Information For Urban Planning Research Articles

Influence of Building Recognition of High-point Monitoring Image by the Optimized Faster R-CNN on Urban Planning

The purpose of the study is to obtain more powerful data in the process of urban planning, and more excellent building recognition algorithms for the high-point monitoring images in the city.26 A new algorithm based on Faster R-CNN (Faster Regions-Convolutional Neural Networks) is proposed, and the optimization methods involved are systematically described, and then a simple description of the Faster R-CNN algorithm is made for optimization. Subsequently, experiments are carried out on the MS COCO (Microsoft Common Objects in Context) datasets, and a series of relevant experimental demonstrations are conducted on the optimization scheme. Different features are used to extract networks; some practical optimization methods are added and the training method is modified; the speed and accuracy are paid attention to, and the expected goal of target detection is achieved. Based on this high-point monitoring image, the speed and accuracy of building recognition are greatly optimized. Urban managers will have more reliable information in urban planning, which has a positive impact on urban development.

Three-dimensional building morphology impacts on PM2.5 distribution in urban landscape settings in Zhejiang, China

Three-dimensional (3D) urban landscape patterns and building morphology are crucial for urban planning and essential for urban landscape functions. In this study, fixed and mobile monitoring sites were used to determine the spatial distribution of PM2.5 concentrations in Hangzhou. Six 3D metrics were selected to analyze the response of PM2.5 pollution to landscape patterns and building morphology, while their two-dimensional (2D) counterparts' metrics were also analyzed to contrast the differences. A variance partitioning analysis (VPA) was performed to measure the combined and relative contribution of 3D and 2D metrics to the changes in PM2.5 concentrations. The results showed that: (1) on the 3D scale, forming a building pattern with a combination of different building heights can eliminate the accumulation of PM2.5; (2) on the 2D scale, fragmentation and decentralization of landscapes and building patches alleviate PM2.5 pollution; and (3) 3D building morphology indicators have the highest explanatory power (40.94%) for the changes of PM2.5 concentrations. It turns out that the explanatory power of 3D metrics for PM2.5 concentrations changes is much greater than that of 2D metrics. In addition, when compared to building morphology indicators from a single dimension, the combination of 2D and 3D metrics is better able to reflect urban PM2.5 pollution. The results of this study expand our understanding of how PM2.5 pollution responds to 2D and 3D metrics and provide useful information for urban planning.

Study on the mechanism of urban planning information exchange to enhance the community participation in urban detailed planning in Vietnam – towards sustainable development

This paper explores the extent of the current mechanism to access urban spatial planning information of community in urban detailed planning projects (UDPPs), the constraints on urban planning information accessibility in Vietnam context to challenge the community participation for sustainable development. The research is based on an analysis of the planning information sharing content found in four UDPPs in Vietnam and interviews with 20 key informants. The research identified that the urban spatial planning information for the community is limited and difficult to access, and the planning information exchange process is one-way flow, authority-controlled and non-transparent. Besides, the lack of human resources and technological application provide a challenging context for involving the community in UDPPs. The paper concludes by proposing several recommendations for improving the urban planning information exchange process and technological tools that should be applied to foster community participation in Vietnam's urban context, towards sustainable development.

Scene-Change Detection Based on Multi-Feature-Fusion Latent Dirichlet Allocation Model for High-Spatial-Resolution Remote Sensing Imagery

With the continuous development of high-spatial-resolution ground observation technology, it is now becoming possible to obtain more and more high-resolution images, which provide us with the possibility to understand remote sensing images at the semantic level. Compared with traditional pixel- and object-oriented methods of change detection, scene-change detection can provide us with land use change information at the semantic level, and can thus provide reliable information for urban land use change detection, urban planning, and government management. Most of the current scene-change detection methods are based on the visual-words expression of the bag-of-visual-words model and the single-feature-based latent Dirichlet allocation model. In this article, a scene-change detection method for high-spatial-resolution imagery is proposed based on a multi-feature-fusion latent Dirich- let allocation model. This method combines the spectral, textural, and spatial features of the high-spatial-resolution images, and the final scene expression is realized through the topic features extracted from the more abstract latent Dirichlet allocation model. Post-classification comparison is then used to detect changes in the scene images at different times. A series of experiments demonstrates that, compared with the traditional bag-of-words and topic models, the proposed method can obtain superior scene-change detection results.

Risk assessment of shanghai extreme flooding under the land use change scenario

Environmental changes have led to non-stationary flood risks in coastal cities. How to quantitatively characterize the future change trend and effectively adapt is a critical problem that needs urgent attention. To this end, this study uses the 2010 Shanghai land use data as the base and utilizes the future land use simulation (FLUS) model to simulate future land use change scenarios (2030, 2050, and 2100). Based on the results of storm and flood numerical simulations, probabilistic risk, and other multidisciplinary methods, extreme storm and flood risks of various land uses (residential, commercial and public service, industrial, transportation, agricultural, and other land) in Shanghai are analyzed. Our findings demonstrate that the future land use simulated results show that the simulation accuracy is very high, meeting the needs of our research. We evaluated future land use exposure assets and losses and found that their spatial distribution patterns are consistent, ranging from a sporadic distribution for 1/10-year to a banded distribution for 1/1000-year under the two emission scenarios. In terms of economic loss, the losses of total land use in Shanghai for 1/1000-year in 2100 are 1.8–2.7 times that of 2010 under the RCP8.5 scenario. The expected annual damage (EAD) of Shanghai’s land use in 2030, 2050, and 2100 is 189.9 million CNY, 409.8 million CNY, and 743.5 million CNY under the RCP8.5 scenario, respectively, which is 1.7–3.0 times the EAD under the RCP2.6 scenario. Among them, residential, commercial and public service land as well as industrial land has the highest EAD. Risks are mainly distributed in the city center, the lower reaches of the Huangpu River, the northern shore of Hangzhou Bay, the Qingpu (QP)–Songjiang (SJ) depression in the southwest, and Chongming (CM) Island (southwest and northeast). Our work can provide meaningful information for risk-sensitive urban planning and resilience building in Shanghai. These multidisciplinary methods can also be applied to assess flood risk in other coastal cities.

Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China.

The surface urban heat island (SUHI) effect poses a significant threat to the urban environment and public health. This paper utilized the Local Climate Zone (LCZ) classification and land surface temperature (LST) data to analyze the seasonal dynamics of SUHI in Wuhan based on the Google Earth Engine platform. In addition, the SUHI intensity derived from the traditional urban–rural dichotomy was also calculated for comparison. Seasonal SUHI analysis showed that (1) both LCZ classification and the urban–rural dichotomy confirmed that Wuhan’s SHUI effect was the strongest in summer, followed by spring, autumn and winter; (2) the maximum SUHI intensity derived from LCZ classification reached 6.53 °C, which indicated that the SUHI effect was very significant in Wuhan; (3) LCZ 8 (i.e., large low-rise) had the maximum LST value and LCZ G (i.e., water) had the minimum LST value in all seasons; (4) the LST values of compact high-rise/midrise/low-rise (i.e., LCZ 1–3) were higher than those of open high-rise/midrise/low-rise (i.e., LCZ 4–6) in all seasons, which indicated that building density had a positive correlation with LST; (5) the LST values of dense trees (i.e., LCZ A) were less than those of scattered trees (i.e., LCZ B) in all seasons, which indicated that vegetation density had a negative correlation with LST. This paper provides some useful information for urban planning and contributes to the healthy and sustainable development of Wuhan.

The Interactive Modeling Method of Virtual City Scene Based on Building Codes

For higher-level requirements of urban planning and management and the recent development of earth and city, it is urgent to establish protocols for the construction of three-dimensional digital city models. However, some problems still exist in the digital technology of the three-dimensional city model, such as insufficient precision of the three-dimensional model, not optimizing the scene and not considering the constraints of building codes. In view of those points, a method to interactively simulate a virtual city scene based on building codes is proposed in this paper. Firstly, some constraint functions are set up to restrict the models to adhere to the building codes, and an improved directional bounding box technique is utilized to solve the problem that geometric objects may intersect in a virtual city scene. The three-dimensional model invocation strategy is designed to convert two-dimensional layouts to a three-dimensional urban scene. A Leap Motion hardware device is used to interactively place the 3D models in a virtual scene. Finally, the design and construction of the three-dimensional scene are completed by using Unity3D. The experiment shows that this method can simulate urban virtual scenes that strictly adhere to building codes in a virtual scene of the city environment, but also provide information and decision-making functions for urban planning and management.

Evaluation of the landslide susceptibility map obtained by a GIS matrix method: a case of Al Hoceima city (northern Morocco)

This work presents a method for preparing landslide susceptibility maps based on an analysis between past movements and predisposing factors. The proposed methodology for determining susceptibility is an adaptation of the matrix method in the territory of the city of Al Hoceima (Northern Morocco). The data needed to evaluate vulnerability has based on the landslide inventory and the available cartographic documents. These data were prepared and integrated into a GIS. This phase of the data preparation has followed by assessment and susceptibility mapping. The obtained map was validated and compared with an independent landslide dataset than the one used to develop the model. The results of the ROC (receiver operating characteristic) curve demonstrate the good predictive ability (AUC = 0.94). We can deduce that the map correlates well with the existing terrain conditions. The successfully validated prediction will enable decision-makers to offer handy information for infrastructure construction and urban planning in the future or other areas with similar situations

Application of Land Use Change and Prediction in Urban Planning Evaluation and Formulation

The study monitored land use change in Nansha during 2000-2018, evaluate Nansha’s urban planning by the overlay analysis of land use general planning, land use distribution, and economic data, and predicted the urban expansion in 2025 and 2035 to provide scientific information for urban planning in the future. The results showed that the built-up area rapidly expanded (78.32 km2 to 153.75 km2 in 2000-2018), and the GDP and the newly-added enterprises remarkably increased as well. Most of built-up area matched the land use planning. The urban expansion was simulated to 168.27 km2 in 2025 and to 207.04 km2 in 2035 for future land use planning.

Photographic Reportage on the Rebuilding after the Irpinia-Basilicata 1980 Earthquake (Southern Italy)

This paper aims to present, through a photographic reportage, the current state of rebuilding of the most devastated villages by the earthquake that hit the Southern Italy on 23 November 1980, in Irpinia-Basilicata. The earthquake was characterized by magnitude Ml = 6.9 and epicentral intensity I0 = X MCS. It was felt throughout Italy with the epicenter in the Southern Apennines, between the regions of Campania and Basilicata that were the most damaged areas. About 800 localities were serious damaged; 7,500 houses were completely destroyed and 27,500 seriously damaged. The photographic survey has been done in 23 towns during the last five years: Castelnuovo di Conza, Conza della Campania, Laviano, Lioni, Santomenna, Sant’Angelo dei Lombardi, Balvano, Caposele, Calabritto and the hamlet of Quaglietta, San Mango sul Calore, San Michele di Serino, Pescopagano, Guardia dei Lombardi, Torella dei Lombardi, Colliano, Romagnano al Monte, Salvitelle, Senerchia, Teora, Bisaccia, Calitri and Avellino. Forty years after the 1980 earthquake, the photographs show villages almost completely rebuilt with modern techniques where reinforced concrete prevails. Only in few instances, the reconstruction was carried out trying to recover the pre-existing building heritage, without changing the original urban planning, or modifying it. We argue that this photography collection allows to assess the real understanding of the geological information for urban planning after a major destructive seismic event. Even more than this, documenting the rebuilding process in a large epicentral area reveals the human legacy to the natural landscape, and our ability, or failure, to properly interpret the environmental fate of a site.

Пространственные данные в градостроительной деятельности

In the view of the sections of the state information system for ensuring urban planning activities, the place is shown and the role in regulating the development of territories is revealed. The composition of spatial data contained in various types and sections of urban planning documentation is analyzed. A review of world practice has shown that many of the analyzed spatial data are included in national spatial data infrastructures, which indicates their high level of signifi cance. Consideration of the issues of updating graphic documents by maintaining duty plans of urban planning information using GIS technologies revealed the need to consolidate secondary (updated) documents at the legislative level. An analysis of the requirements for spatial data in the fi eld of urban planning and the real estate cadastre showed the need for their refi nement in terms of clarifying the requirements for the accuracy of describing the objects of urban planning activities and unifying the coordinate systems used. Proposals are given for the establishment of uniform requirements for methods for determining the coordinates of spatial objects and the rules for determining the permissible discrepancy, as well as requirements for the accuracy of determining the coordinates of the characteristic points of the boundaries of objects of urban planning activities.

The urban planning information platform: progressive methods of planning and managing the sustainable development of territories

The urban planning information platform, including the “digital twin” model, has become the basis for the integration of various urban planning and sector-wide approaches and a tool for progressive spatial planning, design, and management of the sustainable development of territories. The study resulted in the identification of several “smart urban planning” aspects as a promising direction in the development of the theory and practice of urban planning. The article gives a brief description of the modern global experience in digital forecasting, programming, and design of the spatial system. It discloses the provisions of strategic urban planning, modeling the graphic identity of a territory, and master planning taking into account digital information systems and information and communication technologies. The authors propose to separate the concepts of “territorial” and “spatial”, taking into account their differences and tasks. They determine perspective tasks in the field of the development of urban planning information systems and “digital twins” aimed at spatial planning and modeling. They consider the theoretical provisions of predictive probabilistic modeling; initiative, institutional and participatory planning, taking into account the interests of residents, business, scientific and public institutions, as well as the tasks of creating a comfortable, safe and landscaped living environment.

Smart urban planning: modern technologies for ensuring sustainable territorial development

The issues of improving the methodological foundations on urban planning based on the requirements of “smart” planning and managing sustainable development of territories are considered. The concepts of “smart urban planning”, “urban planning information platform”, “digital twin” of a territory and “cyber physical urban development system” are defined. A brief review of the world experience in adopting “smart” technologies for urban development is given. The paper sets the long-term problems associated with theoretical and practical development in the field. Finally, the authors propose the concept of “smart urban planning”, aimed at providing comfortable and safe living conditions. It provides fundamental principles of goal-setting, planning and adopting the sustainable territorial development using information and communication technologies; a theoretical model of the urban planning information platform based on automation methods, adaptation conditions, as well as describing the “digital twin” layers of an urban development object.

Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

P-LINKNET: LINKNET WITH SPATIAL PYRAMID POOLING FOR HIGH-RESOLUTION SATELLITE IMAGERY

Abstract. Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating. Although extensively studied in the past years, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing image is still a challenging task. Repeated pooling and striding operations used in CNNs reduce feature resolutions and cause the loss of detail information. In order to solve this problem, we proposed a deep learning model with a spatial pyramid pooling module based on the LinkNet. The proposed model called P-LinkNet that takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information. We tested it on Inria Building dataset. Experimental results show that the proposed P-LinkNet is superior to the LinkNet.

A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction

Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored.

Study on the limiting height of rooftop solar energy equipment in street canyons under the cityscape constraints

The random installation of various solar energy equipment on building roofs has caused visual damage to the cityscape. Keeping the rooftop equipment out of sight of the pedestrians is a new solution to eliminate visual pollution. Based on the complex urban street canyons, this paper describes a physical and mathematical model relating to the limiting height of equipment installations at any position on the roof. It is found that the limiting height of equipment varies with the building roof height, the street width, and other factors. In addition, for a case with four main urban roads, the variation patterns for the limiting height of rooftop equipment changed with the street width and the distance from the installation to the parapet have been established, which is of great relevance for determination of the limiting equipment height in actual installations. The research can not only help to remove visual pollution from rooftop equipment in street canyons, and provide a guide for the selection and installation of solar equipment, but can also provide significant information for urban space planning and building design.

Predicting urban cold-air paths using boosted regression trees

Ensuring the transport of colder air masses from rural surroundings or inner urban open spaces into the dense urban center is a vital approach for reducing nocturnal warming of urban areas in summer, i.e. the urban heat island (UHI) effect. Hence, cold-air paths are an important mitigation aspect in urban planning. The identification of cold-air paths by field measurements, however, is time-consuming and it is difficult to cover larger spatial domains (e.g. entire cities). Therefore, we used a machine learning technique, boosted regression trees (BRT), for estimating the spatial distribution of cold-air paths in three German cities (Braunschweig, Freiburg, Stuttgart). We identified the most important predictor variables for cold-air path occurrence and tested the spatial transferability of BRT models from one training city to the other study cities.Three variables explaining differences in surface elevation indicated strongest influence on spatial distribution of cold-air paths: relative surface elevation, topographic position index, and topographic wind index. We achieved predictions for the spatial distribution of cold-air paths for cities the model was trained on, resulting in model performances of 0.85 < AUC < 0.96 (AUC: area under the receiver operating characteristic curve). Model transfers to other cities, however, achieved only poor to moderate results (0.5 < AUC < 0.8). The city of Freiburg tended to be the most suitable basis for a general model, since it covers the largest range of conditions. Transfer works best for ‘similar’ cities, i.e. cities with a comparable range of predictor variables and morphological structure. Cold-air path identification via BRT modelling appeared to be a promising method delivering helpful information for urban planning.