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DTSTART:20210328T020000
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DTSTAMP:20211228T164011Z
UID:AF2117FF-7D45-4138-A72B-AEFCD4D09ADC
DTSTART;TZID=Europe/Lisbon:20210625T103000
DTEND;TZID=Europe/Lisbon:20210625T113000
DESCRIPTION:In this seminar the application of the Breaks for Additive Seas
 onal and Trend (BFAST) algorithm is described to support the detection of 
 changes in Portuguese forests\, observed by the Sentinel-2 satellite. In p
 articular\, the considered implementation of the algorithm relies on the u
 se of PyOpenCL\, which supports co-processing between CPU and GPU\, from P
 ython wrapping OpenCL\, and allows the efficient multi-core implementation
  of the BFAST to reduce processing time. A total of 204 Sentinel-2 images 
 from a central region of Portugal were used to detect and evaluate the cha
 nges that occurred there from 2017 to 2020\, and to test the use of BFAST 
 for known fires. Also\, a set of 5 tests was carried out to assess the acc
 uracy of the application of BFAST\, and to inspect the impact of different
  time series on results quality\, and the impact of the size of the region
  on the computation time.\n\nSpeaker(s): Carolina Oliveira\, \n\nVirtual: 
 https://events.vtools.ieee.org/m/296264
LOCATION:Virtual: https://events.vtools.ieee.org/m/296264
ORGANIZER:l.oliveira@fct.unl.pt
SEQUENCE:2
SUMMARY:Breaks for Additive Seasonal and Trend (BFAST) algorithm applied to
  support the detection of changes in Portuguese forests
URL;VALUE=URI:https://events.vtools.ieee.org/m/296264
X-ALT-DESC:Description: <br /><p>In this seminar the application of the Bre
 aks for Additive Seasonal and Trend (BFAST) algorithm is described to supp
 ort the detection of changes in Portuguese forests\, observed by the Senti
 nel-2 satellite. In particular\, the considered implementation of the algo
 rithm relies on the use of PyOpenCL\, which supports co-processing between
  CPU and GPU\, from Python wrapping OpenCL\, and allows the efficient mult
 i-core implementation of the BFAST to reduce processing time. A total of 2
 04 Sentinel-2 images from a central region of Portugal were used to detect
  and evaluate the changes that occurred there from 2017 to 2020\, and to t
 est the use of BFAST for known fires. Also\, a set of 5 tests was carried 
 out to assess the accuracy of the application of BFAST\, and to inspect th
 e impact of different time series on results quality\, and the impact of t
 he size of the region on the computation time.</p>
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