Comprehending the interaction between urban function and morphology at traffic analysis zones scale: The case study from Hangzhou

Geographical Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Abstract Urbanisation is transitioning from disorderly sprawl to compact intensification, accompanied by functional differentiation and morphological changes spatially. This study addresses the relationship between urban functions morphologies at block scale in Hangzhou. Leveraging geo‐big data, we adopt a points of interest (POI) weighting method map four essential functions—residential, commercial, public service, industrial—at traffic analysis zones (TAZ) scale. Additionally, estimate indices using building footprint data volume data. Our investigation reveals intriguing patterns: residential, service exhibit central concentration trend diminishing towards periphery, whereas industrial demonstrate multi‐hotspot distribution. Morphological like patch density mean while size shape index, presenting pronounced peripheral distribution trend. Significantly, nuanced associations were elucidated. Residential tend display dense small patches, commercial areas showcase larger volumes, complex shapes. Furthermore, construction intensity‐based heterogeneity unveils dynamics morphologies, particularly high‐density areas. These findings underscore importance integrating considerations into planning, offering fresh perspective for zoning planning.

Language: Английский

---

Second-order texton feature extraction and pattern recognition of building polygon cluster using CNN network

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 129, P. 103794 - 103794

Published: March 29, 2024

The cluster patterns of features in map space represent a comprehensive reflection individual feature geometric attributes and their spatial adjacency relationships. These also embody cognition results under the Gestalt principle. Describing non-linear as effective regular structures is one fundamental tasks deep learning for recognizing patterns. In this study, based on concept texture co-occurrence matrices from gray-scale images, we utilized Voronoi diagrams to construct tessellation structure building polygons. Built upon foundation first-order texton matrices, established three-dimensional polygons, considered five size, shape, orientation, density, encompassed 64 different combinations second-order neighboring directions. This matrix concretizes latent characteristics polygon clusters into sparse matrix. It then used an input vector convolutional neural network Through adjustments optimizations strategies, along with validation through practical case studies comparisons other models, have demonstrated effectiveness describing clusters.

Language: Английский

---

Revisiting spatial optimization in the era of geospatial big data and GeoAI

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 129, P. 103832 - 103832

Published: April 26, 2024

Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, computer science. provides important theoretical foundations solutions for determining optimal spatial arrangements or configurations entities, resources, goods. However, complexity problems poses critical challenges in modeling, efficiently solving. Recently, surge multi-source geospatial big data, emerging technologies such as artificial intelligence (GeoAI), advancements computing along with ever-expanding capabilities data storage have created significant opportunities effective efficient addressing issues, even though numerous still exist. Therefore, this paper aims revisit existing literature quantitatively qualitatively, well reflect on challenges, especially posed by GeoAI. Through these efforts, we seek stimulate greater engagement research practices, accelerate integration novel methods, collectively advance development field.

Language: Английский

---

A review of crowdsourced geographic information for land-use and land-cover mapping: current progress and challenges

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

---

Unveiling the impacts of climate change and human activities on land-use evolution in ecologically fragile urbanizing areas: A case study of China’s Central Plains urban agglomeration

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112936 - 112936

Published: Dec. 1, 2024

Language: Английский

---

Land-Unet: A deep learning network for precise segmentation and identification of non-structured land use types in rural areas for green urban space analysis

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103078 - 103078

Published: Feb. 1, 2025

Language: Английский

---

Optimizing land use spatial patterns to balance urban development and resource-environmental constraints: A case study of China's Central Plains Urban Agglomeration

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125173 - 125173

Published: April 1, 2025

Language: Английский

---

From 2D to 3D Urban Analysis: An Adaptive Urban Zoning Framework That Takes Building Height into Account

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 1182 - 1182

Published: April 3, 2025

The vertical heterogeneous structures formed during the evolution of urban agglomerations, driven by globalization, pose challenges to traditional two-dimensional spatial analysis methods. This study addresses heterogeneity and multiscale problem in three-dimensional space proposes an adaptive framework that takes into account building height for clustering areas. Firstly, we established a macro-, meso- micro-level system characteristics structures. Subsequently, developed parameter-adaptive model through dynamic coupling mechanism thresholds average elevations. Finally, proposed density-based method integrates with parameter adaptation distinguish features at different scales, thereby achieving regional delineation. experimental results demonstrate outperforms hierarchical algorithms terms both Silhouette Coefficient Davies–Bouldin Index, effectively resolving density variation clustering.

Language: Английский

---

S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104505 - 104505

Published: April 4, 2025

Language: Английский

---

Analyzing multiscale associations and couplings between integrated development and eco-environmental systems: A case study of the central plains urban agglomeration, China

Applied Geography, Journal Year: 2024, Volume and Issue: 171, P. 103387 - 103387

Published: Aug. 20, 2024

Language: Английский

---