共查询到20条相似文献，搜索用时 457 毫秒
1.
Yao Yao Zhaotang Liang Zehao Yuan Penghua Liu Yongpan Bie Jinbao Zhang 《International journal of geographical information science》2013,27(12):2363-2384
ABSTRACTThough global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments. 相似文献
2.
ABSTRACTTerrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. 相似文献
3.
Recently, the Chinese government raised an urban planning policy which suggests communities open their private roads for public transport and establish street networks with high spatial density in cities. In this context, the purpose of this study is to analyze the potential changes to street network accessibility using GIS techniques and to provide spatial information that may influence the decision making of urban managers. In addition to the existing street network data in the case study city, Shenzhen, intra-community roads are extracted from building footprints in GIS topographic database and used to construct a potential street network with respect to the community opening policy. An automatic GIS-based method is proposed here to analyze the location advantage information in the simulated urban environment, by combining Delaunay triangulation model and graph theory concepts. Specifically, we establish a two-step framework based on the spatial relationships between roads and buildings. Firstly, intra-community roads between neighboring building footprints are generated using a Delaunay triangulation skeleton model, and with the existing inter-community roads they form the simulated network. Secondly, street centrality indices of the current and simulated networks are compared in terms of closeness, betweenness and straightness. Results indicate that after applying the policy the global accessibility in the city would be increased at some places and decreased at others, and places' directness among others would be generally improved. In addition, the skeleton of central routes for through traffic would not change much. The presented method can also be applied to other cities. 相似文献
4.
LUO Luhua CHEN Mingjie DONG Lulu SU Wei LI Xin HU Xiaodong ZHANG Xin LI Chen CHENG Weiming SHI Hanning LUO Jiancheng 《资源与生态学报(英文版)》2022,13(2):247-256
With the expansion of a city, the urban green space is occupied and the urban heat island effect is serious. Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment. To provide effective data support for urban green space planning, this paper used high-resolution images to (1) obtain accurate building spots on the map of the study area through deep learning assisted manual correction; and (2) establish an evaluation index system of roof greening including the characteristics of the roof itself, the natural environment and the human society environment. The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process (AHP). The suitable green roof locations were evaluated by spatial join, weighted superposition and other spatial analysis methods. Taking the areas within the Chengdu city’s third ring road as the study area, the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment. The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction, with an accuracy of 86.58%. The roofs suitable for greening account for 48.08%, among which, the high-suitability roofs, medium-suitability roofs and low-suitability roofs represent 45.32%, 38.95% and 15.73%. The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu. This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment. 相似文献
5.
目前海岛经济快速发展,为避免海岛建筑无序扩建,了解海岛建筑分布特征尤为重要。机器学习方法是从高分遥感影像提取地物目标的常见方式,然而建筑物遥感特征复杂,机器学习方法出现鲁棒性差、难以充分挖掘深层次特征的弊端。文章提出基于DeepLabv3plus网络模型的深度学习语义分割方法提取海岛建筑,并对网络结构进行改进,使用组归一化（GN）方法替代批归一化（BN）以适合小batch size下的语义分割操作。针对海岛建筑数据量较少的问题,采用迁移学习策略,设计基于多源数据的国内城市建筑数据集的预训练样本智能采集和标注方法,再人工标注中国部分海岛建筑进行算法实验。结果表明,在batch size较小时,基于GN的DeepLabv3plus语义分割算法的平均精度和mIoU均得到提升,能够获得更为精确的像素级海岛建筑提取结果。 相似文献
6.
Michael Batty Yichun Xie 《International journal of geographical information science》2013,27(3):291-307
Abstract This is the first of two papers elaborating a framework for embedding urban models within GIS. This framework is based upon using the display capabilities of GIS as the user interface to the conventional modelling process, beginning with data selection and analysis, moving to model specification and calibration, and thence to prediction. In this paper, we outline how various stages in this process based on purpose-built software outside the system, are accessed and operated through the GIS. We first deal with display based on thematic maps, surfaces, graphs and linked windows, standard to any data from whatever source, be it observations, model estimates or predictions. We then describe how various datasets are selected, how the spatial system can be partitioned or aggregated, and how rudimentary exploratory spatial data analysis enables scatterplots to be associated with thematic maps. We illustrate all these functions and operations using the proprietary GIS ARC-INFO applied to population data at the tract level in the Buffalo region. In the second part of the paper, various residential location models are outlined and the full modelling framework is assembled and demonstrated. 相似文献
7.
8.
广州中心城区住宅租金差异的核心影响因素 总被引：1，自引：0，他引：1
构建并阐述城市住宅的特征租金理论框架,建立包括建筑特征、便利性特征、环境特征、区位特征在内的“四分法”特征租金模型。以2020年3月广州中心城区23126套待租住宅的挂牌月租金单价为基本数据,通过分级空间统计和空间自相关分析广州中心城区住宅租金的空间差异格局与空间关联性,构建4要素12个指标的广州中心城区住宅租金影响因素指标体系,通过3种模型比选,采用空间误差模型测度住宅租金的影响因素,并筛选核心影响因素。结果表明：① 在研究城市内部住宅租金影响因素时,可采用本文构建的特征租金理论框架及其特征租金模型;② 广州中心城区中低租金水平的住宅数量最多,住宅租金呈现核心区高,外围城区低的空间分异格局,具有显著的空间集聚和空间关联特征;③ 建筑特征（建筑面积、朝向与楼层、房龄、电梯与物业）、便利性特征（地铁便利性、办公便利性、基础教育便利性）、环境特征（公园可达性、工业污染影响）和区位特征（距市中心距离）共4个方面的10个因素对广州中心城区住宅租金差异有显著影响;④ 建筑面积、房龄和距市中心距离是住宅租金的3个最关键核心影响因素,电梯与物业、办公便利性也是核心影响因素。 相似文献
9.
基于居民行为周期特征的城市空间研究 总被引：4，自引：1，他引：3
伴随着中国经济社会进入“新常态”的发展阶段,对城市存量空间的研究提出了更加精细化的要求,基于居民行为活动的周期规律对城市空间进行研究,进而提升城市空间的品质日益重要。随着信息通信技术的快速发展,使许多大数据的获取成为可能,并由于其低成本、即时、大样本等优势,在城市空间研究方面具有巨大的价值。以上海市中心城区为例,利用手机信令数据,探究居民活动的空间周期变化特征,并基于空间的周期特征曲线,采用相似性传播聚类算法进行空间分类。研究表明,居民活动有平日一日周期和平日加周末二日周期,与人的作息规律相符合。市核心区、城市副中心及主要就业中心,昼夜波动和平日周末活动强度的差异都较为明显。空间分类结果显示,城市活动空间的组织既体现出个体充分的空间能动性,也反映出对土地使用类型以及设施建设、投入程度的耦合性。上海市内环内核心区混合多样的用地模式使得活动区内居民活动内容丰富,周期特征功能区边界模糊。研究成果可为未来的城市空间规划提供指导,为城市空间结构、功能布置、设施布局等优化提供决策支撑和科学依据。 相似文献
10.
基于“规模—密度—形态”的大连市城市韧性评估 总被引：14，自引：3，他引：11
以建设安全城市为目标,依据地理学和景观生态思想方法,构建基于“规模—密度—形态”的三维城市韧性研究框架,并对2000-2016年大连市各县市区的城市韧性进行评估。其中规模韧性利用生态基础设施工具进行度量,密度韧性利用生态足迹与生态承载力工具进行度量,形态韧性基于源汇景观平均距离指数进行度量。还对各年份三类韧性的组合形式进行综合评判。本文认为,“规模—密度—形态”三位一体的韧性评估方法可有效识别城市的韧性特征,是建立城市规划与城市韧性研究之间有效联系的纽带。研究发现：① 规模安全是城市空间扩张的基本约束条件;② 生态承载力是城市密度的安全阈值;③ “源—汇”景观的空间耦合是优良城市形态的基本特征;④ 是“规模—密度—形态”三个韧性的组合状况而不是某一单项指标决定城市的安全性。基于规模、密度、形态韧性及其组合特征判定,为未来大连的城市发展提出建议：① 严格限制中心城区与金州区开发强度,遏制其蔓延式增长趋势;② 严格控制海岸带开发,维持山体和绿色植被斑块的完整性;③ 促进市域均衡开发,提升城市整体韧性;④ 优化新市区开发战略,形成良好城市形态。 相似文献
11.
Oleksandr Kit 《Applied geography (Sevenoaks, England)》2012,32(2):660-667
This paper outlines a methodology to identify informal settlements out of high resolution satellite imagery using the concept of lacunarity. Principal component analysis and line detection algorithms were applied alternatively to obtain a high resolution binary representation of the city of Hyderabad, India and used to calculate lacunarity values over a 60 × 60 m grid. A number of ground truthing areas were used to classify the resulting datasets and to identify lacunarity ranges which are typical for settlement types that combine high density housing and small dwelling size - features characteristic for urban slums in India. It was discovered that the line detection algorithm is advantageous over principal component analysis in providing suitable binary datasets for lacunarity analysis as it is less sensitive to spectral variability within mosaicked imagery. The resulting slum location map constitutes an efficient tool in identifying particularly overcrowded areas of the city and can be used as a reliable source in vulnerability and resilience assessments at a later stage. The proposed methodology allows for rapid analysis and comparison of multi-temporal data and can be applied on many developing urban agglomerations around the world. 相似文献
12.
Jose Morales Alfred Stein Johannes Flacke Jaap Zevenbergen 《International journal of geographical information science》2020,34(7):1451-1474
ABSTRACT Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice. 相似文献
13.
Mahmoud Saeedimoghaddam 《International journal of geographical information science》2020,34(5):947-968
ABSTRACT Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. 相似文献
14.
城镇产业布局基础空间信息数据库系统的设计与实现 总被引：1，自引：0，他引：1
建立城镇产业布局基础空间信息数据库的目的是避免城镇基础空间数据集的重复采集,减少浪费,协调空间数据的使用,加强对信息资源有效而经济的管理。针对城镇产业布局分析中对基础数据的实际要求,综合利用地理信息系统技术、数据库技术和空间数据库引擎技术,设计了城镇产业布局基础空间信息数据库的系统框架结构和建库技术路线,并在此基础上建立了一套分布式城镇产业布局数据库原型系统。该系统能够对城镇产业布局分析相关的海量空间数据进行有效的组织、管理和应用,并预留了数据接口,可为相关的城镇产业布局分析软件提供数据支持服务。此外,该系统还可实现不同部门和用户的数据共享,为城镇建设决策提供数据支持。 相似文献
15.
Ziliang Zhao Yang Xu Feng Lu Jie Chen Ling Yin 《International journal of geographical information science》2016,30(9):1738-1762
ABSTRACTIn recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data. 相似文献
16.
《Urban geography》2013,34(7):953-972
Although water demand theories identify price structures, technology, and individual behavior as determinants of water demand, limited theoretical or empirical evidence suggests a link between urban development patterns and water use. To assess the role of urban development patterns on water demand, we used GIS and statistical models to analyze single-family residential water consumption in the Portland, Oregon, metropolitan area. Our results show that residential water consumption per household at the census block group scale is best explained by average building size, followed by building density and building age, with low water consumption areas clustering together and typically located in high-density and older neighborhoods. Accounting for spatial dependence among residuals, explanatory variables explain up to 87% of variations in water consumption. Our results help to develop a water demand framework that incorporates existing factors with urban development policies to more effectively manage limited water and land resources. 相似文献
17.
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting 总被引：1，自引：0，他引：1
Yang Zhang Tao Cheng Yibin Ren Kun Xie 《International journal of geographical information science》2020,34(5):969-995
ABSTRACT Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets. 相似文献
18.
3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database 总被引：1，自引：0，他引：1
Rujun Cao Xinyi Liu Zongze Zhao 《International journal of geographical information science》2017,31(7):1359-1380
Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance. 相似文献
19.
Jing Li George Taylor David Kidner Mark Ware 《International journal of geographical information science》2013,27(11-12):1197-1218
This paper explains a ray tracing method which is applied to prediction and visualization of diffracted and reflected GPS signals in dense urban areas. Reflected and diffracted signals can have a detrimental effect on GPS positioning accuracy especially in highly built‐up areas. The ray tracing technique implemented in this paper is specially geared to LiDAR height pole data at 1‐m spatial resolution and 2D building footprints in raster and vector format, respectively. Such a simple data format allows for rapid implementation of 3D ray tracing in a GIS without further processing so that detailed 3D urban models in vector format are not required. Issues of spatial uncertainty in the data used are also addressed in relation to the identification of multipath signals. Some preliminary results obtained from fieldwork are presented and analysed in detail. 相似文献