Search Results (165)

Small object detection in UAV-based remote sensing imagery is crucial for applications such as traffic monitoring, emergency response, and urban management. However, aerial images often suffer from low object resolution, complex backgrounds, and varying lighting conditions, leading to missed or false detections. To address these challenges, we propose DCS-YOLOv8, an enhanced object detection framework tailored for small target detection in UAV scenarios. The proposed model integrates a Dynamic Convolution Attention Mixture (DCAM) module to improve global feature representation and combines it with the C2f module to form the C2f-DCAM block. The C2f-DCAM block, together with a lightweight SCDown module for efficient downsampling, constitutes the backbone DCS-Net. In addition, a dedicated P2 detection layer is introduced to better capture high-resolution spatial features of small objects. To further enhance detection accuracy and robustness, we replace the conventional CIoU loss with a novel Scale-based Dynamic Balanced IoU (SDBIoU) loss, which dynamically adjusts loss weights based on object scale. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed DCS-YOLOv8 significantly improves small object detection performance while maintaining efficiency. Compared to the baseline YOLOv8s, our model increases precision from 51.8% to 54.2%, recall from 39.4% to 42.1%, $mA{P}_{0.5}$ from 40.6% to 44.5%, and $mA{P}_{0.5:0.95}$ from 24.3% to 26.9%, while reducing parameters from 11.1 M to 9.9 M. Moreover, real-time inference on RK3588 embedded hardware validates the model’s suitability for onboard UAV deployment in remote sensing applications. Full article

Urban planning in arid climates must overcome numerous nonclimatic constraints that often result in outdoor thermal discomfort. This is particularly evident in Béchar, a city in southern Algeria known for its long, intense summers with temperatures frequently exceeding 45 °C. This study investigates the influence of urban morphology on thermal comfort and explores architectural and digital solutions to enhance energy performance in buildings. This research focuses on Béchar’s city center, where various urban configurations were analyzed using a multidisciplinary approach that combines typomorphological and climatic analysis with numerical simulations (ENVI-met 3.0 and TRNSYS 16). The results show that shaded zones near buildings have lower thermal loads (under +20 W/m²), while open areas may reach +100 W/m². The thermal comfort rate varies between 22% and 60%, depending on wall materials and occupancy patterns. High thermal inertia materials, such as stone and compressed stabilized earth blocks (CSEBs), reduce hot discomfort hours to under 1700 h/year but may increase cold discomfort. Combining these materials with targeted insulation improves thermal balance. Key recommendations include compact urban forms, vegetation, shading devices, and high-performance envelopes. Early integration of these strategies can significantly enhance thermal comfort and reduce energy demand in Saharan cities. Full article

This study presents a multi-objective optimization framework for enhancing environmental performance in high-density residential complexes, addressing the critical balance between sunlight access, visual openness, and ground-level privacy. Applied to Helio City Phase 3 in Seoul—a challenging case with 2026 units surrounded by adjacent blocks—the research developed a sequential three-stage optimization strategy using computational design tools. The methodology employs Ladybug simulations for solar analysis, Galapagos genetic algorithms for view optimization, and parametric modeling for privacy assessment. Through grid-based layout reconfiguration, tower form modulation, and strategic conversion of vulnerable ground-floor units to public spaces, the optimized design achieved 100% sunlight standard compliance (improving from 64.31%), increased average visual openness to 66.31% (from 39.48%), and eliminated all privacy conflicts while adding 30 residential units. These results demonstrate that computational optimization can significantly surpass conventional planning approaches in addressing complex environmental trade-offs. The framework provides a replicable methodology for performance-driven residential design, offering quantitative tools for achieving regulatory compliance while enhancing residents’ experiential comfort in dense urban environments. Full article

To improve semantic segmentation performance for complex urban remote sensing images with multi-scale object distribution, class similarity, and small object omission, this paper proposes MFPI-Net, an encoder–decoder-based semantic segmentation network. It includes four core modules: a Swin Transformer backbone encoder, a diverse dilation rates attention shuffle decoder (DDRASD), a multi-scale convolutional feature enhancement module (MCFEM), and a cross-path residual fusion module (CPRFM). The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. The DDRASD’s diverse dilation rates attention (DDRA) block combines convolutions with diverse dilation rates and channel-coordinate attention to enhance multi-scale contextual awareness, while Shuffle Block improves resolution via pixel rearrangement and avoids checkerboard artifacts. The MCFEM enhances local feature modeling through parallel multi-kernel convolutions, forming a complementary relationship with the Swin Transformer’s global perception capability. The CPRFM employs multi-branch convolutions and a residual multiplication–addition fusion mechanism to enhance interactions among multi-source features, thereby improving the recognition of small objects and similar categories. Experiments on the ISPRS Vaihingen and Potsdam datasets show that MFPI-Net outperforms mainstream methods, achieving 82.57% and 88.49% mIoU, validating its superior segmentation performance in urban remote sensing. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Form-based design codes have emerged as a planning tool aimed at shaping the physical form of neighborhoods to reinforce local character and enhance sense of place (SoP). However, their effectiveness in delivering these outcomes remains underexplored. This study investigates the extent to which Buffalo’s Green Code—a form-based zoning ordinance—enhances SoP in residential environments, using Elmwood Village as a case study. A multi-scalar analytical framework assesses SoP at the building, street, and neighborhood levels. Empirical data were gathered through an online survey, while the neighborhood was systematically mapped into street segment blocks categorized by Green Code zoning. The study consolidates six Green Code classifications into three overarching categories: mixed-use, residential, and single-family. SoP satisfaction is analyzed through a two-step process: first, comparative assessments are conducted across the three zoning groups; second, k-means clustering is applied to spatially map satisfaction levels and evaluate SoP at different scales. Findings indicate that mixed-use areas are most closely associated with place identity, while residential and single-family zones (as defined by the Buffalo Green Code) yield higher satisfaction overall—though satisfaction varies significantly across spatial scales. These results suggest that while form-based codes can strengthen SoP, their impact is uneven, and more scale-sensitive zoning strategies may be needed to optimize their effectiveness in diverse urban contexts. This research overall offers an empirically grounded, multi-scalar assessment of zoning impacts on lived experience—addressing a notable gap in the planning literature regarding how form-based codes perform in established, rather than newly developed, neighborhoods. Full article

An important historical and cultural region in southern China, the Xiangjiang River Basin, has formed a unique spatial pattern and regional cultural characteristics in its long-term development. In recent years, the acceleration of urbanization has led to the historical texture and cultural elements of Changsha’s suburban blocks facing deconstruction pressure. How to identify and protect their cultural value at the spatial structure level has become an urgent issue. Taking three typical traditional township blocks in the suburbs of Changsha as the research object, this paper constructs a trinity research framework of “spatial gene identification–diversity analysis–strategy optimization.” It systematically discusses the makeup of the types, quantity, distribution, relative importance ranking, and diversity characteristics of their spatial genes. The results show that (1) the distribution and quantity of spatial genes are affected by multiple driving forces such as historical function, geographic environment, and settlement evolution mechanisms, and that architectural spatial genes have significant advantages in type richness and importance indicators; (2) spatial gene diversity shows the structural characteristics of “enriched artificial space and sparse natural space,” and different blocks show clear differences in node space and boundary space; (3) spatial genetic diversity not only reflects the complexity of the spatial evolution of a block but is also directly related to its cultural inheritance and the feasibility of renewal strategies. Based on this, this paper proposes strategies such as building a spatial gene database, improving the diversity evaluation system, and implementing differentiated protection mechanisms. These strategies provide theoretical support and methods for the protection and sustainable development of cultural heritage in traditional blocks. Full article

Urban thermal environment challenges in China have made outdoor thermal comfort a key factor in evaluating spatial quality and livability. Building layout not only affects internal performance but also shapes the microclimate of surrounding outdoor spaces. The climatic characteristics of temperate monsoon climate regions significantly impact residents’ outdoor activities. Most existing studies focus solely on either the external thermal environment or the buildings themselves in isolation. This study focuses on Beijing, a representative city in the temperate monsoon climate zone, and explores block-scale spatial optimization using computational typology. The objective is to balance architectural performance with outdoor thermal comfort in both winter and summer. Optimization targets include the Universal Thermal Climate Index (UTCI), winter sunshine duration, and summer solar radiation. Results show winter UTCI can be optimized to −6.13 °C to −1.18 °C and summer UTCI to 28.19 °C to 29.17 °C, with greater optimization potential in winter (23.5% higher). Synergistic relationships are observed between winter comfort and sunshine duration (coefficient: 0.777) and between summer comfort and solar radiation (coefficient: 0.947). However, trade-offs exist between seasonal comfort indicators, with strong conflicts between winter and summer objectives. Two distinct form types—“low-south-high-north enclosed” for winter and “high-rise point-type low-density” for summer—are identified as effective for seasonal adaptation. The study proposes an integrated method combining data-driven generation, multi-objective optimization, and clustering-based decision-making. This approach moves beyond traditional empirical design, offering a quantitative and adaptable strategy for climate-responsive urban block planning and supporting low-carbon urban transformation. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

Block-level urban land use classification (BLULUC), like residential and commercial classification, is highly useful for urban planners. It can be achieved in the form of high-frequency full coverage without biases based on the data of high-spatial-resolution remote sensing images (HSRRSIs), which social sensing data like POI data or mobile phone data cannot provide. However, at present, the extraction of quantitative features from HSRRSIs for BLULUC primarily relies on computer vision or deep learning methods based on image signal characteristics rather than land cover patterns, like vegetation, water, or buildings, thus disconnecting existing knowledge between the landscape patterns and their functions as well as greatly hindering BLULUC by HSRRSIs. Well-known landscape metrics could play an important connecting role, but these also encounter the scale selection issue; i.e., the optimal spatial unit size is an image pixel or a segmented image object. Here, we use the task of BLULUC with 2 m satellite images in Beijing as a case study. The results show the following: (1) pixel-based classification can achieve higher accuracy than segmented object-based classification, with an average of 3% in overall aspects, while some land use types could reach 10%, such as commercial land. (2) At the pixel scale, if the quantity metrics at the class level, such as the number of patches, and the proportion metrics at the landscape level, such as vegetation proportion, are removed, the accuracy can be greatly reduced. Moreover, removing landscape-level metrics can lead to a more significant reduction in accuracy than removing class-level metrics. This indicates that in order to achieve a higher accuracy in BLULUC from HSRRSIs, landscape-level land cover metrics, including patch numbers and proportions at the pixel scale, can be used instead of object-scale metrics. Full article

Urban densification associated with rapid urbanization has weakened horizontal ventilation in cities. Previous studies point out that building-height variability can enhance vertical ventilation, while most of them rely on idealized models that overlook the complexity of real urban environments. This study analyzes 20 actual urban blocks using CFD simulations, considering average building height, building density, and height standard deviation. The results show that areas with low-rise, uniform buildings exhibit superior pollutant dispersion, while mid- and high-rise zones experience complex turbulence and pollutant accumulation. Ventilation performance peaks when the height standard deviation ranges between 35 and 40. These findings underscore that optimizing urban form for vertical ventilation requires a combined strategy of density control and height variation. Realistic building group models more accurately capture airflow dynamics and provide valuable insights for the design of effective vertical ventilation corridors and the enhancement of urban pollutant dispersion. Full article

Historical blocks are a vital component of urban cultural heritage, serving as a link for regional cultural inheritance and a carrier for showcasing urban charm. Enhancing the quality of cultural tourism experiences in these areas can activate the endogenous momentum of cultural tourism industries and foster a virtuous cycle of cultural heritage conservation and utilization. Currently, research on the relationship between historical block spaces and cultural tourism experiences remains deep, and related theoretical gaps also constrain sustainable revitalization practices. Therefore, in this study, 20 representative historic districts with distinct regional cultural characteristics and well-developed cultural tourism in the Huizhou area were selected as research objects. By integrating multi-source data such as geographic information and Dianping reviews and applying the Partial Least Squares Regression (PLSR) statistical method, this study measures the correlation between the spatial morphology of Huizhou historic districts and cultural tourism experience indicators, identifying spatial influencing factors affecting cultural tourism experiences. The results show a significant correlation between the spatial form characteristics of historic districts and the quality of tourists’ cultural tourism experiences. Specifically, the regression coefficients of architectural space, transportation space, landscape space, and facility space in relation to the quality of cultural tourism experiences are significant at the p < 0.01 level. This paper innovatively conducts research from the perspective of urban design, employing a combined quantitative and qualitative analytical approach. The study fills existing gaps in quantitative analysis and empirical research on the spatial forms of historic districts and cultural tourism experiences and breaks through the limitations of qualitative research on traditional cultural tourism. It provides practical references for the organic protection of historical district buildings in the context of sustainable urban renewal. Full article

A road network is an important spatial carrier for the efficient and reliable operation of urban services and material flows. In recent years, the “high road density, small block size” trend has become a major focus in urban planning practices. However, whether high-density road networks are highly resilient lacks quantitative evidence. This study presents a multi-scale analytical framework for measuring road network resilience from a topological perspective. We abstract 186 ideal orthogonal grid density models from an actual urban road network, quantifying resilience under two disturbance scenarios: random failures and intentional attacks. The results indicate that road network density indeed has a significant impact on resilience, with both scenarios showing a trend where higher densities correlate with greater resilience. However, the increase in resilience value under the intentional attack scenario is significantly higher than that under the random failure scenario. The findings indicate that network density plays a decisive role in determining resilience levels when critical edges fail. This is attributed to the greater presence of loops in denser networks, which helps maintain connectivity even under intentional disruption. In the random failure scenario, network resilience depends on the combined effects of the node degree and density. This study offers quantitative insights into the design of resilient urban forms in the face of disruptive events, establishing reference benchmarks for road network spacing at both meso- and micro-scales. The results provide practical guidance for resilient city planning in both newly developed and existing urban areas, supporting informed decision-making in urban morphology and disaster risk management. Full article

With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how urban block morphology impacts building photovoltaic (PV) efficiency and energy consumption has become crucial for sustainable urban development and climate change mitigation. Current research primarily focuses on individual building optimization, while block-scale coupling relationships between PV utilization and energy consumption remain underexplored. This study developed an integrated prediction and optimization tool using deep learning and physical simulation to assess how urban block design parameters (building morphology, orientation, and layout) affect PV efficiency and energy performance. Through a methodology combining block modeling, PV potential assessment, and energy consumption simulation, the research quantified relationships between design parameters, PV utilization, and energy consumption. Results demonstrate that appropriate building forms and layouts reduce shadow obstruction, enhance PV system capability, and simultaneously improve PV efficiency while reducing energy consumption. The tool provides improved prediction accuracy, enabling urban planners to scientifically design block layouts that maximize PV generation and minimize energy use. Extensive experimental validation demonstrates that the integrated model and analytical methods proposed in this study will help urban planners break through the limitations of individual building research, making PV-energy consumption optimization analysis at the block scale possible, and providing scientific basis for achieving low-carbon transformation and sustainable energy development in the building sector. Full article

In recent years, block-style commercial complexes have become a prominent form of commercial architecture in many Chinese cities. The thermal comfort of their outdoor spaces significantly influences people’s activities and the overall quality of these areas. This study explores the relationship between the morphological elements of outdoor spaces in such complexes and thermal comfort, using quantifiable methods to identify key control indicators. Enhancing thermal comfort is crucial for improving spatial quality, increasing dwell time, and boosting commercial vibrancy. Focusing on the hot summer and cold winter climate of Shanghai, this research analyzed two representative block-style commercial complexes. It employed computer simulations and sensory comfort surveys to demonstrate that block morphology significantly impacts outdoor thermal comfort. Three control variables—street density, number of street intersections, and street orientation—were selected to study their effects. Spatial prototypes were categorized, and their thermal comfort performance was evaluated using numerical simulations. Based on these findings, spatial morphology was iteratively optimized. This study concluded by proposing evaluation indicators for spatial morphology control elements to enhance outdoor thermal comfort. It also provided external spatial layout strategies for block-style commercial complexes in similar climates, offering architects and urban designers decision-making criteria to improve thermal comfort in outdoor spaces. This research contributes to creating more comfortable and vibrant urban environments. Full article