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  • Open Access


    Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

    Suman Kunwar1,*, Jannatul Ferdush2

    Revue Internationale de Géomatique, Vol.33, pp. 1-13, 2024, DOI:10.32604/rig.2023.047627

    Abstract As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in Artificial Intelligence (AI), computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with red-green-blue (RGB) bands, we achieved an… More > Graphic Abstract

    Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

  • Open Access


    Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

    K. Saranya1,*, K. Selva Bhuvaneswari2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 399-414, 2022, DOI:10.32604/iasc.2022.023149

    Abstract This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification. The proposed approach employs a semantic class label model that classifies the input land cover images into a set of semantic categories and classes depending on the content. The intelligent feature selection algorithm selects the prominent attributes from the given data set using weighted attribute functions and uses fuzzy logic to build the rules based on the membership values. To annotate remote sensing images, the CNN method effectively creates semantics and categorises images. The decision manager then integrates the fuzzy logic rules with… More >

  • Open Access


    Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping

    Yang Liu1, Zeying Lan4, Hanfa Xing2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 311-330, 2019, DOI:10.32604/cmes.2019.04284

    Abstract Web maps represent an effective source for land cover mapping in capturing human activities. However, due to spatial heterogeneity, previous research has mainly focused on generating land cover maps in partial areas. Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale. This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping. First, linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification. Then, spatial factors are constructed from point and polygon features to identify… More >

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