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Статті в журналах з теми "Spatial-semantic model"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 19 липня 2025

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1

Peng, Huilin, Yang Wang, and Hao Ge. "Spatial-Semantic Transformer for Spatial Relation Recognition." Journal of Physics: Conference Series 2583, no. 1 (2023): 012001. http://dx.doi.org/10.1088/1742-6596/2583/1/012001.

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Abstract Spatial relation recognition, which aims to predict a spatial relation predicate, has attracted increasing attention in the computer vision study. During tackling this problem, modeling spatial relation of the subjects and objects is of great importance. We find that only using spatial features leads to poor results in predicting the spatial relation. To overcome these challenges, we propose an effective spatial attention module to enhance spatial features using semantic features. After identifying the importance of spatial attention mechanism, we propose a spatial transformer module

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2

Abburu, Sunitha. "Geospatial Semantic Query Engine for Urban Spatial Data Infrastructure." International Journal on Semantic Web and Information Systems 15, no. 4 (2019): 31–51. http://dx.doi.org/10.4018/ijswis.2019100103.

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The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial

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3

Wu, Tao, Jianxin Qin, and Yiliang Wan. "TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 410. http://dx.doi.org/10.3390/ijgi8090410.

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To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along netwo

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4

Han, Dongfeng, Wenhui Li, and Zongcheng Li. "Semantic image classification using statistical local spatial relations model." Multimedia Tools and Applications 39, no. 2 (2008): 169–88. http://dx.doi.org/10.1007/s11042-008-0203-6.

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5

Mościcka, Albina. "Europeana Data Model in GIS for movable heritage." Geografie 120, no. 4 (2015): 527–41. http://dx.doi.org/10.37040/geografie2015120040527.

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The paper proposes to use European resources in GIS as a set of multi-spatial objects with semantic relations to the space. It improves the analysis and visualization of geographic or contextual associations between various items. This paper aims to integrate the Europeana Data Model with GIS for movable heritage based on semantic relations of movable objects with the space. All classes and properties of the EDM were analyzed. Classes and properties containing spatial information were examined and their semantic relations to the space were proposed. All aspects of the relations of movable heri

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6

Jia, Chengyou, Minnan Luo, Zhuohang Dang, et al. "SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-Form Layout-to-Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2480–88. http://dx.doi.org/10.1609/aaai.v38i3.28024.

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Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate realistic and complex scene images from user-specified layouts, has risen to prominence. However, existing methods transform layout information into tokens or RGB images for conditional control in the generative process, leading to insufficient spatial and semantic controllability of individual instances. To address these limitations, we propose a novel Spatial-Semantic Map

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7

Li, Wenchao, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun, and Jiyong Zhang. "STS: Spatial–Temporal–Semantic Personalized Location Recommendation." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 538. http://dx.doi.org/10.3390/ijgi9090538.

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The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations,

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8

Huang, Xinlei, Zhiqi Ma, Dian Meng, et al. "PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 326–33. https://doi.org/10.1609/aaai.v39i1.32010.

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Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of sem

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9

Wang, Beibei, Youfang Lin, Shengnan Guo, and Huaiyu Wan. "GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4402–9. http://dx.doi.org/10.1609/aaai.v35i5.16566.

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Traffic accident forecasting is of great importance to urban public safety, emergency treatment, and construction planning. However, it is very challenging since traffic accidents are affected by multiple factors, and have multi-scale dependencies on both spatial and temporal dimensional features. Meanwhile, traffic accidents are rare events, which leads to the zero-inflated issue. Existing traffic accident forecasting methods cannot deal with all above problems simultaneously. In this paper, we propose a novel model, named GSNet, to learn the spatial-temporal correlations from geographical an

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10

Shen, Xiang, Dezhi Han, Chongqing Chen, Gaofeng Luo, and Zhongdai Wu. "An effective spatial relational reasoning networks for visual question answering." PLOS ONE 17, no. 11 (2022): e0277693. http://dx.doi.org/10.1371/journal.pone.0277693.

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Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship r

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11

Wang, Shiwei, Long Lan, Xiang Zhang, and Zhigang Luo. "GateCap: Gated spatial and semantic attention model for image captioning." Multimedia Tools and Applications 79, no. 17-18 (2020): 11531–49. http://dx.doi.org/10.1007/s11042-019-08567-0.

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12

Han, Hui-Hui, and Lei Fan. "A New Semantic Segmentation Model for Supplementing More Spatial Information." IEEE Access 7 (2019): 86979–88. http://dx.doi.org/10.1109/access.2019.2915088.

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13

Ouyang, Wensi. "Design of Semantic Matching Model of Folk Music in Occupational Therapy Based on Audio Emotion Analysis." Occupational Therapy International 2022 (June 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/6841445.

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The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of tex

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14

Huang, Wei, Zhuoming Gu, Mengfan Xu, and Xiaofeng Lu. "Spatial–Adaptive Replay for Foreground Classes in Class-Incremental Semantic Segmentation." Electronics 14, no. 7 (2025): 1338. https://doi.org/10.3390/electronics14071338.

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Class-Incremental Semantic Segmentation (CISS) addresses the challenge of catastrophic forgetting in semantic segmentation models. In autonomous driving scenarios, the model can learn the background class information from the new data due to the repetition of many structural background classes in new data. Traditional replay-based methods store the original pixels of these background classes from old data, resulting in low memory efficiency. To enhance memory efficiency, we propose Spatial–Adaptive replay for Foreground objects (SAF), a method that stores only foreground-class pixels and their

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15

Feng, Jiangfan, Xuejun Fu, Yao Zhou, Yuling Zhu, and Xiaobo Luo. "Image-Text Joint Learning for Social Images with Spatial Relation Model." Complexity 2020 (March 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/1543947.

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The rapid developments in sensor technology and mobile devices bring a flourish of social images, and large-scale social images have attracted increasing attention to researchers. Existing approaches generally rely on recognizing object instances individually with geo-tags, visual patterns, etc. However, the social image represents a web of interconnected relations; these relations between entities carry semantic meaning and help a viewer differentiate between instances of a substance. This article forms the perspective of the spatial relationship to exploring the joint learning of social imag

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16

Cheng, Shuli, Liejun Wang, and Anyu Du. "Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval." Entropy 22, no. 11 (2020): 1266. http://dx.doi.org/10.3390/e22111266.

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Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contribution

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17

Wang, Meng, Huilong Pi, Ruihui Li, Yunchuan Qin, Zhuo Tang, and Kenli Li. "VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 7808–16. https://doi.org/10.1609/aaai.v39i8.32841.

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Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the model susceptible to geometric ambiguity caused by occlusion and perspective distortion. Existing methods often lack explicit semantic modeling between objects, limiting their perception of 3D semantic context. To address these challenges, we propose a novel method VLScene: Vision-Language Guidance Distillation for Camera-based 3D Semantic Scene Completion. The key insight is to use the vision-language model to introduce

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18

Zhao, Wenyu, Min Xia, Liguo Weng, et al. "SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images." Remote Sensing 16, no. 17 (2024): 3161. http://dx.doi.org/10.3390/rs16173161.

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Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face challenges in simultaneously attending to both contextual and spatial information when performing semantic segmentation on remote sensing imagery. This often leads to misclassifications and omissions. Therefore, this paper proposes a Dual-Branch Net

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19

Mardiah, Zaqiatul, Afdol Tharik Wastono та Abdul Muta’ali. "A COGNITIVE PERSPECTIVE ON THE ARABIC SPATIAL NOUN/ فَوْقَ /FAWQA/ APPLYING THE PRINCIPLE POLYSEMY MODEL". Paradigma: Jurnal Kajian Budaya 9, № 2 (2019): 154. http://dx.doi.org/10.17510/paradigma.v9i2.286.

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<p class="TeksAbstrak">The present paper provides a cognitive linguistics (CL) framework for analyzing the semantic structure of Arabic spatial noun <em>fawqa</em> based on <em>Principled Polysemy Model </em>(PPM) of Tyler and Evans (2003). PPM approach can broaden the narrow view of classical cognitive linguists regarding the semantic variation in the concept of physical-geometry of a preposition. As a polysemous lexeme,<em> fawqa</em> used by Arabian native to express a broad range of meanings, not only spatial relation but also non-spatial relation.

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20

Wang, Pengtao, Lihong Li, Feiyang Pan, and Lin Wang. "Lightweight Bilateral Network for Real-Time Semantic Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 4 (2023): 673–82. http://dx.doi.org/10.20965/jaciii.2023.p0673.

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Herein, a dual-branch semantic segmentation model based on depth-separable convolution and attention mechanism is proposed for the real-time and accuracy requirement of semantic segmentation. The proposed approach overcomes the problems of poor segmentation effect and over-simplification of feature fusion arising from the constant downsample operations in semantic segmentation. The network is divided into spatial detail and semantic information paths. The spatial detail path utilizes a smaller downsample multiplier to maintain resolution and efficiently extract spatial information. The semanti

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21

Yang, Xin, and Xi’ang Ma. "A Spatial Semantic Feature Extraction Method for Urban Functional Zones Based on POIs." ISPRS International Journal of Geo-Information 13, no. 7 (2024): 220. http://dx.doi.org/10.3390/ijgi13070220.

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Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomple

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22

Navarro i Ferrando, Ignasi. "Embodied semantic parameters for the lexical representation of spatial relational categories." Cognitive Linguistic Studies 11, no. 1 (2024): 203–33. http://dx.doi.org/10.1075/cogls.00118.nav.

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Abstract This paper proposes an explanatory model for the lexical representation of the native speakers’ lexical knowledge of English prepositions. Lexical knowledge of prepositions as relational predicates includes argument structure (trajector-landmark) as in Cognitive Grammar, situation types (position vs state) as in Functional Grammar, lexical hierarchies (spatial subdomains) based on semantic primitives, as in Natural Semantic Metalanguage, and embodied perceptual parameters configured in four dimensions, namely, geometry, topology, force-dynamics and function (from Cognitive Linguistics

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23

Sun, Xia, Li, Shen, and Liu. "A Semantic Expansion Model for VGI Retrieval." ISPRS International Journal of Geo-Information 8, no. 12 (2019): 589. http://dx.doi.org/10.3390/ijgi8120589.

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OpenStreetMap (OSM) is a representative volunteered geographic information (VGI) project. However, there have been difficulties in retrieving spatial information from OSM. Ontology is an effective knowledge organization and representation method that is often used to enrich the search capabilities of search systems. This paper constructed an OSM ontology model with semantic property items. A query expansion method is also proposed based on the similarity of properties of the ontology model. Moreover, a relevant experiment is conducted using OSM data related to China. The experimental results d

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24

Ou, Weihua, and Wenjun Xiao. "Structured sparsity model with spatial similarity regularisation for semantic feature selection." International Journal of Advanced Media and Communication 7, no. 2 (2017): 138. http://dx.doi.org/10.1504/ijamc.2017.085941.

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25

Xiao, Wenjun, and Weihua Ou. "Structured sparsity model with spatial similarity regularisation for semantic feature selection." International Journal of Advanced Media and Communication 7, no. 2 (2017): 138. http://dx.doi.org/10.1504/ijamc.2017.10006892.

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26

Takahashi, Kazuko. "Reasoning about Propagation of Properties over Regions." JUCS - Journal of Universal Computer Science 9, no. (9) (2003): 1030–45. https://doi.org/10.3217/jucs-009-09-1030.

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We discuss how a property of some region is propagated to other regions. We propose a system called SRCC that enables the integration of spatial and semantic data. SRCC can represent the relative positions of regions, properties that hold in some regions, semantic relation between regions, and so on. We define the model and describe an algorithm that checks for the existence of a model for a given set of formulas based on this model. We prove the soundness and completeness of the algorithm and apply it to an example that inspects the causality of contamination in 2D space.

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27

Poux, F., R. Neuville, P. Hallot, and R. Billen. "MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 107–15. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-107-2017.

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This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clo

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28

Liu, Dongxu, Qingqing Li, Meihui Li, and Jianlin Zhang. "A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification." Remote Sensing 15, no. 18 (2023): 4642. http://dx.doi.org/10.3390/rs15184642.

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Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic

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29

Song, Rongxin, Yuanqiao Wen, Wei Tao, Qi Zhang, Eleonora Papadimitriou, and Pieter van Gelder. "Semantic Modeling of Ship Behavior in Cognitive Space." Journal of Marine Science and Engineering 10, no. 10 (2022): 1347. http://dx.doi.org/10.3390/jmse10101347.

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Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior

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30

Voelker, Aaron R., Peter Blouw, Xuan Choo, Nicole Sandra-Yaffa Dumont, Terrence C. Stewart, and Chris Eliasmith. "Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers." Neural Computation 33, no. 8 (2021): 2033–67. http://dx.doi.org/10.1162/neco_a_01410.

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While neural networks are highly effective at learning task-relevant representations from data, they typically do not learn representations with the kind of symbolic structure that is hypothesized to support high-level cognitive processes, nor do they naturally model such structures within problem domains that are continuous in space and time. To fill these gaps, this work exploits a method for defining vector representations that bind discrete (symbol-like) entities to points in continuous topological spaces in order to simulate and predict the behavior of a range of dynamical systems. These

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31

Tan, Yongbin, Hong Wang, Rongfeng Cai, Lingling Gao, Zhonghai Yu, and Xin Li. "Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories." ISPRS International Journal of Geo-Information 14, no. 6 (2025): 233. https://doi.org/10.3390/ijgi14060233.

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Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. In this paper, a method for the semantic representation of the geospatial entity categories (denoted as feature embedding) is presented, taking advantage of the characteristic that regions with similar distributions of geospatial entity categories

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32

Cui, Wei, Xin He, Meng Yao, et al. "Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation." Remote Sensing 13, no. 7 (2021): 1312. http://dx.doi.org/10.3390/rs13071312.

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Анотація:

The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) semantic segmentation to a certain extent. Comparing with the pixel-based methods, the graph neural networks (GNNs) usually use objects as input nodes, so they not only have relatively small computational complexity, but also can carry richer semantic information. However, the traditional GNNs are more rely on the context information of the individual

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33

Li, Muqing, Ziyi Zhu, Ruilin Xu, Yinqiu Feng, and Lingxi Xiao. "Research on Image Classification And Semantic Segmentation Model Based on Convolutional Neural Network." Journal of Computing and Electronic Information Management 12, no. 3 (2024): 94–100. http://dx.doi.org/10.54097/qg7hakzu.

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This paper investigates convolutional neural network (CNN)-based approaches for image classification and semantic segmentation, with a focus on addressing spatial detail loss and multi-scale feature integration issues prevalent in semantic segmentation. The introduced EDNET model tackles these challenges through the incorporation of spatial information branches and the design of efficient feature fusion mechanisms. It further enhances performance via the use of global pooling and boundary refinement modules. Evaluations on the PASCAL VOC 2012 dataset reveal an 11.67% increase in mean intersect

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34

Deng, Kai, Siyuan Wei, Shiyan Pang, Huiwei Jiang, and Bo Su. "Synthesizing Remote Sensing Images from Land Cover Annotations via Graph Prior Masked Diffusion." Remote Sensing 17, no. 13 (2025): 2254. https://doi.org/10.3390/rs17132254.

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Анотація:

Semantic image synthesis (SIS) in remote sensing aims to generate high-fidelity satellite imagery from land use/land cover (LULC) labels, supporting applications such as map updating, data augmentation, and environmental monitoring. However, the existing methods typically focus on pixel-level semantic-to-image translation, neglecting the spatial and semantic relationships among land cover objects, which hinders accurate scene structure modeling. To address this challenge, we propose GMDiT, an enhanced conditional diffusion model that extends the masked DiT architecture with graph-prior modelin

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35

Zhao, Yuehua, Jiguang Zhang, Jie Ma, and Shibiao Xu. "Large-Scale Semantic Scene Understanding with Cross-Correction Representation." Remote Sensing 14, no. 23 (2022): 6022. http://dx.doi.org/10.3390/rs14236022.

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Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. However, most of them only exploit local spatial geometric or semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial–Semantic Incorporation Network (SSI-Net) for real-time large-scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Ne

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36

Muñoz, José Manuel. "Mental causation and neuroscience: The semantic pruning model." THEORIA. An International Journal for Theory, History and Foundations of Science 33, no. 3 (2018): 379. http://dx.doi.org/10.1387/theoria.17312.

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Анотація:

In this paper I propose a hypothetical model of mental causation that I call semantic pruning and which could be defined as the causal influence of contents and meanings on the spatial configuration of the network of synapses of an individual. I will be guided by two central principles: 1) the causal influence of the mental occurs by virtue of external semantic constraints and consists in the selective activation of certain physical powers, 2) when the selective activation is continual, it triggers a process of synaptic pruning in the neural and neuromuscular network.

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37

Liu, Yu, Zhen Ren, Kaifeng Wang, Qin Tian, Xi Kuai, and Sheng Li. "A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods." Symmetry 17, no. 7 (2025): 1064. https://doi.org/10.3390/sym17071064.

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Анотація:

Geographic information systems (GISs) have shown considerable promise in enhancing textual semantic analysis. Current textual semantic analysis methods face significant limitations in accurately delineating semantic boundaries, identifying semantic clustering patterns, and representing knowledge evolution. To address these issues, this study proposes a framework that innovatively introduces GIS methods into textual semantic analysis and aligns them with the conceptual foundation of geographical metaphor theory. Specifically, word embedding models are employed to endow semantic primitives with

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38

Zhang, Kaihua, Jin Chen, Bo Liu, and Qingshan Liu. "Deep Object Co-Segmentation via Spatial-Semantic Network Modulation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12813–20. http://dx.doi.org/10.1609/aaai.v34i07.6977.

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Object co-segmentation is to segment the shared objects in multiple relevant images, which has numerous applications in computer vision. This paper presents a spatial and semantic modulated deep network framework for object co-segmentation. A backbone network is adopted to extract multi-resolution image features. With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image. The spatial modulator captures the correlations of image feature descriptors via unsupervised learning. The learned mask can roughly localize the shared fo

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39

Paul, Debjyoti, Feifei Li, and Jeff M. Phillips. "Semantic embedding for regions of interest." VLDB Journal 30, no. 3 (2021): 311–31. http://dx.doi.org/10.1007/s00778-020-00647-0.

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Анотація:

AbstractThe available spatial data are rapidly growing and also diversifying. One may obtain in large quantities information such as annotated point/place of interest (POIs), check-in comments on those POIs, geo-tagged microblog comments, and demarked regions of interest (ROI). All sources interplay with each other, and together build a more complete picture of the spatial and social dynamics at play in a region. However, building a single fused representation of these data entries has been mainly rudimentary, such as allowing spatial joins. In this paper, we extend the concept of semantic emb

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40

Wu, Hao, Canhai Li, and Yongchang Li. "Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 267–74. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-267-2024.

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Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification.

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41

Jia, Jian, Naiyu Gao, Fei He, Xiaotang Chen, and Kaiqi Huang. "Learning Disentangled Attribute Representations for Robust Pedestrian Attribute Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 1069–77. http://dx.doi.org/10.1609/aaai.v36i1.19991.

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Although various methods have been proposed for pedestrian attribute recognition, most studies follow the same feature learning mechanism, \ie, learning a shared pedestrian image feature to classify multiple attributes. However, this mechanism leads to low-confidence predictions and non-robustness of the model in the inference stage. In this paper, we investigate why this is the case. We mathematically discover that the central cause is that the optimal shared feature cannot maintain high similarities with multiple classifiers simultaneously in the context of minimizing classification loss. In

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42

Wang, Ziquan, Yongsheng Zhang, Zhenchao Zhang, et al. "Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation." Remote Sensing 16, no. 5 (2024): 758. http://dx.doi.org/10.3390/rs16050758.

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Анотація:

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsuper

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43

Ben Mahfoudh, Houssem, Ashley Caselli, and Giovanna Di Marzo Serugendo. "Learning-Based Coordination Model for On-the-Fly Self-Composing Services Using Semantic Matching." Journal of Sensor and Actuator Networks 10, no. 1 (2021): 5. http://dx.doi.org/10.3390/jsan10010005.

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Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage

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44

Zhang, Jingsi, Xiaosheng Yu, Xiaoliang Lei, and Chengdong Wu. "A Multi-Feature Fusion Model Based on Denoising Convolutional Neural Network and Attention Mechanism for Image Classification." International Journal of Swarm Intelligence Research 14, no. 2 (2023): 1–15. http://dx.doi.org/10.4018/ijsir.324074.

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Spatial location features extracted by denoising convolutional neural network. At this time, an attention mechanism is introduced into denoising convolutional neural network. The dual attention model of local area is presented from two dimensions of channel and space—channel attention mechanism weights channel and spatial attention mechanism weights location. A variety of machine learning methods are used to classify and train different features. Multi-semantic features and heterogeneous features are fused by adaptive weighted fusion algorithm. Finally, the data sets Cifar-10, STL-10, Cifar-10

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45

Zhao, Xin, Liufang Sang, Guiguang Ding, Jungong Han, Na Di, and Chenggang Yan. "Recurrent Attention Model for Pedestrian Attribute Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9275–82. http://dx.doi.org/10.1609/aaai.v33i01.33019275.

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Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature

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46

Jiang, Peilong, and Xiao Ke. "Lightweight spatial pyramid pooling network for real-time semantic segmentation." Journal of Physics: Conference Series 2234, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2234/1/012012.

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Abstract In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic

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47

Colin, Clement, Diego Vinasco-Alvarez, John Samuel, Sylvie Servigne, Christophe Bortolaso, and Gilles Gesquière. "A model-driven methodology for integrating heterogeneous 3D geospatial urban entities." AGILE: GIScience Series 5 (May 30, 2024): 1–11. http://dx.doi.org/10.5194/agile-giss-5-3-2024.

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Abstract. Data on geospatial entities is increasingly available from various fields, such as GIS, CIM (City Information Modeling), and BIM (Building Information Modeling). They are described using different data models, such as IFC and CityGML, and are composed of both semantic and geometric data. Integrating this heterogeneous data to create a unified view of geospatial entities requires the use of technologies and methods adapted to each type of data. In this work, we propose a data integration methodology that leverages geospatial and semantic web technologies to provide data views suited t

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48

Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.

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Анотація:

As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic

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49

Li, Bosen, Rui Li, Junhao Wang, and Aihong Song. "Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews." Future Internet 17, no. 7 (2025): 276. https://doi.org/10.3390/fi17070276.

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Current tourism route recommendation systems often overemphasize popular destinations, thereby overlooking geographical accessibility between attractions and the experiential coherence of the journey. Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are define

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50

Zhu, Yuchang, and Nanfeng Xiao. "Simple Scalable Multimodal Semantic Segmentation Model." Sensors 24, no. 2 (2024): 699. http://dx.doi.org/10.3390/s24020699.

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Анотація:

Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a more effective strategy involves leveraging multiple modalities, which is because different sensors of the autonomous driving system bring diverse information, and the complementary features among different modalities enhance the robustness of the semantic segmentation

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