Sökning: "Learning regions"

Visar resultat 6 - 10 av 188 avhandlingar innehållade orden Learning regions.

1. 6. Artificial intelligence in weather and climate prediction : Learning atmospheric dynamics

   Författare :Sebastian Scher; Gabriele Messori; Roberto Buizza; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; atmosfärvetenskap och oceanografi; Atmospheric Sciences and Oceanography;

   Sammanfattning : Weather and climate prediction is dominated by high dimensionality, interactions on many different spatial and temporal scales and chaotic dynamics. This makes many problems in the field quite complex ones, and also state-of-the-art numerical models are - despite their immense computational costs - not sufficient for many applications. LÄS MER

3. 7. Multispectral Remote Sensing and Deep Learning for Wildfire Detection

   Författare :Xikun Hu; Yifang Ban; Ioannis Gitas; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; active fire detection; biome; multi-criteria; Sentinel-2; Landsat-8; burned area mapping; deep learning; semantic segmentation; machine learning.; aktiv branddetektering; biom; multikriterietillvägagångssätt; Sentinel-2; Landsat-8; kartläggning av bränt område; djupinlärning; semantisk segmentering; maskininlärningsmetoderna; Geoinformatik; Geoinformatics;

   Sammanfattning : Remote sensing data has great potential for wildfire detection and monitoring with enhanced spatial resolution and temporal coverage. Earth Observation satellites have been employed to systematically monitor fire activity over large regions in two ways: (i) to detect the location of actively burning spots (during the fire event), and (ii) to map the spatial extent of the burned scars (during or after the event). LÄS MER

4. 8. Organising Regional Innovation Support : Sweden's Industrial Development Centres as Regional Development Coalitions

   Författare :Marie-Louise Eriksson; Linköpings universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; lnnovation support organisations; lnnovation policy; Learning organisations; Development coalitions; Partnership; Organisationai capabilities; Economic geography of innovation; Learning regions; Socio-cultural and institutional context; Path-dependency; Sweden; Finspång; Gnosjö; Tekniska innovationer; regional utveckling; regionalpolitik; innovation; Sverige; INTERDISCIPLINARY RESEARCH AREAS; TVÄRVETENSKAPLIGA FORSKNINGSOMRÅDEN;

   Sammanfattning : This PhD dissertation examines the issues of institutional and policy learning often referred to in discussions about innovation policy in the literature on economic geography of innovation, systems of innovation, and learning regions. A central argument is that in order to enhance OUTknowledge of this learning dimension in innovation policy we need to focus on the level at which much innovation policy is organised and implemented, i. LÄS MER

5. 9. Adapting Deep Learning for Microscopy: Interaction, Application, and Validation

   Författare :Ankit Gupta; Carolina Wählby; Ida-Maria Sintorn; Ola Spjuth; Andreas Hellander; Philip Kollmannsberger; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Microscopy; Human-in-the-Loop; Semi-Supervised Learning; Application-Specific Analysis; Image Classification; Image-to-Image Translation; Template Matching; Computerized Image Processing; Datoriserad bildbehandling;

   Sammanfattning : Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. LÄS MER

7. 10. Deep Learning for Geo-referenced Data : Case Study: Earth Observation

   Författare :Nosheen Abid; Marcus Liwicki; Muhammad Zeshan Afzal; Luleå tekniska universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Artificial Intelligence; Machine Learning; Earth Observation; Computer Vision; Machine Learning; Maskininlärning;

   Sammanfattning : The thesis focuses on machine learning methods for Earth Observation (EO) data, more specifically, remote sensing data acquired by satellites and drones. EO plays a vital role in monitoring the Earth’s surface and modelling climate change to take necessary precautionary measures. LÄS MER