Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Assessing the Impact of Using Different Land Cover Classification in Regional Modeling Studies for the Manaus Area,Brazil

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

Urban planning construction land standard and its revision based on climate and topography in China

Urban planning construction land standard is the technical specification for scientifically allocating various types of urban construction land,and it is the basis for drawing up and revising the overall urban planning scheme.Considering China’s current urban planning construction land standard,many problems exist,such as the gap in the land use control threshold,the lack of regional differences in the climate revision,and failing to consider the topographic factors.To resolve these problems,this study proposed a step-by-step process framework and quantitative calculation method for the establishment and revision of standards in accordance with the principle of Total-Structure control.By setting the conditions,a universal basic standard for construction land was established.Quantitative analysis was then conducted on the relationship between the basic standard and the selected key indicators,such as urban population size,sunshine spacing coefficient,the width of river valleys or inter-montane basins,and terrain slope,among others.Finally,revised standards were formed for climate conditions,topography,and geomorphologic conditions,which were matched with the basic standards.The key results are three-fold:(1)The per capita construction land standard of 95 m~2/person can be used as the total indicator of China’s urban planning basic standard,and the corresponding per capita single construction land comprises 32.50%of residential land,7.42%of public management and public service land,22.50%of industrial land,17.50%of transportation facilities,12.50%of green space,and 7.58%of other land-use types.The results of the revision of the urban population size indicate that the difference in population size has little effect on the total amount of per capita construction land.(2)The climate revision results of per capita residential land and per capita construction land in major cities reveal that the revised climate value varies greatly between north and south China.The revised climate values of the per capita area of construction land vary by latitude as follows:the value at 20°N is 93 m^(2)/person,the value at 30°N is 97 m^(2)/person,the value at 40°N is 103 m^(2)/person,and the value at 50°N is 115 m^(2)/person.The basic standard land value of 95 m^(2)/person is generally distributed across the Xiamen-Guilin-Kunming line.(3)The cities located in mountainous areas,hilly valleys,or inter-montane basins can reduce the allocation of community parks and comprehensive parks when the average width of an existing river valley or inter-montane basin is less than 2 km.When the average width of the valley or inter-montane basin is between 2 km to 4 km,the allocation of the comprehensive parks can be reduced.The revised results of per capita sloping construction land reveal that the terrain slope greatly affects the revised value of per capita construction land.Specifically,the revised value at 3°is 3.68%higher than the basic standard value,and the increase rates at 8°,15°,and 25°are 11.25%,26.49%,and 68.47%,respectively.

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.

Integrating urban morphology and land surface temperature characteristics for urban functional area classification

The classification of urban functional areas plays an important role in urban planning and resource management.Although previous studies have confirmed that different urban func-tional areas have different morphological structures and Land Surface Temperature(LST)characteristics,these two types of characteristics have rarely been fully integrated and used for functional area classification.In this paper,a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features.First,metrics are constructed from three levels,namely,building,road and region,which are used to portray urban morphology;LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics:the average temperature,maximum temperature,temperature difference and standard deviation of temperature.Then,the functional areas are classified into four categories:service/public land,commercial land,residential land and industrial land.A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas.The effectiveness of the proposed framework is tested in the study area of Shenzhen City,Guangdong Province.The results show that the combined classification accuracy of the proposed classification method is 0.85,which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features.The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.

Automating land parcel classification for neighborhood-scale urban analysis

Homeowners’Associations(HOAs)dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA.Determining the location and spatial extent of HOAs is critical for examining its influence.However,such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis.The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification,which is an initial step towards the goal of determining the impact of HOAs on urban land management.Using Maricopa County,Arizona as a testbed,we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269,such that boundaries better align with neighborhood units to which rule sets like land covenants apply.Moreover,after an initial training period,this process was completed in just over 7 hours.This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and,eventually,nationally and determining proposed links between HOAs and land management outcomes.

Mapping essential urban land use categories(EULUC)using geospatial big data:Progress,challenges,and opportunities

Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning,land-scape design,environmental management,health promotion,and biodiversity conservation.Land-use maps outlining the distribution,pattern,and composition of essential urban land use categories(EULUC)have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies.New and improved Earth observations,algorithms,and advanced products for extracting thematic urban information,in association with emer-ging social sensing big data and auxiliary crowdsourcing datasets,all together offer great potentials to mapping fine-resolution EULUC from regional to global scales.Here we review the advances of EULUC mapping research and practices in terms of their data,methods,and applications.Based on the historical retrospect,we summarize the challenges and limitations of current EULUC studies regarding sample collection,mixed land use problem,data and model generalization,and large-scale mapping efforts.Finally,we propose and discuss future opportunities,including cross-scale mapping,optimal integration of multi-source features,global sam-ple libraries from crowdsourcing approaches,advanced machine learning and ensembled classification strategy,open portals for data visualization and sharing,multi-temporal mapping of EULUC change,and implications in urban environmental studies,to facil-itate multi-scale fine-resolution EULUC mapping research.

Land cover changes in the rural-urban interaction of Xi’an region using Landsat TM/ETM data

Landsat ETM/TM data and an artificial neural network （ANN） were applied to analyse the expansion of the city of Xi＇an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index （NDBI） were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km^2 in 2000 to 358.60 km^2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi＇an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of “returning farmland to woodland”, some farmland was converted into economic woodland, such as Chinese goosebeerv garden, vineyard etc.

Integrating structure and function:mapping the hierarchical spatial heterogeneity of urban landscapes

Background:Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity,which are closely related to myriad urban problems.However,the tools to map and quantify this heterogeneity are lacking.We here developed a new three-level classification scheme,by considering ecosystem types(level 1),urban function zones(level 2),and land cover elements(level 3),to map and quantify the hierarchical spatial heterogeneity of urban landscapes.Methods:We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics.We used a top-down classification procedure by conducting the classification in the order of ecosystem types,function zones,and land cover elements.The classification of the lower level was based on the results of the higher level.We used an objectbased methodology to carry out the three-level classification.Results:We found that the urban ecosystem type accounted for 45.3%of the land within the Shenzhen city administrative boundary.Within the urban ecosystem type,residential and industrial zones were the main zones,accounting for 38.4%and 33.8%,respectively.Tree canopy was the dominant element in Shenzhen city,accounting for 55.6%over all ecosystem types,which includes agricultural and forest.However,in the urban ecosystem type,the proportion of tree canopy was only 22.6%because most trees were distributed in the forest ecosystem type.The proportion of trees was 23.2% in industrial zones,2.2%higher than that in residential zones.That information“hidden”in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management.Conclusions:This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery,infrastructural data available at a municipal level,and object-based spatial analysis.For effective planning and management,the hierarchical levels of landscape classification(level 1),the analysis of use and cover by urban zones(level 2),and the fundamental elements of land cover(level 3),each exposes different respects relevant to city plans and management.

基于Landsat影像的合肥市地表温度空间特征分析

文章以合肥市为例,选择2008—2020年内5期Landsat影像,对包河区、蜀山区、庐阳区、瑶海区4个行政区进行地物分类与地表温度反演;采用地理加权回归(geographically weighted regression,GWR)模型与标准差椭圆(standard deviational ellipse,SDE)分别分析不透水层对地表温度的影响程度及其空间分布演化特征。在500 m格网化划分尺度下,4个行政区不透水层与地表温度的GWR全局拟合系数R^(2)基本上大于0.8,其中包河区与庐阳区的R^(2)随时间变化相对稳定,而蜀山区与瑶海区的R^(2)增加,不透水层变化显著;2008—2020年,不透水层对应SDE演化趋势与对应城区扩张趋势基本一致,椭圆面积呈增长趋势,这与合肥市由以主城区为主的发展模式变为多地区共同发展模式相关。研究结果可为合肥市未来城市规划提供参考。

POI辅助下的城市国土空间地类信息细化研究

为提升城市国土空间地类信息细化工作效率,将POI数据、国土变更调查数据与行业专题资料相结合,利用分类映射、数据匹配、空间叠置等关键技术,研究形成了一套POI辅助下的城市国土空间地类信息细化技术流程。通过搭建FME模板,实现了城镇住宅、医疗机构、学校等各细化地类的快速自动定位与信息细化补充,在一定程度上减少了人工工作量,提升了工作效率。以徐州市为例,统计了POI对于不同细化地类的利用率。结果表明,分类映射提取后的POI专题数据整体利用率较好;但由于不同数据标准的差异性,对于部分细化类型,POI数据需进一步结合其他数据来提升其可用性。

云南施甸土地质量地球化学空间分布特征

基于云南省施甸县1∶5万土地质量地球化学调查评价相关数据成果,分析了土壤pH、土壤养分、土壤环境质量、土壤质量、土地质量及一般富硒土等级划分标准及空间分布特征,并在调查区划定了一般富硒土地资源(Se平均值0.5mg/kg),面积233.32km 2,为发展富硒特色农业奠定了基础。

Assessment of Urban Morphology through Local Climate Zone Clas­sification and Detection of the Changing Building States of Siliguri Municipal Corporation and Its Surrounding Area, West Bengal

Progressive population concentration to the urban centres has fuelled urban expansion in both horizontal as well as vertical direction,consequences in the urban landscape change.This growth resulted in posing many complexities towards sustainable urban development which can be counted by observing the changing proportions of natural landscapes and built up areas.Local climate zones(LCZs),a systematic classification of natural lands and built up lands,are identified in Siliguri Municipal Corporation(SMC)and its surrounding region to explore the spatio temporal complexity of urban growth in recent years.Rapid urbanization and population growth of SMC have led to change the building states from low rise to mid and high rise which added an important feature to the urban landscape dynamics of the area.The work intends to provide the vision of spatial urban morphology of the area through investigation of its changing land use and changing urban built space using the LCZ classification.The study shows that the WUDAPT method can accurately generate LCZs,especially the built type LCZs.The results of the proposed LCZ classification scheme are tested using error matrix for the year 2001 and 2021 having coefficient values of 0.79 and 0.81 respectively.The study explores the changing pattern of building states of SMC using LCZ products,which is essential for proper urban planning implementations.

时空大数据支持的城镇开发边界划定研究--以长沙市为例

划定城镇开发边界是加强空间开发管制、控制城镇无序蔓延的重要措施;土地利用变化模拟模型是划定城镇开发边界的重要手段之一;现有相关研究大多基于土地利用数据与统计数值进行,对人类活动反映不足,尤其缺乏对商业、医疗、教育等经济活动的刻画。本研究以长沙市为研究区,采集兴趣点数据、签到数据、人口空间化数据等多源时空大数据,利用CLUMondo模型根据现状数据和土地利用需求开展长沙市未来土地利用变化模拟,划定城镇开发边界。结果表明:①土地利用模拟总体精度大于90%。②2035年长沙市中心城区共有建设用地1339 km^(2)。中心城区城镇开发边界面积为1207 km^(2),其中,城镇开发边界内建设用地1157 km^(2),占比为95.86%。③2020~2035年,研究区正东、西北两个方位保持较高扩展强度。正东方向集聚式扩展明显;西北方向沿湘江扩散式发展,延伸进入望城区内部。④研究区建设用地集聚效应明显,其开发边界划定结果与人口密度、商业活动强度有着较高的一致性。

多时相Landsat数据动态监测城市土地利用变化——以上海市为例

近年来,上海市由于经济的快速发展,城市用地急剧增加,利用多时相的遥感影像,可以动态地定量监测这种城市化的现象。该文采用平行六面体分类法、最小距离分类法、最大似然分类方法,对上海市2002年和2009年Landsat5遥感影像进行分类并对比,发现最大似然分类方法精度最高,在此基础上,分析利用ENVI获取的土地利用类型转移矩阵,可以发现,上海市土地利用发生重大变化,城市用地面积急剧增加,耕地面积快速减少。大量的耕地资源流向了城市用地,但其他用地很少转移到耕地,这种占用不可逆。上海市各区县城市用地的扩张不仅存在数量上的差异,同时还存在着空间上的差异。

城市轨道交通站点地区土地利用与交通协同性评价

为实现城市轨道交通与土地利用的高质量一体化发展,基于节点-场所模型,纳入功能指标,构建城市轨道交通站点地区土地利用与交通协调性评价指标体系,以评估站点地区轨道交通与土地利用的协调关系。在青岛市的实证研究中,以扩展模型3项价值得分为输入,采用层次K-Means聚类法将轨道交通站点划分为5类:从属型、场所滞后型、平衡发展型、节点滞后型和压力型。其中压力型和节点滞后型在各项指标上均表现出较高水平,主要分布在核心城区,但节点滞后型的节点价值低于平均水平;平衡发展型站点的指标价值适中,而场所滞后型的场所价值相对较低,表明该类站点地区的土地开发滞后于轨道交通建设;从属型站点主要分布在城市片区,各项指标普遍较低。通过分析典型站点,得到分类结果与实际情况高度一致,验证模型的有效性,也为其他城市轨道交通站点地区建设和土地开发提供理论参考。

Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System

The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia, which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20,448 (ha) whereas other land covers, such as green area, have decreased significantly by 14,554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.

都市快轨车站设计研究——以京雄快线典型车站设计为例

都市快轨是在我国都市圈发展背景下应运而生的轨道交通干线,以京雄快线为例,以探索可推广的都市快轨车站设计标准为目的,使其兼有铁路的快速通达和城轨的公交化运营两方面优势,并与城市空间格局和城市综合交通体系相融合。梳理传统铁路客运站与城轨车站的特点和差异,以因地制宜、各取所长作为设计导向,剖析京雄快线各站点在线网中的功能定位,分为城市重大交通节点、城市级、区域级,提出面对差异化的城市空间形态、开发强度做好建筑空间一体化。面对都市快轨快速通达的乘客诉求,采取公交化运营缩短旅行时间,提供高效便捷的换乘、接驳服务水平。面对列车高速越行产生的风压,设全高站台门隔绝轨行区与乘客候车区,提供舒适的候车环境,采用“桥建分离”的结构形式,并采取安全可靠的技术措施满足风压计算强度要求。面对都市圈轨道先行,圈内各区域发展的不确定性,在站型选择和总体布局中为都市轨道线路未来逐步成网预留好主支结合、不断生长、网络化运营的拓展条件。

基于三调的城镇内部建设用地细化调查方法研究

三调成果是规划和自然资源管理的底图。为更好地衔接并应用三调成果,本文分析了当前自然资源系统对国土地表利用现状和用途的分类体系及其特征,提出城镇内部建设用地细化调查分类标准基础上,研究提出了以管理数据为基础提取工作底图及城镇边界修正、路网构建、图斑细化、综合用途处理、外业调查、数据库及国土变更衔接方法,为自然资源管理精细化分类调查提供技术参考。