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Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

International Journal of Digital Earth 2023年第1期16卷 430-445页

作者： Junchuan Fan Gautam Thakur Geospatial Science and Human Security Division Oak Ridge National LaboratoryOak RidgeTNUSA

The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use *** previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use *** is a lack of understanding on the impact of spatial scale,semantic granularity,and geographic context on POI-based land use modeling,particularly large-scale land use *** this study,we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic *** developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data ***,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and *** trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic *** classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

关键词： Land use POI geospatial semantic deep learning semantic granularity spatial scale

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Learning a hierarchical image manifold for Web image classification

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第10期13卷 719-735页

作者： Rong ZHU Min YAO Li-hua YE Jun-ying XUAN School of Information Engineering Jiaxing UniversityJiaxing 314001China School of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

Image classification is an essential task in content-based image ***,due to the semantic gap between low-level visual features and high-level semantic concepts,and the diversification of Web images,the performance of traditional classification approaches is far from users' *** an attempt to reduce the semantic gap and satisfy the urgent requirements for dimensionality reduction,high-quality retrieval results,and batch-based processing,we propose a hierarchical image manifold with novel distance measures for *** that the images in an image set describe the same or similar object but have various scenes,we formulate two kinds of manifolds,object manifold and scene manifold,at different levels of semantic *** manifold is developed for object-level classification using an algorithm named extended locally linear embedding(ELLE) based on intra-and inter-object difference *** manifold is built for scene-level classification using an algorithm named locally linear submanifold extraction(LLSE) by combining linear perturbation and region *** results show that our method is effective in improving the performance of classifying Web images.

关键词： Web image classification Manifold learning Image manifold semantic granularity Distance measure

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