Sökning: "burned area mapping"

Hittade 3 avhandlingar innehållade orden burned area mapping.

  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. Deep Learning for Wildfire Progression Monitoring Using SAR and Optical Satellite Image Time Series

    Författare :Puzhao Zhang; Yifang Ban; Lorenzo Burzzone; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Remote Sensing; Deep Learning; Wildfire; Burned Area Mapping; Synthetic Aperture Radar; Change Detection; Segmentation; Optical and Radar Image Analysis; Sentinel-1; Sentinel-2; fjärranalys; djup inlärning; skogsbrand; kartläggning av brända områden; Synthetic Aperture Radar; upptäckt av förändringar; segmentering; analys av optiska och radarbilder; Sentinel-1; Sentinel-2; Geoinformatik; Geoinformatics;

    Sammanfattning : Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. LÄS MER

  4. 3. 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