Remote Sensing - Journal - Bridge of Knowledge

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Remote Sensing

ISSN:

2072-4292

Publisher:

MDPI

Disciplines
(Field of Science):

  • automation, electronics, electrical engineering and space technologies (Engineering and Technology)
  • information and communication technology (Engineering and Technology)
  • civil engineering, geodesy and transport (Engineering and Technology)
  • environmental engineering, mining and energy (Engineering and Technology)
  • forestry (Agricultural sciences)
  • agriculture and horticulture (Agricultural sciences)
  • socio-economic geography and spatial management (Social studies)
  • law (Social studies)
  • biotechnology (Natural sciences)
  • Earth and related environmental sciences (Natural sciences)

Ministry points: Help

Ministry points - current year
Year Points List
Year 2024 100 Ministry scored journals list 2024
Ministry points - previous years
Year Points List
2024 100 Ministry scored journals list 2024
2023 100 Ministry Scored Journals List
2022 100 Ministry Scored Journals List 2019-2022
2021 100 Ministry Scored Journals List 2019-2022
2020 100 Ministry Scored Journals List 2019-2022
2019 100 Ministry Scored Journals List 2019-2022
2018 35 A
2017 35 A
2016 35 A
2015 35 A
2014 35 A
2013 35 A

Model:

Open Access

Points CiteScore:

Points CiteScore - current year
Year Points
Year 2023 8.3
Points CiteScore - previous years
Year Points
2023 8.3
2022 7.9
2021 7.4
2020 6.6
2019 6.1
2018 5.6
2017 5.3
2016 5.2
2015 4.3
2014 3.9
2013 3.7
2012 2.7
2011 1.1

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total: 63

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Catalog Journals

Year 2024
  • From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland
    Publication

    - Remote Sensing - Year 2024

    Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and...

    Full text to download in external service

  • Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
    Publication
    • R. W. Aslam
    • H. Shu
    • I. Naz
    • A. Quddoos
    • A. Yaseen
    • K. Gulshad
    • S. Saud Alarifi

    - Remote Sensing - Year 2024

    Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...

    Full text available to download

  • Measuring Tilt with an IMU Using the Taylor Algorithm
    Publication

    This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because...

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  • The Effect of Varying the Light Spectrum of a Scene on the Localisation of Photogrammetric Features
    Publication

    In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., demosaicing and conversion to greyscale, among others. These algorithms respond differently to the varying light spectrum of the scene, which consequently results in the feature...

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