Complex Urban Landscapes Research Articles

Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks

In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments.

Enhancing Culinary Policies for Halal and Non-Halal Labeling: Protecting Muslim Consumers in Global Jakarta

This study aims to analyze the dynamics of preserving and sharing halal food information for Muslim consumers in multicultural and global contexts. Access to halal food is essential in Muslim-majority regions. However, the rise of multiculturalism and global cosmopolitanism has led to acculturation in native foodways and diversification of culinary products. In Jakarta, a Muslim-majority city with rich multicultural complexity, merging culinary traditions often blur distinctions between halal and non-halal foods.Using qualitative methods, including in-depth interviews with culinary associations and enterprises in Singapore and Malaysia and an intensive literature review, this study identifies key practices and challenges in halal and non-halal labeling. Findings indicate that transparent and honest disclosure of non-halal product information is crucial for supporting enterprises in meeting halal certification standards. These measures protect Muslim consumers, enhance international consumer trust, and foster compliance with global food standards. In the post-pandemic era and Jakarta's transition to a global metropolis after the capital relocation, there is an urgent need to improve culinary literacy initiatives and standardize labeling procedures. These initiatives will strengthen Jakarta’s role as an inclusive and multicultural city, capable of addressing diverse dietary needs. This study, however, is limited in addressing broader policy and regulatory frameworks to ensure compliance across the culinary sector. Future research should explore strategies to overcome these limitations within Jakarta’s complex urban landscape. The findings emphasize the significance of clear labeling and regulatory consistency to promote local businesses, safeguard consumer trust, and support Jakarta’s evolving role in the global market.

A resilient routing strategy based on deep reinforcement learning for urban emergency communication networks

A resilient routing strategy based on deep reinforcement learning for urban emergency communication networks

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico

Abstract. This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

Navigating Urban Skies: Obstacle Avoidance Strategies for Quadrotor MAVs

Abstract Micro Aerial Vehicles (MAVs) has gained attentions since more than two decades ago starting from the applications in air combat up to civil applications such as packet deliveries, environmental monitoring, and surveillance. In an environment such as cities that grows denser, navigation and control for drones becomes challenging to ensure safe navigation around buildings and other obstacles. This study proposes an approach for obstacle avoidance for MAVs by using ultrasonic sensors. Four sensors are strategically positioned to cover the front, right, back, and left directions. Additionally, a downward-facing sensor measures the quadrotor’s height above ground. Our goal is to develop autonomous MAV that can avoid obstacles, ensuring safe flight even in complex urban landscapes. The scenario implemented in the study is by introducing obstacle in any directions. When an obstacle is detected by the ultrasonic sensor, the signal will be sent to microcontroller and the attitude of the MAVs, roll or pitch will be adjusted to avoid the obstacle by moving to the counter direction of the obstacle. We conducted 20 trials of experiments by varying the gain values of Proportional Integral Derivative (PID) values, we fine-tune our obstacle avoidance algorithm. Modifications include optimizing roll and pitch adjustments, refining detection height thresholds, and implementing countermeasures after obstacle clearance. The results show that our proposed method has 10% overshoot when detecting any obstacles in different directions to avoid the obstacles. Our findings contribute to the advancement of safe and efficient urban drone operations, bridging the gap between technology and real-world challenges.

The dual lens of diversity: Seasonal variability of avian community structure in urban hills

The dual lens of diversity: Seasonal variability of avian community structure in urban hills

Metropolitan Mosaics: A Study of Literary Representations of Children Amidst Urban Diversity

Urban centers, often touted as melting pots of diversity, present a complex and dynamic backdrop where the voices and experiences of children are both moulded and reflected. This study explores how children’s multifaceted narratives in urban settings challenge preconceived notions of urban life and diversity. Utilizing Louis Wirth’s urban sociology, this research examines how urbanization influences social behaviour and community formation amidst historical and contemporary diversities. Furthermore, Cathy Caruth’s trauma theory elucidates the profound psychological impacts of Partition, revealing how trauma and memory shape the individual and collective identities of children. The childhood studies framework of Chris Jenks and Alan Prout highlights the active agency of children as they navigate these complex urban landscapes, transforming them into resilient actors. By analysing Ikramullah’s “Regret” and Amit Majumdar’s “Partitions”, this paper unveils the intricate tapestry of challenges children face during and after the Partition’s aftermath. The historic 1947 Partition emerged as a pivotal event that redefined urban landscapes and social boundaries, compelling children to contend with multifaceted forms of marginalization and division. This research provides a nuanced perspective on the enduring effects of historical trauma on the lives of children, offering insights into the long-term impacts on urban diversity and the continuous reshaping of urban identities.

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

From Barn Raising to Community Empowerment: the legacy of Karl Linn and John Turner

This article examines the lasting impact of architects Karl Linn and John Turner on community development and informal settlements. Grounded in the barn raising tradition, their distinct yet converging paths highlight the power of community participation, collaboration, and self-help in marginalized neighborhoods. Linn's Neighborhood Commons projects and Turner's ideas on self-help housing and user-centered design have influenced participatory practices, challenged institutional norms, and prioritized a process over product approach in today's complex urban landscapes. This exploration underscores the profound impact of their work on neighborhood improvement and inclusive urban environments.

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AODAERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale.

Crowded and warmer: Unequal dengue risk at high spatial resolution across a megacity of India

The role of climate factors on transmission of mosquito-borne infections within urban landscapes must be considered in the context of the pronounced spatial heterogeneity of such environments. Socio-demographic and environmental variation challenge control efforts for emergent arboviruses transmitted via the urban mosquito Aedes aegypti. We address at high resolution, the spatial heterogeneity of dengue transmission risk in the megacity of Delhi, India, as a function of both temperature and the carrying-capacity of the human environment for the mosquito. Based on previous results predicting maximum mosquitoes per human for different socio-economic typologies, and on remote sensing temperature data, we produce a map of the reproductive number of dengue at a resolution of 250m by 250m. We focus on dengue risk hotspots during inter-epidemic periods, places where chains of transmission can persist for longer. We assess the resulting high-resolution risk map of dengue with reported cases for three consecutive boreal winters. We find that both temperature and vector carrying-capacity per human co-vary in space because of their respective dependence on population density. The synergistic action of these two factors results in larger variation of dengue’s reproductive number than when considered separately, with poor and dense locations experiencing the warmest conditions and becoming the most likely reservoirs off-season. The location of observed winter cases is accurately predicted for different risk threshold criteria. Results underscore the inequity of risk across a complex urban landscape, whereby individuals in dense poor neighborhoods face the compounded effect of higher temperatures and mosquito carrying capacity. Targeting chains of transmission in inter-epidemic periods at these locations should be a priority of control efforts. A better mapping is needed of the interplay between climate factors that are dominant determinants of the seasonality of vector-borne infections and the socio-economic conditions behind unequal exposure.

UAVPal: A New Dataset for Semantic Segmentation in Complex Urban Landscape With Efficient Multiscale Segmentation

Semantic segmentation has recently emerged as a prominent area of interest in Earth Observation. Several semantic segmentation datasets already exist, facilitating comparisons among different methods in complex urban scenes. However, most open high-resolution urban datasets are geographically skewed towards Europe and North America, while coverage of Southeast Asia is very limited. The considerable variation in city designs worldwide presents an obstacle to the applicability of computer vision models, especially when the training dataset lacks significant diversity. On the other hand, naively applying computationally expensive models leads to inefficacies and sometimes poor performance. To tackle the lack of data diversity, we introduce a new UAVPal dataset of complex urban scenes from the city of Bhopal, India. We complement this by introducing a novel dense predictor head and demonstrate that a well-designed head can efficiently take advantage of the multi-scale features to enhance the benefits of a strong feature extractor backbone. We design our segmentation head to learn the importance of features at various scales for each individual class and refine the final dense prediction accordingly. We tested our proposed head with a state-of-the-art backbone on multiple UAV datasets and a high-resolution satellite image dataset for land use land cover classification. We observed improved Intersection over Union (IoU) in various classes and up to 2% better mean IoU. Apart from the performance improvements, we also observed nearly 50% reduction in computing operations required when using the proposed head compared to the traditional segmentation head.

Assessment of 5G Connectivity for UAV Operations in Urban Environments: An Analysis of Network Performance and UAV Communication Architecture

Assessment of 5G Connectivity for UAV Operations in Urban Environments: An Analysis of Network Performance and UAV Communication Architecture

An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples

An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples

Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia

Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia

A Review of Urban Heat Studies in Recife—PE (Brazil): Urban Heat Scales, Landscape Inequalities and Future Perspectives

The objective of this article is to discuss the main studies related to urban heat in the city of Recife, the capital of the state of Pernambuco in Brazil, and contribute to providing theoretical and methodological perspectives for future research and the understanding of the history of these studies. Recife is the core of the largest metropolitan region in the northeastern region of Brazil, characterized by a complex urban landscape marked by inequalities and socio-spatial challenges. Moreover, it is recognized as one of the most vulnerable cities to climate change in the world. Analyzing urban heat in Recife through a descriptive review has the potential to provide a comprehensive analysis of the interactions between physical, social, and environmental elements with urban heat in this city, as well as to identify shortcomings and trends in ongoing initiatives and future perspectives. The discussions encompass the following themes: (a) urban heat scales, ranging from macro to global, in both temporal and spatial aspects; (b) the regional and local contexts of urban growth in Recife; (c) the challenges in urban heat studies in the city, including difficulties in data acquisition and integration of approaches; and (d) current efforts and future perspectives in addressing urban heat in this city. The main challenges for future heat studies include developing means for applicability in urban planning, enhancing observations of the interaction with the urban landscape, overcoming the lack of data sources, and understanding the interaction between scales to develop approaches that integrate the social and physical dimensions of urban climate and its relationship with climate change. Such reflections can also be extended to other geographic areas that share similar patterns of urban growth and socio-spatial complexities, helping to enhance the representation of the Global South in this field.

Analysis using a Model-Based Approach of Path Planning for Multiple Uavs in the Context of Surveying Building Damage Following a Disaster

In the wake of disasters, efficient and timely assessment of building damage is of paramount importance for effective disaster response and recovery efforts. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as indispensable tools for postdisaster surveying due to their ability to rapidly cover large areas and provide high-resolution imagery. The effective utilization of multiple UAVs in such scenarios necessitates optimal path planning to maximize coverage, minimize mission time, and ensure safe navigation. This paper presents a comprehensive model-based analysis of multi-UAV path planning strategies tailored for surveying building damage in postdisaster environments. The study commences by emphasizing the critical role of UAVs in disaster management, especially in the context of assessing structural damage. Acknowledging the challenges posed by complex urban landscapes and the need for swift response, the research focuses on the development and evaluation of path planning strategies that leverage advanced modeling techniques. The proposed model integrates spatial data, building blueprints, and environmental information to generate a realistic representation of the disaster-affected area, facilitating informed decision- making for path planning. A range of path planning approaches is examined within the model-based framework. These include grid-based methods, which discretize the area into cells and deploy UAVs to cover designated regions, and graph-based approaches such as Rapidly-exploring Random Trees (RRT), which generate paths through probabilistic exploration. The paper also delves into machine learning-enhanced strategies, leveraging reinforcement learning algorithms to adapt UAV paths based on real-time observations and evolving disaster dynamics. Furthermore, the study underscores the scalability of the model-based approach, demonstrating its applicability to scenarios involving a varying number of UAVs. Through comprehensive experimentation, the paper provides insights into the optimal deployment of UAV teams, shedding light on the balance between increased coverage and potential communication overhead.