Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace DOI Creative Commons
Shiyuan Cheng, Jianchen Zhang, Guangxia Wang

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(6), P. 178 - 178

Published: May 29, 2024

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support revealing deepening maps. In this study, CiteSpace was used to examine confluence neural networks over past decade (2013–2023), thus prevailing research trends cutting-edge investigations field machine learning its application mapping. addition, analysis included systematic categorization knowledge clusters arising from fusion networks, which followed discernment pivotal Crucially, study diligently identified critical studies (milestones) that made significant contributions development these elucidated clusters. Timeline track studies’ origins, evolution, current status. Finally, we constructed collaborative among contributing authors, journals, institutions, countries. This mapping aids identifying visualizing primary factors evolution encompassing facilitating interdisciplinary multidisciplinary investigations.

Language: Английский

Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image DOI
Yan Zhang, Pengyuan Liu, Filip Biljecki

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 198, P. 153 - 168

Published: March 16, 2023

Language: Английский

Citations

45
Deep learning for urban land use category classification: A review and experimental assessment DOI Creative Commons
Ziming Li, Бин Чэн, Shengbiao Wu

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114290 - 114290

Published: July 14, 2024

Mapping the distribution, pattern, and composition of urban land use categories plays a valuable role in understanding environmental dynamics facilitating sustainable development. Decades effort mapping have accumulated series approaches products. New trends characterized by open big data advanced artificial intelligence, especially deep learning, offer unprecedented opportunities for patterns from regional to global scales. Combined with large amounts geospatial data, learning has potential promote higher levels scale, accuracy, efficiency, automation. Here, we comprehensively review advances based research practices aspects sources, classification units, approaches. More specifically, delving into different settings on learning-based mapping, design eight experiments Shenzhen, China investigate their impacts performance terms sample, model. For each investigated setting, provide quantitative evaluations discussed inform more convincing comparisons. Based historical retrospection experimental evaluation, identify prevailing limitations challenges suggest prospective directions that could further facilitate exploitation techniques using remote sensing other spatial across various

Language: Английский

Citations

31
A graph-based neural network approach to integrate multi-source data for urban building function classification DOI
Bo Kong, Tinghua Ai, Xinyan Zou

et al.

Computers Environment and Urban Systems, Journal Year: 2024, Volume and Issue: 110, P. 102094 - 102094

Published: March 15, 2024

Language: Английский

Citations

21
A graph-based multimodal data fusion framework for identifying urban functional zone DOI Creative Commons
Tao Yuan,

Wanzeng Liu,

Jun Chen

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104353 - 104353

Published: Jan. 5, 2025

Language: Английский

Citations

2
Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping DOI Creative Commons
Yongze Song, Margaret Kalácska, Mateo Gašparović

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 120, P. 103300 - 103300

Published: April 28, 2023

Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) Earth observation to a new stage. GeoAI has enhanced traditional analysis mapping, altering the methods for understanding managing complex human–natural systems. However, there are still challenges various aspects of applications related natural, built, social environments, integrating unique features into models. Meanwhile, data critical components geocomputation studies, as they can effectively reveal patterns, factors, relationships, decision-making processes. This editorial provides comprehensive overview classifying them four categories: (i) buildings infrastructure, (ii) land use analysis, (iii) natural environment hazards, (iv) issues human activities. In addition, summarizes case studies seven categories, including in-situ data, datasets, crowdsourced (i.e., big data), remote sensing photogrammetry LiDAR, statistical data. Finally, presents opportunities future research.

Language: Английский

Citations

41
An MIU-based deep embedded clustering model for urban functional zoning from remote sensing images and VGI data DOI Creative Commons
Anqi Lin, Bo Huang, Hao Wu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103689 - 103689

Published: Feb. 21, 2024

Urban functional zoning offers valuable insights into urban morphology and sustainable development. However, the conventional fixed spatial units, such as blocks grids, cannot easily capture morphological characteristics inherent in union separation during evolution. In this paper, by taking advantage of remote sensing images geospatial big data, we propose a minimum identification unit (MIU)-based model. This approach integrates deep embedded clustering buildings to generate segmentation, then identifies function generating semantic vectors with Word2Vec The effectiveness proposed method was tested city Wuhan China. results highlight that MIUs provide more flexible suitable for segmenting zones compared traditional street blocks. is feasible way deal redundancy volunteered geographic information (VGI) data when identifying function, quality issue only has significant impact on minor types. Moreover, building can effectively reveal fine-scale structure, especially administration, manufacturing, residential demonstrates potential our enhancing understanding supporting

Language: Английский

Citations

15
A multimodal fusion framework for urban scene understanding and functional identification using geospatial data DOI Creative Commons
Chen Su, Xinli Hu, Qingyan Meng

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 127, P. 103696 - 103696

Published: Feb. 2, 2024

Urban scene understanding and functional identification are essential for accurately characterizing the spatial structure optimizing city layouts during rapid urbanization. Multimodal data is important recognizing distribution patterns of urban functions revealing internal details. Previous studies have focused primarily on remote sensing imagery points interest (POIs) data, overlooking role building characteristics in determining scenes. These also limited terms mining fusing multimodal features. To address these challenges, this study proposes a fusion framework that integrates imagery, POIs, footprints mapping. The employs dual-branch model extracts visual semantic features from socioeconomic auxiliary such as POIs footprints. A branch attention module designed to assign weights Additionally, multiscale feature introduced extract combine through modal interaction. Experiments Beijing Chengdu validate effectiveness proposed with overall accuracy 90.04% 92.07%, kappa coefficient 0.881 0.895, respectively. This provides empirical evidence support accurate planning further promote sustainable development. source code at: https://github.com/sssuchen/MMFF.

Language: Английский

Citations

8
A review of crowdsourced geographic information for land-use and land-cover mapping: current progress and challenges DOI Creative Commons
Hao Wu, Yan Li, Anqi Lin

et al.

International Journal of Geographical Information Science, Journal Year: 2024, Volume and Issue: 38(11), P. 2183 - 2215

Published: July 14, 2024

The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution land-use and land-cover (LULC) mapping. This approach taps into collective power public to share spatial information, providing a relevant data source for producing LULC maps. Through analysis 262 papers published from 2012 2023, this work provides comprehensive overview field, including prominent researchers, key areas study, major CGI sources, mapping methods, scope research. Additionally, it evaluates pros cons various sources methods. findings reveal that while applying with labels is common way by using analysis, limited incomplete coverage other quality issues. In contrast, extracting semantic features interpretation often requires integrating multiple datasets remote sensing imagery, alongside advanced methods such as ensemble deep learning. paper also delves challenges posed in explores promising potential introducing large language models overcome these hurdles.

Language: Английский

Citations

7
A point selection method in map generalization using graph convolutional network model DOI
Tianyuan Xiao, Tinghua Ai, Huafei Yu

et al.

Cartography and Geographic Information Science, Journal Year: 2023, Volume and Issue: 51(1), P. 20 - 40

Published: March 21, 2023

For point clusters, the conflict and crowding of map symbols is an inevitable problem during transition from large to small scales. The cartographic generalization involved in this as a spatial decision-making process usually related analysis context, choice abstraction operators, judgment resulting data quality. rules summarized by traditional methods require manual setting conditions or thresholds sometimes encounter special cases that make it difficult directly match certain integrate different together. An alternative method using data-driven strategy under AI technology background simulate cartographer behaviors through typical sample training, such deep learning. integration cartography domain knowledge learning better settle decisions. This study uses combination approach introduce graph neural networks into cluster generalization. First, we construct virtual structure clusters Delaunay triangulation, secondly, extract features, contextual attributes each separately, then propose model based on TAGCN network. Finally, trained with manually generalized realize automatic results demonstrate proposed valid efficient for algorithm can maintain various characteristics both local area overall compared other methods.

Language: Английский

Citations

16
GDP spatial differentiation in the perspective of urban functional zones DOI
Xin Li, Yingbin Deng,

Baihua Liu

et al.

Cities, Journal Year: 2024, Volume and Issue: 151, P. 105126 - 105126

Published: May 23, 2024

Language: Английский

Citations

4