30988 research outputs found
Sort by
Assessment of the Comparability of Foreign and Domestic Real Estate Special Investment Funds under EU Law
No full textPain management and recovery after short-stay surgery with a special interest on spine surgery
No full textPerceptions of Yoruba Migrants o AI-assisted Language Tools in Negotiating Identity and Social Inclusion
No full textMapping large European aspens (Populus tremula L.) using national aerial imagery and a U-Net convolutional neural network
No full textBiodiversity supports boreal forest stability, with European aspen (Populus tremula L.) enhancing it by providing crucial habitats and resources for dependent species. Despite existing aspen remote sensing methods, there is no efficient method for mapping it over large areas to support forest management and conservation. In this study, we developed a deep learning-based semantic segmentation model to segment aspen from openly available national aerial imagery, evaluated its accuracy using field data, and compared the segmentation performance between leaf-on and leaf-off conditions. The study was conducted in three areas in southern Finland: Helsinki, Lohja, and Evo. We employed a U-Net encoder-decoder architecture using four-band aerial imagery (RGB and NIR) with a spatial resolution of 0.5 m, captured during both seasonal conditions. Training data consisted of visually identified aspen locations from imagery between 2010 and 2023, covering 290 sites across Finland. We found notable differences in segmentation accuracy between leaf-on and leaf-off conditions and aspen size. The F1-score was higher in leaf-off (0.573) than leaf-on (0.494), with intersection over union (IoU) values of 0.280 and 0.256 for leaf-off and leaf-on, respectively. The diameter at breast height (DBH) of the segmented aspens was similar for both conditions. Moreover, segmentation accuracy improved for larger aspens, with F1-scores reaching 0.663 (leaf-off) and 0.551 (leaf-on) for aspens >20 cm DBH, and 0.710 (leaf-off) and 0.594 (leaf-on) for those >30 cm. The developed model reasonably locates aspen distribution and abundance, assisting forest managers to make informed management decisions
Socioeconomic Health Inequalities in Wellbeing Services County Strategies and Wellbeing Plans
No full textNasaalisuuden arviointi amyotrofisessa lateraaliskleroosissa: Systemaattinen kirjallisuuskatsaus
No full textInterpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
No full textWe propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model. The method identifies which unit or units of the model contain most (or least) information about the channel parameter(s) of the interest, providing insights at both global and local levels—with global explanations aggregating local ones. Experiments on link-level simulations demonstrate the method’s effectiveness in identifying units that contribute most (and least) to signal-to-noise ratio processing. Although we focus on a radio receiver model, the method generalizes to other neural network architectures and applications, offering robust estimation even in high-dimensional settings
