Sökning: "djupinlärning"

Visar resultat 1 - 5 av 25 avhandlingar innehållade ordet djupinlärning.

1. 1. 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

3. 2. Large-Scale Multi-Source Satellite Data for Wildfire Detection and Assessment Using Deep Learning

   Författare :Xikun Hu; Yifang Ban; Martin Wooster; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Wildfire; Remote Sensing; Active Fire Detection; Burned Area Mapping; Burn Severity Assessment; Sentinel-2; Landsat; Sentinel-1; Deep Learning; Semantic Segmentation; Image Translation; Skogsbrand; fjärranalys; aktiv branddetektering; kartläggning av bränt område; bedömning av brännskador; Sentinel-2; Landsat; Sentinel-1; djupinlärning; semantisk segmentering; bildöversättning.; Geoinformatik; Geoinformatics;

   Sammanfattning : Earth Observation (EO) satellites have great potential in wildfire detection and assessment at fine spatial, temporal, and spectral resolutions. For a long time, satellite data have been employed to systematically monitor wildfire dynamics and assess wildfire impacts, including (i) to detect the location of actively burning spots, (ii) to map the spatial extent of the burn scars, (iii) to assess the wildfire damage levels. LÄS MER

4. 3. Time, space and control: deep-learning applications to turbulent flows

   Författare :Luca Guastoni; Ricardo Vinuesa; Hossein Azizpour; Philipp Schlatter; Andrea Beck; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; turbulence; deep learning; deep reinforcement learning; flow control; turbulens; djupinlärning; djupförstärkningsinlärning; flödeskontroll; Teknisk mekanik; Engineering Mechanics;

   Sammanfattning : In the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. LÄS MER

5. 4. Source Code Representations of Deep Learning for Program Repair

   Författare :Zimin Chen; Martin Monperrus; Benoit Baudry; Zhendong Su; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Code Representation; Deep Learning; Program Repair; Datalogi; Computer Science;

   Sammanfattning : Deep learning, leveraging artificial neural networks, has demonstrated significant capabilities in understanding intricate patterns within data. In recent years, its prowess has been extended to the vast domain of source code, where it aids in diverse software engineering tasks such as program repair, code summarization, and vulnerability detection. LÄS MER

7. 5. US Equity REIT Returns and Digitalization

   Författare :Birger Axelsson; Han-Suck Song; Herman Donner; Peter Palm; KTH; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; REITs; quantitative easing; quantitative tightening; deep learning; LSTM; REITs; kvantitativa lättnader; kvantitativ åtstramning; djupinlärning; LSTM; Fastigheter och byggande; Real Estate and Construction Management;

   Sammanfattning : This licentiate thesis is a collection of two essays that utilize time-series econometric methods in real estate finance. The first essay applies econometric modelling on Real Estate Investment Trust (REIT) index returns, focusing on estimating the effect of the quantitative easing (QE) and quantitative tightening (QT) programmes on U.S. LÄS MER