Search Results - "Idbraim, Soufiane"

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  1. 1

    Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review by Saidi, Souad, Idbraim, Soufiane, Karmoude, Younes, Masse, Antoine, Arbelo, Manuel

    Published in Remote sensing (Basel, Switzerland) (01-10-2024)
    “…Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain…”
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    Journal Article
  2. 2

    Atmospheric visibility estimation: a review of deep learning approach by Ait Ouadil, Kabira, Idbraim, Soufiane, Bouhsine, Taha, Carla Bouaynaya, Nidhal, Alfergani, Husam, Cliff Johnson, Charles

    Published in Multimedia tools and applications (01-04-2024)
    “…Estimating atmospheric visibility is a critical task in our current time, particularly in the safety of aviation, navigation, and highway traffic, because it…”
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    Journal Article
  3. 3

    Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities by Hamidou Soumana, Abdoul Nasser, Ben Salah, Mohamed, Idbraim, Soufiane, Boulouz, Abdellah

    “…The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion…”
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    Journal Article
  4. 4

    Atmospheric Visibility Image-Based System for Instrument Meteorological Conditions Estimation: A Deep Learning Approach by Bouhsine, Taha, Idbraim, Soufiane, Bouaynaya, Nidhal Carla, Alfergani, Husam, Ouadil, Kabira Ait, Johnson, Charles Cliff

    “…Visibility degradation is the cause of many road accidents and air crashes around the globe, it is caused and controlled by multiple key factors that hinder…”
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    Conference Proceeding
  5. 5

    Change detection by new DSmT decision rule and ICM with constraints: Application to Argan land cover by Elhassouny, Azeddine, Idbraim, S., Mammass, D., Ducrot, D.

    “…The objective of this work is, in the first place, the integration in a fusion process using hybrid DSmT model, both, the contextual information obtained from…”
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    Conference Proceeding