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

The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics.Most 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 characterization.There 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 modeling.In 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 regions.We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources.Then,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs.We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.The 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.

Learning a hierarchical image manifold for Web image classification

Image classification is an essential task in content-based image retrieval.However,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' expectations.In 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 calculation.Assuming 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 granularity.Object manifold is developed for object-level classification using an algorithm named extended locally linear embedding(ELLE) based on intra-and inter-object difference measures.Scene manifold is built for scene-level classification using an algorithm named locally linear submanifold extraction(LLSE) by combining linear perturbation and region growing.Experimental results show that our method is effective in improving the performance of classifying Web images.