BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.4.0.1//EN
TZID:Europe/Paris
X-WR-TIMEZONE:Europe/Paris
BEGIN:VEVENT
UID:230@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20240523T140000
DTEND;TZID=Europe/Paris:20240523T140000
DTSTAMP:20260825T133746Z
URL:https://isdm.umontpellier.fr/events/probabilistic-graphical-models-and
 -deep-neural-networks-for-remote-sensing-image-analysis/
SUMMARY:Probabilistic graphical models and deep neural networks for remote 
 sensing image analysis
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice\n\nGiven the current advances in 
 space missions for Earth observation\, it is possible to have access to ve
 ry-high-resolution and multimodal satellite imagery. The data acquired can
  be optical (e.g.\, panchromatic\, multispectral\, and hyperspectral image
 s) or radar\, with different synthetic aperture and various trade-offs bet
 ween resolution and coverage. This offers great application potential in t
 he field of remote sensing. An important role in this context is played by
  semantic segmentation whose purpose is to assign each pixel in an image t
 o a semantic class\, typically related to land cover or land use and with 
 prominent applications in areas such as urban planning\, precision agricul
 ture\, monitoring of forest species\, natural disaster management\, and cl
 imate change monitoring and mitigation. This presentation focuses on novel
  methods for the analysis of multimodal data aimed at fully exploiting all
  the available information\, combining ideas from stochastic models and de
 ep learning. On the one hand\, deep learning is currently the dominant app
 roach to image classification and segmentation. However\, the performances
  of deep learning methods are remarkably influenced by the quantity and qu
 ality of the ground truth used for training. On the other hand\, probabili
 stic graphical models have sparked major interest in the past few years\, 
 because of the ever-growing need for structured predictions. Depending on 
 the underlying graph topology over which they are defined\, they can effec
 tively model spatial and multiresolution information. The idea is to devel
 op approaches leveraging the advantages of these two major methodological 
 families for the exploitation of multimodal remote sensing data and of the
  complementary information they convey. The experimental validations\, con
 ducted with multimodal multispectral\, panchromatic\, and radar satellite 
 images\, suggest the effectiveness of the proposed methods. Best\, Cassio\
 n\nMachine Learning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Saint Priest Campus - Building 5 - Room 02.124\, 860 rue St Priest
 \, Montpellier\, 
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=860 rue St Priest\, Montpel
 lier\, ;X-APPLE-RADIUS=100;X-TITLE=Saint Priest Campus - Building 5 - Room
  02.124:geo:0,0
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20240331T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
END:VCALENDAR