresponding Python script, exact software-version…
Background and Objective: Self-harm is a psychologically damaging behavior, and its accurate differentiation from other wounds (violence, accidents, burns, diabetic ulcers) is critically important in forensic medicine. However, this differentiation often falls into a diagnostic "…
Self-supervised deep learning has emerged as a powerful method for image enhancement when a priori ground-truth references are not available. Stemming from Noise2Noise , it was shown that a convolutional neural network (CNN) can be trained from a noisy input and target pair of th…
The Microgreen Master Dataset (Public Version) is an open RGB image dataset developed to support research in computer vision, artificial intelligence, and smart agriculture. The dataset is intended for training and evaluating machine learning models for automatic classification o…
These are multimodal dataset objects and trained model parameters used in the study. The files are organized in pairs, where each multimodal dataset (.h5mu file) corresponds to a trained model parameter file (.pt) generated using the MIMA (Multimodal Integration with Modality-agn…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…