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 noti...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.展开更多
Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe a...Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.展开更多
Studying the impact of urbanization on agricultural development in shrinking areas is important for maintaining food security and promoted agricultural development in China.Based on the measurement results of the shri...Studying the impact of urbanization on agricultural development in shrinking areas is important for maintaining food security and promoted agricultural development in China.Based on the measurement results of the shrinking cities in the three provinces of Northeast China,this paper selects 15 shrinking cities as the research object,and constructs a multi-dimensional index system to explore the impact of the urbanization level of the shrinking areas on the agricultural development in the region since 2007–2019,analyzes the influencing factors and their differences by using the geographically-weighted regression model and Geodetector,and proposes a targeted regulation strategy.The results show that:1)overall,there is a negative correlation between the urbanization level and the agricultural development level in the contracted areas of the three northeastern provinces.The urbanization level in these areas has a certain negative impact on the overall level of agricultural development;2)regarding the time dimension,the impact of urbanization level on the agricultural development level in the contracted areas of the three northeastern provinces gradually increases over time;3)regarding the spatial pattern,the overall impact of shrinking urbanization levels in the three provinces of Northeast China on the agricultural development shows a significant distribution pattern of high in the east and low in the west;4)the total population and natural population growth rate at the end of the year were the main factors influencing a certain level of urbanization on agricultural development in the shrinking cities while population density and the urban fixed asset investment rate were the secondary factors;and 5)the main reasons why the level of agricultural development in different cities was affected by the level of urbanization were different.However,they can be categorized into areas of population loss and spatial construction,which can be further divided into area of population loss in the northeast,areas of negative population growth in the west,and areas of urban spatial change in the south.According to the causes of the impact,this paper adopted targeted regulation strategies and formulated relevant policies and solutions that cater to local conditions.展开更多
Background: Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be use...Background: Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be used to assess the potential impact of inundation by a proposed hydroelectricity dam in the Mokihinui gorge, New Zealand, on representation of natural forests. Specifically we ask: 1) How well are the types of forest represented Locally, regionally, and nationally; and 2) How does the number of distinct communities (i.e. beta diversity) in the target catchment compare with other catchments nationally? Methods: For local and regional comparisons plant species composition was recorded on 45 objectively located 400 m2 vegetation plots established in each of three gorges, with one being the proposed inundation area of the Mokihinui lower gorge. The fuzzy classification framework of noise clustering was used to assign these plots to a specific alliance and association of a pre-existing national-scale classification. NationaLly, we examined the relationship between the number of alliances and associations in a catchment and either catchment size or the number of plots per catchment by fitting Generalised Additive Models. Results: The four alliances and five associations that were observed in the Mokihinui lower gorge arepresent in the region but limited locally. One association was narrowly distributed nationally, but is the mostfrequent association in the Mokihinui lower gorge; inundation may have consequences of national importance to its long-term persistence. That the Mokihinui lower gorge area had nearly twice as many plots that could not be assigned to pre- existing alliances and associations than either the Mokihinui upper or the Karamea lower gorges and proportionally more than the national dataset emphasises the compositional distinctiveness of this gorge. These outlier plots in the Mokihinui lower gorge may be unsorted assemblages of species or reflect sampling bias or that native- dominated woody riparian vegetation is rare on the landscape. At a national scale, the Mokihinui catchment has a higher diversity of forest alliances and associations (i.e. beta-diversity) than predicted based on catchment size and sampling intensity. Conclusions: Our analytical approach demonstrates one transparent solution to a common conservation planning problem: assessing how well ecosystems that will be destroyed by a proposed land-use change are represented using a multi-scale spatial and compositional framework. We provide a useful tool for assessing potential consequences of land-use change that can help guide decision making.展开更多
Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use...Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use planning and regulation. However, land-use classification requires highly trained, complex learning algorithms for accurate classification. Current machine learning techniques already exist to provide accurate image recognition. This research paper develops an image-based land-use classifier using transfer learning with a pre-trained ResNet-18 convolutional neural network. Variations of the resulting approach were compared to show a direct relationship between training dataset size and epoch length to accuracy. Experiment results show that transfer learning is an effective way to create models to classify satellite images of land-use with a predictive performance. This approach would be beneficial to the monitoring and predicting of urban development patterns, management of water and other natural resources, and land-use planning.展开更多
This study was undertaken to construct a preliminary spatial analysis method for building an urban-suburban-rural category in the specific sample area of central California and providing distribution characteristics i...This study was undertaken to construct a preliminary spatial analysis method for building an urban-suburban-rural category in the specific sample area of central California and providing distribution characteristics in each category, based on which, some further studies such as regional manners of residential wood burning emission (PM2.5, the term used for a mixture of solid particles and liquid droplets found in the air, refers to particulate matter that is 2.5μm or smaller in size) could be carried out for the project of residential wood combustion. Demographic and infrastructure data with spatial characteristics were processed by integrating both Geographic Information System (GIS) and statistics method (Cluster Analysis), and then output to a category map as the result. It approached the quantitative and multi-variables description on the major characteristics variations among the urban, suburban and rural; and perfected the TIGER’s urban-rural classification scheme by adding suburban category. Based on the free public GIS data, the spatial analysis method provides an easy and ideal tool for geographic researchers, environmental planners, urban/regional planners and administrators to delineate different categories of regional function on the specific locations and dig out spatial distribution information they wanted. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.展开更多
Changes in transport are likely to preduce changes in land use, and these long-term effects of transport policy may be of considerable potential importance. There is a growing movement, "The New Urbanism", w...Changes in transport are likely to preduce changes in land use, and these long-term effects of transport policy may be of considerable potential importance. There is a growing movement, "The New Urbanism", which seeks to reconnect transport with land use and in particular to eslablish transitoriented development where higher-density,mixed-use areas are built around high-quality transit systems. Based on analysis on development and Pattern of urban transport inGuangzhou, this paper researches composition of urban transportation and struclure of travel pattern. The urban transport system development and change in urban form as well as change in land use are closely related. The urban transport, system required and promnoted by the high-density land-use pattern. There are many problems in the urban transportation and land-use, one of the resolving is integration of urban transport planning and land-use planning.展开更多
This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two ...This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.展开更多
This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades...This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.展开更多
Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management.Therefore,it is necessary to first classify emergency responses to such a...Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management.Therefore,it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly.The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas.This study used a multilevel hierarchical structural model to determine emergency-response classification.In the model,accident attributes,urban road network vulnerability,and institutional resilience were used as classification criteria.Each evaluation indicator was selected according to importance ranking and independence screening and was given an interpretation and a quantitative criterion.The Fuzzy Delphi Method was used to rank the importance of the evaluation indices and the combined weight of each index was calculated using the G1 method.Finally,the case of a fatal traffic accident was used to validate the model.The results showed that the multilevel hierarchical structural model,Fuzzy Delphi Method,and G1 method can effectively address the problem of emergency-response classification.Because of its simplicity and adaptability,the approach presented here could be useful for decisionmakers and practitioners for determining emergency-response classifications.展开更多
The hidden dimension of the urban morphology is the underlying the urban morphological rules system.The number of these rules has increased and their application tends to become more complex.The urban morphosis based ...The hidden dimension of the urban morphology is the underlying the urban morphological rules system.The number of these rules has increased and their application tends to become more complex.The urban morphosis based digital approaches tends to become widespread.However,achieving the target values for all the rules is difficult.This impacts the social,environmental and aesthetic objectives of these rules.This paper proposes a classification of urban morphological rules to assist the digital morphosis of urban form.The aim is to endow the system of rules with a hierarchy,which can make efficient the automatic generation of the urban forms respectful of the urban law.Thus,this work promotes the concerns of artificial intelligence in urban morphology.展开更多
The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aer...The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.展开更多
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-c...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.展开更多
Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use,topography and human activity on soil classification changes in the Toshan wa...Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use,topography and human activity on soil classification changes in the Toshan watershed in northern Iran.Seven representative pedons derived from loess parent materials were studied on different land-uses and topographic positions. The studied pedons in forest(FO) on backslopes and footslope were classified as Calcic Haploxeralfs and Typic Haploxeralfs, respectively. The soils in abandoned lands(AB) and orchards(OR), where formerly under natural forests, located on the shoulder and backslopes positions were classified as Calcic Haploxeralfs and Vertic Haploxeralfs, respectively.Well-developed argillic horizons as indicators for higher degrees of soil evolution were observed in more-stable areas under the natural forest or less disturbed areas. Clay lessivage through these soil profiles have led to formation of Typic or Calcic Haploxeralfs, while under croplands(CP) were classified as Typic Calcixerepts. Conversion of sloping deforested areas to CP along with inappropriate management have accelerated soil erosion, resulting in unstable conditions in which decalcification and formation of developed soils cannot occur. Paddy cultivation in flat areas has caused to reduced conditions and formation of Typic Haplaquepts.Because of unfavorable conditions for chemical weathering(e.g. lower water retention compared to more-stable areas) no vermiculite was detected in the CP. The results showed that evolution and classification of the studied soils were strongly affected by land-use type, topography and management.展开更多
Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergon...Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.展开更多
Represents the first attempt to classify all of China’s295 cities in terms of industrial functions,using 1984 data.Within the framework of economic base theory of urban development,three elements are defined as speci...Represents the first attempt to classify all of China’s295 cities in terms of industrial functions,using 1984 data.Within the framework of economic base theory of urban development,three elements are defined as specialized branch,functional intensity and functional scale.The method used here is based on a combination of the three elements.A number of techniques tried made it possible to base the classification on a composite measure,consisting of the Ward’s Error Method of hierarchical cluster analysis and a supplementary application of Nelson measure.The 295 cities have been grouped into three categories with 19 subcategories and 54 functional groups.The distribution of cities in most of the subcategories are displayed on 8 maps.展开更多
Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling ur...Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control,planning of the soundscape environment,or selection of safe living space.In this paper,we proposed a self-attention long short-term memory(LSTM)method that can improve sound classification compared to previous baselines.An attention mechanism will be designed solely to capture the key section of an audio data series.This is practical as we only need to process important parts of the data and can ignore the rest,making it applicable when gathering information with long-term dependencies.The dataset used is the Urbansound8k dataset which specifically pertains to urban environments and data augmentation was applied to overcome imbalanced data and dataset scarcity.All audio sources in the dataset were normalized to mono signals.From the dataset above,an experiment was conducted to confirm the suitability of the proposed model when applied to the mel-spectrogram and MFCC(Mel-Frequency Cepstral Coefficients)datasets transformed from the original dataset.Improving the classification accuracy depends on the machine learning models as well as the input data,therefore we have evaluated different class models and extraction methods to find the best performing.By combining data augmentation techniques and various extraction methods,our classification model has achieved state-of-the-art performance,each class accuracy is up to 98%.展开更多
With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regio...With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regional heat island(RHI) with a larger range of impact to the regional environment. However, there are few studies on the heat island effect of urban agglomerations in arid and semiarid regions, so this paper selects the urban agglomeration of Hohhot, Baotou and Ordos(HBO) of Inner Mongolia, China as the study area. Based on the 8-day composite Moderate-resolution Imaging Spectroradiometer(MODIS) surface temperature data(156scenes in all) and land use maps for 2005, 2010, and 2015, we analyze the spatiotemporal distributions of regional heat(cool) islands(RH(C)I) and the responses of surface temperatures to land-use changes in the diurnal and interannual surface cities. The results showed that: 1) from 2005 to 2015, urban areas showed the cold island effect during the day, with the area of the cold island showing a shrinking feature;at night, they showed the heat island effect, with the area of the heat island showing a first decrease and then an increase.2) From 2005 to 2015, the land development(unutilized land to building land) brings the greatest temperature increase(ΔT = 1.36°C)during the day, while the greatest temperature change at night corresponds to the conversion of cultivated land to building land(ΔT =0.78°C) exhibited the largest changes at night. From 2010 to 2015, the land development(grassland to building land) bring the greatest temperature increase(ΔT = 0.85°C) during the day, while the great temperature change at night corresponds to the conversion of water areas to building land(ΔT = 1.38°C) exhibited the largest changes at night. Exploring the spatial and temporal evolution of surface urban heat(cool) islands in urban agglomerations in arid and semiarid regions will help to understand the urbanization characteristics of urban agglomerations and provide a reference for the formulation of policies for the coordinated and healthy development of the region and co-governance of regional environmental problems.展开更多
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘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.
基金funded by the project,“Design and implementation of real-time safety ensuring system in the indoor environment by applying machine learning techniques”.IRN:AP14971555.
文摘Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.
基金Under the auspices of Natural Science Foundation of Heilongjiang(No.JJ2023LH0720)Philosophy and Social Sciences Research Program of Heilongjiang(No.21JLE323)Social Service Capacity Improvement Project of Harbin Normal University in 2022(No.1305123124)。
文摘Studying the impact of urbanization on agricultural development in shrinking areas is important for maintaining food security and promoted agricultural development in China.Based on the measurement results of the shrinking cities in the three provinces of Northeast China,this paper selects 15 shrinking cities as the research object,and constructs a multi-dimensional index system to explore the impact of the urbanization level of the shrinking areas on the agricultural development in the region since 2007–2019,analyzes the influencing factors and their differences by using the geographically-weighted regression model and Geodetector,and proposes a targeted regulation strategy.The results show that:1)overall,there is a negative correlation between the urbanization level and the agricultural development level in the contracted areas of the three northeastern provinces.The urbanization level in these areas has a certain negative impact on the overall level of agricultural development;2)regarding the time dimension,the impact of urbanization level on the agricultural development level in the contracted areas of the three northeastern provinces gradually increases over time;3)regarding the spatial pattern,the overall impact of shrinking urbanization levels in the three provinces of Northeast China on the agricultural development shows a significant distribution pattern of high in the east and low in the west;4)the total population and natural population growth rate at the end of the year were the main factors influencing a certain level of urbanization on agricultural development in the shrinking cities while population density and the urban fixed asset investment rate were the secondary factors;and 5)the main reasons why the level of agricultural development in different cities was affected by the level of urbanization were different.However,they can be categorized into areas of population loss and spatial construction,which can be further divided into area of population loss in the northeast,areas of negative population growth in the west,and areas of urban spatial change in the south.According to the causes of the impact,this paper adopted targeted regulation strategies and formulated relevant policies and solutions that cater to local conditions.
基金funded by Meridian Energy Limited,New Zealandby Core funding for Crown Research Institutes from the New Zealand Ministry of Business,Innovation and Employment’s Science and Innovation Group
文摘Background: Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be used to assess the potential impact of inundation by a proposed hydroelectricity dam in the Mokihinui gorge, New Zealand, on representation of natural forests. Specifically we ask: 1) How well are the types of forest represented Locally, regionally, and nationally; and 2) How does the number of distinct communities (i.e. beta diversity) in the target catchment compare with other catchments nationally? Methods: For local and regional comparisons plant species composition was recorded on 45 objectively located 400 m2 vegetation plots established in each of three gorges, with one being the proposed inundation area of the Mokihinui lower gorge. The fuzzy classification framework of noise clustering was used to assign these plots to a specific alliance and association of a pre-existing national-scale classification. NationaLly, we examined the relationship between the number of alliances and associations in a catchment and either catchment size or the number of plots per catchment by fitting Generalised Additive Models. Results: The four alliances and five associations that were observed in the Mokihinui lower gorge arepresent in the region but limited locally. One association was narrowly distributed nationally, but is the mostfrequent association in the Mokihinui lower gorge; inundation may have consequences of national importance to its long-term persistence. That the Mokihinui lower gorge area had nearly twice as many plots that could not be assigned to pre- existing alliances and associations than either the Mokihinui upper or the Karamea lower gorges and proportionally more than the national dataset emphasises the compositional distinctiveness of this gorge. These outlier plots in the Mokihinui lower gorge may be unsorted assemblages of species or reflect sampling bias or that native- dominated woody riparian vegetation is rare on the landscape. At a national scale, the Mokihinui catchment has a higher diversity of forest alliances and associations (i.e. beta-diversity) than predicted based on catchment size and sampling intensity. Conclusions: Our analytical approach demonstrates one transparent solution to a common conservation planning problem: assessing how well ecosystems that will be destroyed by a proposed land-use change are represented using a multi-scale spatial and compositional framework. We provide a useful tool for assessing potential consequences of land-use change that can help guide decision making.
文摘Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use planning and regulation. However, land-use classification requires highly trained, complex learning algorithms for accurate classification. Current machine learning techniques already exist to provide accurate image recognition. This research paper develops an image-based land-use classifier using transfer learning with a pre-trained ResNet-18 convolutional neural network. Variations of the resulting approach were compared to show a direct relationship between training dataset size and epoch length to accuracy. Experiment results show that transfer learning is an effective way to create models to classify satellite images of land-use with a predictive performance. This approach would be beneficial to the monitoring and predicting of urban development patterns, management of water and other natural resources, and land-use planning.
文摘This study was undertaken to construct a preliminary spatial analysis method for building an urban-suburban-rural category in the specific sample area of central California and providing distribution characteristics in each category, based on which, some further studies such as regional manners of residential wood burning emission (PM2.5, the term used for a mixture of solid particles and liquid droplets found in the air, refers to particulate matter that is 2.5μm or smaller in size) could be carried out for the project of residential wood combustion. Demographic and infrastructure data with spatial characteristics were processed by integrating both Geographic Information System (GIS) and statistics method (Cluster Analysis), and then output to a category map as the result. It approached the quantitative and multi-variables description on the major characteristics variations among the urban, suburban and rural; and perfected the TIGER’s urban-rural classification scheme by adding suburban category. Based on the free public GIS data, the spatial analysis method provides an easy and ideal tool for geographic researchers, environmental planners, urban/regional planners and administrators to delineate different categories of regional function on the specific locations and dig out spatial distribution information they wanted. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.
文摘Changes in transport are likely to preduce changes in land use, and these long-term effects of transport policy may be of considerable potential importance. There is a growing movement, "The New Urbanism", which seeks to reconnect transport with land use and in particular to eslablish transitoriented development where higher-density,mixed-use areas are built around high-quality transit systems. Based on analysis on development and Pattern of urban transport inGuangzhou, this paper researches composition of urban transportation and struclure of travel pattern. The urban transport system development and change in urban form as well as change in land use are closely related. The urban transport, system required and promnoted by the high-density land-use pattern. There are many problems in the urban transportation and land-use, one of the resolving is integration of urban transport planning and land-use planning.
文摘This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.
文摘This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.
基金supported by the Fifth 333 High-Level Talents Project of Jiangsu Province under Grant BRA2017443the Key Research Base of Jiangsu University Philosophy and Social Science under Grant 2018ZDJD-B007.
文摘Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management.Therefore,it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly.The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas.This study used a multilevel hierarchical structural model to determine emergency-response classification.In the model,accident attributes,urban road network vulnerability,and institutional resilience were used as classification criteria.Each evaluation indicator was selected according to importance ranking and independence screening and was given an interpretation and a quantitative criterion.The Fuzzy Delphi Method was used to rank the importance of the evaluation indices and the combined weight of each index was calculated using the G1 method.Finally,the case of a fatal traffic accident was used to validate the model.The results showed that the multilevel hierarchical structural model,Fuzzy Delphi Method,and G1 method can effectively address the problem of emergency-response classification.Because of its simplicity and adaptability,the approach presented here could be useful for decisionmakers and practitioners for determining emergency-response classifications.
文摘The hidden dimension of the urban morphology is the underlying the urban morphological rules system.The number of these rules has increased and their application tends to become more complex.The urban morphosis based digital approaches tends to become widespread.However,achieving the target values for all the rules is difficult.This impacts the social,environmental and aesthetic objectives of these rules.This paper proposes a classification of urban morphological rules to assist the digital morphosis of urban form.The aim is to endow the system of rules with a hierarchy,which can make efficient the automatic generation of the urban forms respectful of the urban law.Thus,this work promotes the concerns of artificial intelligence in urban morphology.
基金supported by Joint Fund of Natural Science Foundation of Zhejiang-Qingshanhu Science and Technology City(Grant No.LQY18C160002)National Natural Science Foundation of China(Grant No.U1809208)+1 种基金Zhejiang Science and Technology Key R&D Program Funded Project(Grant No.2018C02013)Natural Science Foundation of Zhejiang Province(Grant No.LQ20F020005).
文摘The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.
文摘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.
基金a part of the Ph D thesis in Department of Soil Science, University of Tehran, Iran (Grant No. 7104017/6/23)
文摘Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use,topography and human activity on soil classification changes in the Toshan watershed in northern Iran.Seven representative pedons derived from loess parent materials were studied on different land-uses and topographic positions. The studied pedons in forest(FO) on backslopes and footslope were classified as Calcic Haploxeralfs and Typic Haploxeralfs, respectively. The soils in abandoned lands(AB) and orchards(OR), where formerly under natural forests, located on the shoulder and backslopes positions were classified as Calcic Haploxeralfs and Vertic Haploxeralfs, respectively.Well-developed argillic horizons as indicators for higher degrees of soil evolution were observed in more-stable areas under the natural forest or less disturbed areas. Clay lessivage through these soil profiles have led to formation of Typic or Calcic Haploxeralfs, while under croplands(CP) were classified as Typic Calcixerepts. Conversion of sloping deforested areas to CP along with inappropriate management have accelerated soil erosion, resulting in unstable conditions in which decalcification and formation of developed soils cannot occur. Paddy cultivation in flat areas has caused to reduced conditions and formation of Typic Haplaquepts.Because of unfavorable conditions for chemical weathering(e.g. lower water retention compared to more-stable areas) no vermiculite was detected in the CP. The results showed that evolution and classification of the studied soils were strongly affected by land-use type, topography and management.
文摘Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.
文摘Represents the first attempt to classify all of China’s295 cities in terms of industrial functions,using 1984 data.Within the framework of economic base theory of urban development,three elements are defined as specialized branch,functional intensity and functional scale.The method used here is based on a combination of the three elements.A number of techniques tried made it possible to base the classification on a composite measure,consisting of the Ward’s Error Method of hierarchical cluster analysis and a supplementary application of Nelson measure.The 295 cities have been grouped into three categories with 19 subcategories and 54 functional groups.The distribution of cities in most of the subcategories are displayed on 8 maps.
文摘Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control,planning of the soundscape environment,or selection of safe living space.In this paper,we proposed a self-attention long short-term memory(LSTM)method that can improve sound classification compared to previous baselines.An attention mechanism will be designed solely to capture the key section of an audio data series.This is practical as we only need to process important parts of the data and can ignore the rest,making it applicable when gathering information with long-term dependencies.The dataset used is the Urbansound8k dataset which specifically pertains to urban environments and data augmentation was applied to overcome imbalanced data and dataset scarcity.All audio sources in the dataset were normalized to mono signals.From the dataset above,an experiment was conducted to confirm the suitability of the proposed model when applied to the mel-spectrogram and MFCC(Mel-Frequency Cepstral Coefficients)datasets transformed from the original dataset.Improving the classification accuracy depends on the machine learning models as well as the input data,therefore we have evaluated different class models and extraction methods to find the best performing.By combining data augmentation techniques and various extraction methods,our classification model has achieved state-of-the-art performance,each class accuracy is up to 98%.
文摘With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regional heat island(RHI) with a larger range of impact to the regional environment. However, there are few studies on the heat island effect of urban agglomerations in arid and semiarid regions, so this paper selects the urban agglomeration of Hohhot, Baotou and Ordos(HBO) of Inner Mongolia, China as the study area. Based on the 8-day composite Moderate-resolution Imaging Spectroradiometer(MODIS) surface temperature data(156scenes in all) and land use maps for 2005, 2010, and 2015, we analyze the spatiotemporal distributions of regional heat(cool) islands(RH(C)I) and the responses of surface temperatures to land-use changes in the diurnal and interannual surface cities. The results showed that: 1) from 2005 to 2015, urban areas showed the cold island effect during the day, with the area of the cold island showing a shrinking feature;at night, they showed the heat island effect, with the area of the heat island showing a first decrease and then an increase.2) From 2005 to 2015, the land development(unutilized land to building land) brings the greatest temperature increase(ΔT = 1.36°C)during the day, while the greatest temperature change at night corresponds to the conversion of cultivated land to building land(ΔT =0.78°C) exhibited the largest changes at night. From 2010 to 2015, the land development(grassland to building land) bring the greatest temperature increase(ΔT = 0.85°C) during the day, while the great temperature change at night corresponds to the conversion of water areas to building land(ΔT = 1.38°C) exhibited the largest changes at night. Exploring the spatial and temporal evolution of surface urban heat(cool) islands in urban agglomerations in arid and semiarid regions will help to understand the urbanization characteristics of urban agglomerations and provide a reference for the formulation of policies for the coordinated and healthy development of the region and co-governance of regional environmental problems.