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Human mobility data such as global positioning system (GPS) data from mobile phones and smart card data of public transportation have been used for the analysis of a city. Those studies have attempted to create networks whose nodes represent areas of the city and whose edges represent human flows between areas. Network clustering methods are applied to those networks for extracting community structures of cities. Although many studies have attempted to extract community structures of cities using human mobility data and network clustering methods, little has been explored about which method is the most effective, in terms of the accuracy and the amount of information of extracted clusters. In this paper, we propose evaluation metrics for evaluating network clustering methods based on the geographical cohesiveness, the regularity, and the amount of information of extracted clusters. We use smart card data of public transportation collected in Japan for testing the effectiveness of each method. We compare two types of origin-destination matrices for constructing networks and four types of network clustering methods including two types of bipartite network clustering methods. The results confirmed that a bipartite network clustering method considering multi-facet characteristics of each cluster and the functional relations between areas of a city shows the most accurate and richest information about community structures of cities.

Recently, the rapid increase in mobile data traffic and the diversification of wireless communication services have led to demand for the development of high-quality mobile service areas. Therefore, the modeling of complex radio propagation characteristics in the practical communication environment is an important issue. Radio propagation prediction methods based on machine learning have been proposed, but no study on the appropriate range of urban structure maps as features has been conducted. In this paper, we clarify the appropriate extraction range of map data, applying XGBoost to the machine learning algorithm. Moreover, since XGBoost can output the importance of the input features, we can extract only those features that are useful for training and input them to machine learning. This approach is expected to improve both prediction accuracy and speed. The evaluation using measurement data obtained in urban areas showed that the computation time for learning can be reduced by about 80% while improving the prediction error.

In December 2019, a novel coronavirus disease (COVID-19) suddenly emerged in Wuhan, China. In March 2020, WHO declares the coronavirus outbreak a pandemic. The virus has spread quickly to all over China and most of the countries and regions within the increasing urbanization and globalization, infected more than three million people worldwide. A crucial factor that may significantly affect the spread of COVID-19 is the multiple, interactive, emergent, and complex characteristics and systems of the social systems. This paper describes a systems approach modeling and analyzing the emergence and spread of COVID-19 in urban systems, seeking to combine the multi-layer urban structure between complex infrastructure systems, human activities and policy systems. Moreover, a complex network model is built to illustrate the diffusion of the virus with or without the intervention of policy systems under the different policy intensity by the changed basic reproduction number (R 0 ). Besides, a system dynamics model, including feedback loops and changes, is proposed to demonstrate how the COVID-19 spreads out under the interactive and interrelated characteristics and systems of the complex systems at different levels.

In recent years, mobile data traffic has been increasing, and high-quality mobile communication services are required. Therefore, it is essential to understand the complex radio propagation characteristics in an actual communication environment. In this paper, we propose a method of predicting radio propagation characteristics by machine learning called gradient boosting, in which the feature is the building information around a transmission point and a receive point, which greatly affects radio propagation characteristics. Gradient boosting is a method of constructing a prediction model using a plurality of weak learners, and it is possible to output the importance of the input feature. That is, it is possible to quantify which feature had a large impact on the radio propagation prediction, and to verify the validity of the model. We evaluated the prediction accuracy using the measured data in an urban area, and clarified the effect of the difference of the feature on the prediction accuracy.

The landmark Recognition of any urban structure is a frustrating task to accomplish. This paper presents a deep learning based approach for recognition of the urban structures. Urban structures are basically the usual offices, houses, apartments etc. A generalized word for all these buildings can be urban structures. This classification problem was identified by our team while making our way into the visual positioning system, we saw that the visual landmark recognition of urban structures was an unexplored domain by using deep learning approaches. We also Identified that unlike the landmarks such as the Eiffel Tower, Statue Of Liberty etc, urban structures were difficult to learn by a neural network, the data requirements were quite distinct compared to the ones mentioned for all these famous landmarks around the globe. Most of all the landmark database was created, augmented, labelled and preprocessed in lab. The transfer learning based approaches were not that useful since the overall size of the model would always be so enormous and integration of a transfer learning based model would be meaningless for any deployed web app or even for a local mobile based app. By collecting and engineering our own data using a data collection app, we have proposed a neural architecture which can be configured to learn multiple number of urban landmarks while retaining its overall size under 60MBs. This was achieved by a Convolutional Neural Network based binary classification model trained on landmarks existing in our university. Later this was deployed on a web based application and further on it is being made into an app.

City center is an essential components of urban structure that rules urban activities including economy, transport, and social interactions. In Bangkok, Thailand, the railway network is expanding and the expansion is expected to affect the city center locations. In this study we attempt to capture the effect of public transport network on the accumulation of three types of urban core facilities based on the spatial statistical approach, and estimate the future perspective of locations of those facilities. As a result we found that expected number of facilities in current urban core in Bangkok decreases and the number of facilities at stations on planned railways increases under certain conditions. The results can be utilized to estimate the future travel pattern and residential locations.

As cities undergo rapid sprawl and urbanization, it is commonly becoming a difficult task to find public toilets available. Building new public toilets becomes a promising way to alleviate this issue. However, where to build new public toilets is challenging. Traditionally, urban planners rely on empirical experience and surveys to understand the local toilet demand, which is unreliable and also time and labor-consuming. In this paper, we propose a data-driven approach to tackle the site selection problem of public toilets. Specifically, we propose a two-phase framework to discover knowledge from existing public toilets and use them to guide future planning and construction. In the first phase, we identify the candidate areas for new public toilets based on human mobility, land use, urban structure, and etc. In the second phase, we propose a learning-to-rank method to predict the demand level of the candidate areas and identify the optimal sites for placing new public toilets by simply ranking. Our approach combines the geographic characteristics of the city with mobility patterns of human activity by considering human activity, area functionality, road network, and the toileting demand of taxi drivers. Finally, we evaluate our approach by using multiple datasets including the taxi GPS trajectory data, POI data, and road network data in the real world from the city of Chongqing, China. The experimental results demonstrate the effectiveness of our proposed method.

The performance of Convolutional Neural Networks (CNN) in satellite image classification tasks has been found superior to that of traditional algorithms. However, comparatively fewer studies have experimented with CNN-based classifiers to classify intra-urban characteristics with open mid and low-resolution earth observation (EO) datasets. The current pace of urbanization necessitates understanding and mapping of inherent urban forms, which would further assist in devising policies pertaining to sustainable urban development. While several remote sensing methods have been utilized to understand the urban structure, the replicability and generalizability of such approaches have been some of the key limitations. This study creates the CNN model to identify the degrees of built arrangement in mid resolution Sentinel 2B imagery of ten largest Indian cities. Training and testing datasets for seven land cover classes such as compact, open, sparse built, paved, unpaved, greens, and water are manually created with the help of Google Earth Pro platform. The definitions of the classes are acquired from the LCZ classification scheme. The CNN model trained with the prepared dataset provides the overall accuracy of 90% and kappa value of 0.88. The classification results are plotted for each city and compared with each other. The study has potential in the large-scale assessment of built forms of cities for quick assessment and policy formulation.

Nowadays, urban structure is decisively influenced by the global spread of strategies for transforming urban environments into smart cities. The continuous growth of urban population and the digital transformation aim to leverage the changes and opportunities of digital technologies and their impact across society as well as the more empowered and demanding citizens are essential factors in this approach. All activities in smart city are focused on new and smart technologies that address the urban management and governance issues. Information and Communication Technologies (ICT)-based solutions enhance the interconnectivity provided by the Internet of Things (IoT) capabilities and embrace the new evolution of citizen empowered in handling information and in resolving complex problems. Accelerated by the innovation process in the public service sector, the digital public services must meet the expectations of the new wave of users that can't anymore dissociate their daily activities from the large-scale use of smart technologies. IoT has already proven to better sustain the sensing and connectivity, becoming thus an efficient tool to address a broader range of qualitative public services and to proactively involve the more demanding citizen. A smart citizen expects new types of digital public services able to support his/her engagement in the streamlining of the decision-making processes at the community, regional or national level. He/she is connected at an enhanced level with the appropriate public authority in order to deliver particular data and information or to demonstrate a personal point of view. In the same time, the public authorities are enabled to process the gathered data and the multi-dimensional insights derived from it, generating afterwards urban improvements, efficiency, or patterns to evolve. This paper highlights the main features of the new emerging smart citizen in the context of the already existing digital citizen and how the IoT has the capability to transform the interaction between the citizen and the public authorities, and the digital public services themselves. A proposed reference architecture of a digital public service based on IoT aims to demonstrate how the capabilities of this expandable technology can meet the new requirements in the specific context of a smart city and citizen.

The interaction between travel behavior and land use has drawn much attention for decades. Instead of satellite images, this paper aims to establish a link between human mobility and urban structure using massive taxi data and POI data based on real world. First, the 24-dimensional drop-off/pick-up temporal difference vector (DPTDV) is proposed and used to identify travel patterns. Then, the study area is classified into different clusters according to the hourly variation of the difference between drop-offs and pick-ups. Further, twelve POI features and two indicators are extracted to explore the land use characteristics corresponding to each specific travel pattern. Meanwhile, the relationship between travel activity and land use intensity is linearly fitted. The results from this case study will allow us to have a deeper understanding of urban dynamics, help alleviate traffic congestion and provide guidance for smarter urban construction.

The recent availability of geospatial data on human movements and communications has provided new insights into the understanding of human mobility. This study explores a data set of people flow which contains diverse travel modes in the Tokyo metropolitan area. We constructed a spatially embedded network to model the mobility interactions in urban space and then utilized the community detection method to reveal the sub-regional structures in individual travel modes and as well as the aggregated one. Two patterns of hierarchical polycentric mobility structure were revealed, and then the similarity and difference in sub-regional structures between different travel modes was analyzed. Furthermore, we examined how the mobility structures under different travel models collectively form the overall urban mobility structure. This work sheds some light on improving our understanding of the spatial aggregation of mobility and is of potential value for transport planning.

Southeast Asia is one of the world’s fastest growing economic regions. Many metropolitan areas meanwhile have suffered from unsustainable transportation system, mainly owning to exploding private vehicle dependency. The prerequisite of designing policy having a negative impact on car dependency is to ascertain determinants of household vehicle ownership level. This study aims to investigate the impact of socio-economic characteristics and urban structure factors on a number of vehicles owned by households in Metro Manila at the disaggregate level, using the Multinomial Logit (MNL) regression. The empirical findings highlight that households getting higher accessibility to railway stations are less car-dependent, while households living close to a central business district (CBD) are likely to own more vehicles. Another finding indicates that households residing in the high-density area have the highest propensity to hold more vehicles, contrary to existing studies conducted in developed countries. For scenario analysis, improvement of accessibility of residential areas to railway stations throughout the metropolis would have reduced vehicle fleet, energy demand, and CO 2 emissions by about 50% if it had been introduced earlier.

Urban structure types (USTs) are a concept in urban ecology to divide cities into units of homogenous environmental conditions. They provide a useful means to conceive efficient strategies for sustainable urban development. Cutting edge remote sensing data and methods allow for the automation of the UST classification process. This paper aims at demonstrating the robustness of a recently developed approach for area-wide mapping of 13 USTs. To this end, the experimental setup of this work includes three major cities in Germany, different sets of high-resolution multi-source data as well as statistical measures to enable the assessment of methodological accuracy and transferability. The results of this study emphasize the suitability of the presented approach with regard to classification accuracy, workflow automation, and operational readiness for future UST mapping and monitoring tasks.

Due to the accelerating rate of development of Mobile Internet, the role of mobile phone becomes increasingly vital in people's life. Through the data generated by mobile phones, users' mobility characteristic and online interests can be roundly mined. By connecting human mobility and online behavior, many achievements have been proposed and then applied in many services to improve the quality of people's life. In this paper, by using the cellular network data collected from certain urban city in northeast China over a month, we first extract users' important locations. Based on this, we apply the topic model to extract mobility topics of users and explore users' application usage. We observe that different user mobility properties accompanied with different online behavior tendencies of users. Besides, we extract cross-town events based on users' important locations to explore the macroscopical organization of the city. The results of our research provide a comprehensive understanding of user behavior in Mobile Internet and city structure, which are valuable for the practitioners in the fields of Location-Based Services (LBS) and urban planning.

Urban function zoning is of great importance for urban structure optimization, urban resource allocation, and urban development planning. Since citizens usually act as a network of motion sensors of the city, their activities could reflect the environment around them. We considered taking advantage of VGI data to classify urban function zones. In this paper, we proposed a framework for automated urban function zoning which is based on VGI geo-tagged photos and OpenStreetMap (OSM) data. Through combining the high-level image features of geo-tagged photos with the road network data, we obtained the functional zoning map of the study area. The experiment result shows the effectiveness of the framework we proposed.

In this study, we propose an agent-based model (ABM) that expresses simplifed urban dynamics and verify how policies that affect the daily travel of urban residents bring about a change of an urban structure toward a desirable formation indirectly. The policies include a location of a public facility where residents drop by during their daily commute, measures to promote street attractiveness around such a facility, and ones on transportation such as laying tramway in an urban central area. As a result, we clarified significant differences of improvement effects on an urban structure depending on the locations of a facility, the implementation scale of each promotional measure around the facility, and also demonstrated the synergy effects of the policy on transportation.

In this paper, we propose a mathematical model for intelligent transportation systems (ITS) in an urban environment, which takes into account both the urban structure and vehicles. Using the stochastic geometry approach, we model the locations of vehicles with a Poisson point process on roads whose interval is a fixed value. We consider typical vehicle-to-infrastructure and vehicle-to-vehicle communication scenarios and derive theoretical values of the probability of successful transmission. Our results from numerical experiments reveal how the urban structure can affect wireless communications in ITS.

Identifying the spatial structure of travel activities could help better understand the urban structure, as the structure of a city is heavily shaped by its transportation system. Today the emerging traffic big data contributes a lot to delineate daily travel activity of individuals. In this paper, multi sources of travel data are utilized to give a full description for the spatial structure of travel modes in a city. Firstly, the massive intra-city travel flow data are constructed as connection embedded networks. Then, using the community detection method and the network analysis method, the latent spatial connection structure of travel modes and corresponding properties of sub-networks are investigated. Finally, a case study of Guangzhou is conducted using travel data of three travel modes. Results imply that revealed structures correlate with the land use and the geographical distance. The taxi flow network shares similar characteristics with the bus flow network, but the mass transit railway system is more independent.

Each city exhibits recurring patterns consisting of similar building types, vegetation structures, and open spaces, enabling environmental and socio-economic investigations of the urban fabric. In this study, urban structure types (UST) of the city of Berlin are mapped on the basis of a prior land cover classification utilizing a synergistic approach of knowledge based classification and Random Forests. The results are then compared to the outcomes of a previous analysis regarding a subarea of the utilized high spatial resolution airborne data. Results show that UST classification based on a combination of prototype objects and Random Forests is suitable to generate accurate UST maps for these areas with only minor adaptations. Future analyses will focus on transferring the processes to different German cities and data of several sensors.

The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone call detail record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a call detail record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.

This paper presents a methodology of estimating population at 15-m resolution from ASTER-based urban extent map and existing population grid data. We proposed two methods of estimating population: dasymetric mapping and regression model. Experiment for Tsuchiura City, Japan, and Pasig City, Philippines, showed visually good distribution of the estimated population at 15-m resolution. The data represented population in a better way by representation corresponding to urban structure at 15-m resolution. The result indicated that there were spaces to be improved by introducing new data explaining human activities and iterative machine learning between the two methods proposed.

Economic urban spatial structure has profoundly impacted economy operation efficiency of city, quality of living environment and quality of life. This study focused on a typical multi-center city in China that incorporated geostatistic method with quantitative regression under the assistance of Geographic Information System. The results show that spatial amenities play an important role in reducing the attraction of traditional urban cores, easing the price gradients and constructing polycentric urban structure. The final findings could be used by urban planners and authorities in their attempts to optimize the economic structure of urban spatial, guide the location and scheduling of real estate development with the aims of sustainable urban development.

Sustainable urban planning and management demand innovative concepts and techniques to obtain up-to-date and area-wide information on the characteristics and development of the urban system. The increasing availability of Earth Observation (EO) technologies has provided new opportunities for a wide range of urban applications, such as mapping and monitoring of the urban environment, socio-economic estimations, characterization of urban climate, analysis of regional and global impacts and urban security and emergency preparedness. However, a gap exists between the research-focused results offered by the urban EO community and the application of these data and products by urban planners and decision makers. Hence, the main objective of the GEOURBAN (ExploitinG Earth Observation in sUstainable uRBan plAnning & maNagement) project is to bridge this gap by demonstrating the ability of current and future EO systems to depict parameters of urban structure and urban environmental quality at detailed level. This paper introduces the conceptual approach towards the integration of EO-based geo-information into urban and spatial planning and presents and discusses first results of the indicators developed and evaluated by using remote sensing data.

Stable night lights imaged by the VIIRS instrument on the NASA/NOAA Suomi NPP satellite provides higher spatial resolution and greater dynamic range than DMSP-OLS night light data. We use coregistered OLS and VIIRS night light composites collected in April 2012 to map night lights from a wide variety of settlements in Brazil, India, China and the USA. Color composites of VIIRS and OLS brightness data with Log10VIIRS filtered brightness clearly show the benefits of VIIRS higher spatial resolution and greater dynamic range. Intra-urban variations in brightness are detected by VIIRS in urban cores saturated in the OLS imagery. VIIRS is also able to distinguish small point sources of light at scales approaching 1 km where OLS registers only low luminance background light often indistinguishable from overglow. Comparison of power spectral density estimates of VIIRS and OLS transects show that VIIRS resolves ~10 dB more signal than OLS at scales finer than 70 km. This difference is apparent in VIIRS ability to resolve roads and small villages interconnecting vast spatial networks of larger settlements observed with OLS.

In recent year, much progress has been made in outdoor 3D mapping. However, 3D mapping in real time is still a daunting challenge in urban environment. This paper addresses the problem of 3D mapping from 3D laser scans in urban environments in real time. To do this, we proposed geometric featured voxel which can efficiently represent 3D urban structure without loss of geometric properties. For evaluation of the proposed voxel, we use Oakland dataset.

This paper proposes a novel radar imaging simulator based on analytical electromagnetic and geometric models for a common typical kind of urban structure. The analytical models are presented not only for improving the efficiency of the simulation but also for direct and further understanding the features in the SAR image.

at present, China's urbanization is in a stage of rapid development, resulting in the existing "single-core" urban spatial structure has been unable to meet the requirements of the dense population and various economic and social activities in the cities. Therefore, to alleviate population pressure in urban centers, many new districts have been gradually risen around some big cities, and these cities gradually formed a "double-core", or" multi-core" urban structure. The development of residential projects which related to the housing problem of the new urban residents, become an important development project in the building new districts. And as a real estate development business, how to seize the favorable opportunities for developing new districts, and through the development and construction of residential projects, get high profits, become a hot research issue by many real estate companies. In this paper, using the residential project of new districts in Harbin City, through researching and studying 20 residential projects of new districts in this city, analyzing and summarizing the important factors which affect the value of the residential project in the new district, and establishing a model to predict the comprehensive value of the residential projects in new district, which will provide an effective basis for the real estate business when they make the investment decision for the development of residential projects in the future.

In the rapid urbanization process, there are many irrational existences of urban morphology, thus a large amount of potential disasters lurk. The paper aims to throw a light upon the present urban morphology and some conflicts about urban disaster prevention. This research was given from the viewpoint of urban disaster prevention through several cases of different types of urban disaster. Then the relations between the optimization of urban morphology and urban disaster prevention were analyzed, e.g. location of the city, urban size and environmental capacity, direction for urban development, space urban structure, etc. Furthermore, an application was probed, which was used by some space information technology, such as GIS, in the disaster-oriented urban form optimization.

The paper aims at the major influence of the introduction of high-speed railway terminal, especially the impact on urban spatial structure, through the method of the research of the cases of the development domestic and foreign high-speed railway terminal.

This paper takes Wuxi as a case study and analyzing the characteristics of urban expansion from the viewpoint of urban scale and urban structure, here we emphasize that urban spatial expansion is not only build-up area increase, but also the change land use structure. We further explain the reasons that have given rise to these changes. We conclude this case study with some observations on the impacts upon Wuxi's environment in the wake of the rapid urban expansion during the last three decades.

This research simulated the urban growth scenarios of a mountain city of Chongqing, China through a CA-based model of SLEUTH. The fundamental data were four Landsat TM images of 1978, 1988, 1993, and 2001 which were utilized for generating maps of land use, road networks, and urbanized area. Three alternative development scenarios, including business as usual, compact city and decentralized concentration, were interpreted into the model. Business as usual assumes historical development would carry on. Compact city aims to achieve a compact and continuous urban form. Decentralized concentration promotes polycentric urban structure. The result presents several interesting findings with important planning implications. First, it was noted that predominant urbanization modes in mountain regions were "spread", indicating that new developments usually occurred at the edge of existing urban areas. Second, "breed" indicating that emerging small and sporadic urban patches can easily become new growing poles and induce new developments, was another major growth mode. Moreover, setting limitation to development on steep slopes has largely restricted urban expansion and effectively protected forests. However, it also suggested that there exists no optimal scenario as urban development and nature conservation are conflicting goals in terms of the selected landscape metric.

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability.

People's increasing demands for higher environmental landscape quality have promoted rapid development of the construction of urban lighting landscape. At present, Chinese Urban Lighting Planning system is not yet perfectly organized, it is necessary that the method of making an urban landscape lighting plan needs in-depth study and then be put into practice. This paper first discusses the conceptual framework of urban landscape lighting master plan; proposes, based on urban structure, the theoretical foundation and a new planning method of urban landscape lighting. It provides a scientific basis and effective theoretical supports for the planning method of master plan of urban landscape lighting as result of a case study of Urban Lighting Planning of Central City District of Tianjin.

This paper discusses the water environment vicissitude in the middle reaches of Yangtze River from four stages in history. Taking several typical cities Jingzhou, Wuhan, Yichang as examples, the author analyzes the impacts of water environment vicissitude on urban spatial position, structure, function, rise, fall and distribution in the middle reaches of Yangtze River.

On the basis of Tiebout's theory about optimal provision of public goods and achieve conditions, We have tested Tiebout's hypothesis on the basis of Chongqing district by operating an empirical model, and put some corresponding policies. This paper finds that in Chongqing China, although it has a significantly different conclusion from USA owing to the difference of urban structure, and we have take into account the impact of these differentiae. This paper draws a similar conclusion to Oates' theory with full awareness of the impact of differentia resulting from different urban structure between Chinese and American cities. Thus we prove the validity of Tiebout's theory. Our results indicate that property taxes does affect the value of owner occupied housing and thus imply that appreciable tax capitalization does occur in Chongqing ; and the quality of regional public servings have affected residential property values.

In this paper, NOAA/AVHRR data were used, which were obtained in winter of 2004 and 2006 respectively. And using NOAA/AVHRR data in spring of 2004 as reference, day-night characteristics and spatial distribution characteristics were received. The following are research results. In the daytime, urban heat island intensity was stronger than that at night. The maximum of urban heat island intensity was about 7.5 ° and 9.0 ° respectively.At night, urban heat island demonstrated a single-center or double-centers, and urban heat island center lied within the second circuit road. In the daytime, urban heat island demonstrated multi-centers. Urban heat island centers occurred in industrial and commercial districts, in transport hub, which locate in the north, the east, or the southwest of the city. Little higher temperature zone distributed like a non-closed ring. High temperature ring occurred nearby the second and third circuit road. The ring characteristic of urban heat island had close relationship with urban structure and urban activities, which was consist with some research results in summer.

Conventional thinking in urban remote sensing is to synchronize the dates of when satellite imagery is captured with the date a social survey, such as a population census, is collected. It's assumed that identical dates, or dates as similar as possible, are intuitively important in order to make sure that structure, the physical configuration of a city as captured by remote sensing, relates as closely as possible to function, the socio-economic characteristics of a city, as represented by population censuses. However, this type of intuitive thinking is surely flawed because the structure of a city is hardly ever in harmony with the functionality of a city, at the same time. It is much more realistic to assume that there is a temporal lag between the socio-economic demands from a city's population and the consequential physical ramifications of such demands. In other words, the tangible layout of a city, i.e. roads, houses, etc, is a result of prior decisions made by planners with respect to demands from the city's population. This paper explores this temporal lag between structure and function by comparing the physical structure of cities from satellite images at T+1 with the functionality of the socio-economic characteristics from a population census in T−1. Initial results suggest an improved “agreement” between satellite images taken after a population census, in terms of predicting population growth and predicting residential demands. Further testing is needed in order to establish a theoretical link that can be used by planners in deciding where to develop cities, how sustainable such increased development can be contained and how best to reduce overcrowding, congestion and overall social deprivation.

Due to continuous world-wide urbanization, especially in developing and emerging countries, urban environment represents one of the most dynamic landscapes on earth. The role of human settlements is becoming more and more important in land management, and the various structures of urban areas differ tremendously depending on social and historical backgrounds. So, beyond land-use and land-cover information, it is the urban composition that explains for social disparities, vulnerability to natural hazards, and deficits in urban and regional planning. In this study, first steps are presented towards and urban structural analysis using a multi-sensoral approach. With very high spatial resolution imageries, remote sensing offers techniques well-applicable to measure and monitor intra-urban heterogeneties. Imageries from Quickbird sensor and TerraSAR-X device, Spotlight mode, are taken to delineate field-mapped and well-defined urban structure types (UST). A robust rule base will be built which allows for accurate classifications and interpretation of typical-man-made structures. The most typical structural characteristics of urban regions are alignment and type of buildings, open and green spaces, and transportation network.

Land-use and land-cover (LULC) and their changes have significant influence on heat balance, especially in the urban area. Therefore climate measurement stations are permanently set-up on different land uses, and additionally mobile climate measurements are carried out every year at the same time and similar climate conditions. The study area is Santiago de Chile, one of the most dynamic and fast growing cities in South America. Urban Structure Types (UST) are delineated for areas under investigation in which the urban morphology is rather characteristic for the social strata. UST are compared with climate parameters to find out if significant correlations exist between UST and climate measurements. The overall goal is to estimate environmental justice in large urban agglomerations.

The urban landscape is a highly complex and small-structured, heterogeneous area as a result of permanent human settlement. Urban structure is scale-dependant and can be expressed on various levels of detail by satellite imagery. Very high resolution satellite (VHR) sensors are capable of mapping and monitoring cities - on house/block level - with their high degree of landcover diversity. However, detection of morphological features such as shape and elevation of single objects is performed much better when a digital surface model (DSM) – e.g. derived by airborne laserscanning - is incorporated. An object-oriented methodology for the joint analysis of optical satellite data and a digital surface model is presented for the classification of the urban morphology in terms of urban structural types. These are spatial units – mostly on block level – with aggregated information on the classified single features like landcover/landuse and urban fabric. Hence, a hierarchical, modular segmentation and classification workflow is implemented to extract the required information. The methodology is applied on two study areas in the cities of Cologne and Dresden, Germany, and a validation of the capability of the potential for transferability of the rulebase is shown.

In this paper, we study the time-optimal trajectory planning of a sensor attached to an unmanned aerial vehicle (UAV) to provide complete 3-dimensional coverage with applications to urban environments with 2.5-dimensional features. The basic approach is to approximate the features of interest with a set of non planar coverage surfaces and to design a motion plan that guarantees the coverage surface is swept completely with a conical-field-of-view sensor. We establish a lower bound on time for a UAV to achieve complete coverage and derive the analytical coverage plan whose duration is a constant times this lower bound. Our hardware-in-the-loop simulation results verify the effectiveness of the proposed algorithm.

Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks to their very high ground geometric resolution, especially when more frequent revisit times will be feasible to the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the single building scale. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the December 26th, 2003, earthquake that hit the ancient city of Bam (Iran) has been investigated. The urban area was very close to the epicenter of the seism thus causing strong damage to the urban structures. Pre- and post-earthquake QuickBird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided by in situ survey.

The purpose of this work is, starting from a close tracking of basic electromagnetic processes, to propose a simulator enabling a better understanding of the radar images of urban areas. An improved understanding of the radar images formation processes in this case should lead to a better knowledge of the potentialities of the SAR imagery of urban areas. With the rapid development of computer, numerical techniques with no physical approximation play more and more important role in some studies such as electromagnetic scattering from urban scene. We argue that finite-difference time domain method (FDTD) is a quite new and promising approach for the propagation in urban areas. In order to numerically evaluate its electromagnetic return to an active microwave sensor, a geometric and electromagnetic model of a typical element of urban structure is presented. It consists of a rectangular parallelepiped whose vertical walls form a generic angle with respect to the sensor line of flight. This parallelepiped is placed on an either smooth or rough surface, to take into account multiple scattering between buildings and terrain. Finally, the above model is used to analyze the field backscattered from a building, as a function of the main scene parameters.

Urban structure design and new energy technology could be minimizing energy consumption. By miniaturizing, improving the efficiency, and appropriately placing of supply system of energy equipment within the sight of the urban area, the consumption of energy may be minimized. To develop those urban environmental system, we must know the potential resource of new energy in urban area, local energy demand, the appropriate place to settle the system, or the more effective size of the system, and relationship between the different size of systems. The first step of consideration, we have studied a real case in the urban resident area-Makuhari Baytown. The area profile, energy flow of the district, and the effective area dimension of solar systems are discussed. As the result, a difference among levels in the amounts of potential energy is found, the effective energy supply system for each level should be considered

Kohonen Self-organizing Map (SOM) has been widely used to discover clusters in datasets of various real world applications. Urban structure, as characterized by various social, economic, and environmental features, can be explored with SOM. In this paper, we present a case study of applying SOM for urban structure analysis to the city of New Orleans. One novel aspect of this work is the inclusion of environmental data from satellite imagery for clustering. Ten social-economic variables from Census 2000 data and Normalized Difference Vegetation Index (NDVI) from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery were used as inputs to SOM to group 482 census block groups of New Orleans into 9 clusters. The clustering results show that block groups with high economic status and environmental quality tended to cluster at 3 major outer locations, while block groups with low economic status and environmental quality were concentrated in the mid-city area, which is a well known pattern due to the suburbanization process. Three major components were extracted by a principal component analysis, and they were compared with the clustering results from SOM. Fisher's linear discriminant analysis shows a good separability result among 9 clusters discovered by the SOM.

Range sensing technology allows the photo-realistic modeling of large-scale scenes, such as urban structures. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality applications. Man-made urban scenes provide an abundance of linear features that can be used for the solution of the problem. Many scenes though require the utilization of non-linear primitives. This paper provides a solution of the registration problem based on the robust detection and matching of circular features from the range data sets. We present results from experiments with complex range scans from the interior of a large-scale landmark urban structure (Grand Central Terminal, NYC), where traditional methods would fail. This work is part of a larger range registration system that is based on extracted features of multiple geometric types.