A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method
Land-use classification plays an important role in urban planning and resource allocation
and had contributed to a wide range of urban studies and investigations. With the …
and had contributed to a wide range of urban studies and investigations. With the …
Defining and designing spatial queries: the role of spatial relationships
A Chaves Carniel - Geo-spatial Information Science, 2023 - Taylor & Francis
Spatial relationships are core components in the design and definition of spatial queries. A
spatial relationship determines how two or more spatial objects are related or connected in …
spatial relationship determines how two or more spatial objects are related or connected in …
Application of a graph convolutional network with visual and semantic features to classify urban scenes
Urban scenes consist of visual and semantic features and exhibit spatial relationships
among land-use types (eg industrial areas are far away from the residential zones). This …
among land-use types (eg industrial areas are far away from the residential zones). This …
AGNet: An attention-based graph network for point cloud classification and segmentation
Classification and segmentation of point clouds have attracted increasing attention in recent
years. On the one hand, it is difficult to extract local features with geometric information. On …
years. On the one hand, it is difficult to extract local features with geometric information. On …
Unsupervised haze removal for high-resolution optical remote-sensing images based on improved generative adversarial networks
One major limitation of remote-sensing images is bad weather conditions, such as haze.
Haze significantly reduces the accuracy of satellite image interpretation. To solve this …
Haze significantly reduces the accuracy of satellite image interpretation. To solve this …
DGANet: A dilated graph attention-based network for local feature extraction on 3D point clouds
Feature extraction on point clouds is an essential task when analyzing and processing point
clouds of 3D scenes. However, there still remains a challenge to adequately exploit local …
clouds of 3D scenes. However, there still remains a challenge to adequately exploit local …
Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban spatial
structure of cities. With the proliferation of individual-level geospatial big data, previous …
structure of cities. With the proliferation of individual-level geospatial big data, previous …
WSGAN: an improved generative adversarial network for remote sensing image road network extraction by weakly supervised processing
Road networks play an important role in navigation and city planning. However, current
methods mainly adopt the supervised strategy that needs paired remote sensing images …
methods mainly adopt the supervised strategy that needs paired remote sensing images …
A geometry-aware attention network for semantic segmentation of MLS point clouds
Semantic segmentation of mobile laser scanning (MLS) point clouds can provide meaningful
3 D semantic information of urban facilities for various applications. However, it still remains …
3 D semantic information of urban facilities for various applications. However, it still remains …
Discovering spatial interaction patterns of near repeat crime by spatial association rules mining
Urban crime incidents always exhibit a structure of spatio-temporal dependence. Exploration
of the spatio-temporal interactions of crime incidents is critical to understanding the …
of the spatio-temporal interactions of crime incidents is critical to understanding the …