Urban Monitoring Research Articles

Deep learning and gradient boosting for urban environmental noise monitoring in smart cities

Every day the innovative IoT technology is expanding further and further in our environment, with applications deployed in various contexts including cities. Communities can indeed address problems linked to urbanization thanks to this technology through the Smart City concept and thus support a sustainable development of their cities. Artificial intelligence and namely its machine learning branch is expected to reinforce this trend by making smart cities even smarter. However, smart cities can only be successful if they can be trusted and, with this in mind, machine learning can potentially be an efficient tool to mitigate cyberattacks. This paper can be divided into two main parts. In the first part the ability of Gradient Boosting and Deep Learning to make long-term predictions of noise level is studied based on noise data collected in the suburb of an English city. In the second part, we proposed an approach for detecting noise levels anomalies based on predictions. Two types of injections were taken into consideration namely punctual noise level attacks and gradual noise level attacks. Specifically, for the punctual attacks, when the difference between the actual sensed and predicted noise levels is greater than a given threshold, we considered that there is an anomaly in the data. For the gradual attacks, we used a criterion comparing the mean absolute error of predictions in the attacked set of data to statistics of the absolute error in the training set. The obtained results show that our approach, which uses a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) hybrid network for the noise level prediction, can effectively be used to detect the aforementioned types of anomalies. In the case of punctual attacks an increase in sound intensity of 5 dB was detected, while for gradual attacks, smaller changes can be detected.

Classification of Complicated Urban Forest Acoustic Scenes with Deep Learning Models

The use of passive acoustic monitoring (PAM) can compensate for the shortcomings of traditional survey methods on spatial and temporal scales and achieve all-weather and wide-scale assessment and prediction of environmental dynamics. Assessing the impact of human activities on biodiversity by analyzing the characteristics of acoustic scenes in the environment is a frontier hotspot in urban forestry. However, with the accumulation of monitoring data, the selection and parameter setting of the deep learning model greatly affect the content and efficiency of sound scene classification. This study compared and evaluated the performance of different deep learning models for acoustic scene classification based on the recorded sound data from Guangzhou urban forest. There are seven categories of acoustic scenes for classification: human sound, insect sound, bird sound, bird–human sound, insect–human sound, bird–insect sound, and silence. A dataset containing seven acoustic scenes was constructed, with 1000 samples for each scene. The requirements of the deep learning models on the training data volume and training epochs in the acoustic scene classification were evaluated through several sets of comparison experiments, and it was found that the models were able to achieve satisfactory accuracy when the training sample data volume for a single category was 600 and the training epochs were 100. To evaluate the generalization performance of different models to new data, a small test dataset was constructed, and multiple trained models were used to make predictions on the test dataset. All experimental results showed that the DenseNet_BC_34 model performs best among the comparison models, with an overall accuracy of 93.81% for the seven acoustic scenes on the validation dataset. This study provides practical experience for the application of deep learning techniques in urban sound monitoring and provides new perspectives and technical support for further exploring the relationship between human activities and biodiversity.

Air Quality at Ponta Delgada City (Azores) Is Unaffected so Far by Growing Cruise Ship Transit in Recent Years

The ease of travel allowed by contemporary means of long-range transportation has brought increasingly higher numbers of visitors to remote and relatively undisturbed insular territories. In a framework of environmental conservation of the natural patrimony, sustainably accommodating touristic flooding and the associated polluting footprint poses a demanding challenge. Over the past decade, Ponta Delgada, the largest city of the mid-Atlantic Azores archipelago, has become a hotspot for transatlantic cruise ship (CS) lines in spring and autumn. CSs are substantial contributors to the emissions of greenhouse gases (GHG), as well as hazardous sulphur (SOx) and nitrogen (NOx) oxides. It is hereby retrospectively investigated whether the background levels of air pollutants were raised following the CS influx at Ponta Delgada, which conventionally displays great air quality. The daily CS traffic at the local harbor was associated with the daily concentrations of air pollutants (SO2, O3, NO2, NOx, PM10 and PM2.5), monitored by the local urban background monitoring station (~1.3 km northwards). Exceedances above daily legislated limits from 2013 until 2020 only occurred sporadically for PM10 and PM2.5, often during episodes of natural dust storms. No major correlation was found between CS parameters with the recorded values of pollutants, although a noticeable signal of NOX increase of southern origin is observed during spring days with CS presence. Daily data suggest CS influx has not strongly influenced background air quality. A near-source, real-time monitoring network should be implemented in the city to provide the necessary spatial and temporal resolution for tracking short-term fluctuations in air pollutants during CS arrivals and departures.

Assessment of street forest characteristics in four African cities using google street view measurement: Potentials and implications

Accurate information on urban forests of tree sizes, health state, community structures, and spatial distribution is still limited in African cities. Using a Google Street View (GSV)-based tree-size measuring method developed by our team, this paper aims to evaluate street trees of four African metropolitan cities using GSV data. The study compiled a large dataset with 46,016 street trees in 3454 sites in Kampala, Nairobi, Bloemfontein, and Johannesburg. The data including tree size (diameter at breast height, DBH; tree height, TH; underbranch height, UBH; canopy size), tree floristic composition (apical dominance types, broadleaf-conifer-palm leaf, flowering or not), tree health (leaf color, diebacks, dead tree, and bracket-supporting percent), streetside development (lane number, roadside shops, parking vehicle, and pedestrian density), and geolocation (latitude, longitude). These data can be spatially visualized with the help of ArcGIS, and the large dataset favors reliable maps from the street-view level. Data statistics showed that four cities were dominated by broad-leaved, apical dominance, and flowering trees, with a low level of unhealthy leaves and a tiny percentage of dead. The arbor-shrubs-herb structure vegetation dominated all four cities. Kampala had the most slender trees (DBH = 23 cm, TH = 8.4 m), while Nairobi and Johannesburg had the thickest trees (DBH = 38 cm, TH = 8.5–8.6 m). Bare land rates were lowest at 23% in Bloemfontein and highest at 33% in Nairobi. Principal analysis and Pearson correlations showed that these tree variations were closely associated with street development and local land use configuration. By comparing the urban tree data in other regions of the world, we found that the trees in African cities are generally giant but have a lower density (the trees within a 100-m street segment). Our findings emphasized that GSV data is feasible enough for urban forest monitoring in Africa, and the database is helpful for urban landscape planning and management.

Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment.

An Acoustic Camera for Use on UAVs.

Airborne acoustic surveillance would enable and ease several applications, including security surveillance, urban and industrial noise monitoring, rescue missions, and wildlife monitoring. Airborne surveillance with an acoustic camera mounted on an airship would provide the deployment flexibility and utility required by these applications. Nevertheless, and problematically for these applications, there is not a single acoustic camera mounted on an airship yet. We make significant advances towards solving this problem by designing and constructing an acoustic camera for direct mounting on the hull of a UAV airship. The camera consists of 64 microphones, a central processing unit, and software for data acquisition and processing dedicatedly developed for far-field low-level acoustic signal detection. We demonstrate a large-aperture mock-up camera operation on the ground, although all preparations have been made to integrate the camera onto an airship. The camera has an aperture of 2 m and has been designed for surveillance from a height up to 300 m, with a spatial resolution of 12 m.

Remote sensing and geostatistics in urban water-resource monitoring: a review

Context At present, many cities are facing severe water-resources problems caused by urbanisation. With the development of remote sensing and geostatistics, they have been widely used in urban water-resource monitoring. Aims To review and summarise the application of remote sensing and geostatistics in monitoring urban water resources and prospect for their furtherdevelopment. Methods First, bibliometrics was used to analyse the existing literature in this field. We then discuss the use of remote sensing and geostatistics to improve urban water-resources monitoring capacity, focusing on the classification of technologies and equipment and their applications in urban surface-water and urban groundwater monitoring. Finally, a look at the future research direction is taken. Conclusions In the past decade, the relevant research has shown an upward trend. The use of remote sensing and geostatistics can improve the city’s water-resource monitoring capacity, thereby promoting better use of water resources in cities. Implications In the future, with the development and addition of deep learning, remote-sensing and geographic-analysis systems can be used to conduct remote-sensing monitoring and data analysis on urban water resources more accurately, intelligently, and quickly, and improve the status of urban water resources.

РАЗВИТИЕ ПЕТЕРБУРГСКОЙ СИСТЕМЫ ПРОФИЛЬНЫХ КЛАССОВ ПСИХОЛОГО-ПЕДАГОГИЧЕСКОЙ НАПРАВЛЕННОСТИ: ПЕРСПЕКТИВЫ И РИСКИ ПРОЦЕССА

Currently, a large number of specialized classes with a psychological and pedagogical orientation are appearing in Russia, which indicates the active development of this area of pre-professional training for schoolchildren. The pedagogical ideology of their creation provides the possibility of achieving a variety of social and educational effects, the implementation of various models of their organization. The article analyzes the current situation of the development of the regional network of specialized classes of psychological and pedagogical orientation in St. Petersburg. Based on statistical data from urban monitoring and research data on the perception of the potential of pedagogical classes by St. Petersburg teachers the problem analysis of risks and necessary prospects for their further development in the region is carried out. The article can be useful both for representatives of the movement of psychological and pedagogical classes from Russian regions, and for teachers working in them, from the point of view of a comparative analysis of the situation.

Use of Low-Power Unmanned Aerial Vehicles for Control of an Urbanized Area

The article presents an analysis of the current classification of unmanned aerial systems according to the European Aviation Safety Agency related to the use of unmanned aerial vehicles for urban area monitoring. The possible tasks solved by unmanned aerial vehicles in an urbanized area were investigated. An analysis of the crisis events - floods, fires and landslides in the Pleven region of the Republic of Bulgaria for the period from 2010 to 2020 was made and a model for the use of unmanned aircraft was proposed.

Intelligent traffic safety cloud supervision system based on Internet of vehicles technology

<abstract> <p>In view of the poor supervision effect of the traditional monitoring cloud supervision system, this paper puts forward a design method of Intelligent Transportation Security Cloud supervision system based on the Internet of vehicles technology, uses the tsed-01 sensor chip to optimize the hardware configuration of the cloud supervision system, perfects the software functions based on the Internet of vehicles technology, and relies on the Internet of vehicles communication platform and cloud data sharing equipment to optimize the software functions of the cloud supervision system, identify and manage the heterogeneous data sources generated by different modules in the cloud supervision system to simplify the steps of the cloud supervision system and provide data support for the comprehensive decision-making of traffic management. The experimental results show that the intelligent traffic safety cloud supervision system based on the Internet of vehicles technology has good practicability, and has guiding significance for the construction of urban rail transit monitoring cloud supervision systems in the future.</p> </abstract>

Towards urban drainage sediment accumulation monitoring using temperature sensors

An innovative technique based on temperature measurements promises to better understand and manage sediments in urban drainage systems.

Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network

Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach

Urban environment monitoring in industrial city using remote sensing of snow cover

The dynamic development of modern cities requires new solutions to urban planning and management by local regional authorities. The paper focuses on ecological indicators based on Earth Remote Sensing Data (ERSD) of the snow cover with the purpose to evaluate the city and to plan ecological environment protection strategy. The paper deals with the method of using space images to assess the snow cover pollution of Chelyabinsk, a large Russian industrial city. The assessment of the snow cover of Chelyabinsk was carried out by comparing the heavy metals concentrations with the Landsat 8 data. The spectral indices were calculated for fourteen sites evenly distributed over the urban area of four types: courtyards, car parks, industrial zones and roads. We found a statistically significant difference between the Swirl/Green index and the site type and a correlation with the concentrations of dissolved and suspended forms of heavy metals in snow cover. Snow cover indices can be used as ecological indicators of urban environment.

Building Extraction From Very High-Resolution Remote Sensing Images Using Refine-UNet

Accurate building extraction from very high-resolution (VHR) remote sensing images plays an important role in urban dynamic monitoring, planning, and management. However, it is still a challenging task to achieve building extraction with high accuracy and integrity due to diverse building appearances and more complex ground background in VHR remote sensing images. Recently, Unity Networking (UNet) has been proven to be capable of feature extraction and semantic segmentation of remote sensing images. However, UNet cannot achieve sufficient multi-scale and multi-level features with larger receptive fields. To address these problems, an improved network based on UNet structure (Refine-UNet) is proposed for extracting buildings from the VHR images. The proposed Refine-UNet mainly consists of encoder module, decoder module, and refine skip connection scheme. The refine skip connection scheme is composed of an atrous spatial convolutional pyramid pooling (ASPP) module and several improved depthwise separable convolution (IDSC) modules. Experimental results on the Jilin-1 VHR datasets with a spatial resolution of 0.75 m demonstrate that compared with UNet, PSPNet, DeepLabV3+, and SegNet, the proposed Refine-UNet can obtain more accurate building extraction results and achieve the best Precision of 95.1% and intersection over union (IoU) of 87.0%, respectively, indicating the great practical potential.

Monitoring Land Use/Land Cover Change (LULCC) Using Remote Sensing

Land use/land cover change (LULCC) is crucial for social and economic systems. For detecting LULCC, remote sensing technology has become crucial. This paper illustrated the use of remote sensing to monitor LULCC, with particular emphasis on urban planning and the ecological environment which is currently in the news, and the limitations of remote sensing. Remote sensing performs well in urban land cover change, urban growth analysis and urban heat island monitoring, giving these areas a effective way to solve problems. In ecological environment monitoring, it develops some new methods combined with computer science in vegetation monitoring. Meanwhile, more and more researchers focus on the wetland monitoring and ecosystem service evaluation. Remote sensing is anticipated to support the monitoring and study of LULCC in the future. This paper provides an overview of current study and developments in the application of remote sensing to track LULCC.

Mining association rules between lichens and air quality to support urban air quality monitoring in Nigeria

Urban environments represent the most intense human-environment interaction. This interaction can result in negative outcomes like air pollution and its health implications. There is a significant data deficit in air quality monitoring across many developing nations, which prevents effective policies and measures from being taken to promote the accomplishment of sustainable development. Around the world, lichens have been used to track environmental changes due to their sensitivity to changes and concentration of atmospheric pollutants. This study investigated the relationships between lichen and air quality across some Nigerian cities. Lichen surveys were conducted in four cities. At various periods during the day, NO2, SO2, PM2.5, and PM10 levels were measured. Association rule mining was carried out to investigate the relationship between lichen found and air quality categories. Results showed that the most prevalent lichen Genera are Pyxine in Abuja and Kano, Diorygma in Lagos, and Dirinaria in Port Harcourt. Out of the 40 rules found from the rule mining, 17 are important (lift values ≥ 1.1), capturing six of the fourteen lichen genera identified in the field. The findings indicated that there are important relationships between lichens and air quality indices, suggesting that some lichen species in Nigeria may serve as indicators of long-term air quality. To develop a network of urban environmental quality bioindicators across Nigerian cities, surveying and transplanting are advised. The use of lichen for air quality monitoring can provide information for sustainable management of air quality and environmental quality in Nigeria.

Research on Intelligent Monitoring of Urban Environment under the Environment of the Internet of Things

With the development of the economy, human society has gradually strengthened the emphasis on environmental protection. Environmental monitoring, early warning, and governance have become important worldwide tasks. Traditional urban environmental monitoring relies on environmental monitoring stations. Manual data collection, processing, and analysis often consume a lot of manpower, and at the same time, efficiency and accuracy are lacking. The Internet of Things and sensors enable people to carry out unmanned collection, transmission, and analysis. This paper discusses the research on intelligent monitoring of the urban environment under the Internet of Things, designs the scheme from the software and hardware perspective, and proposes a time series data prediction method based on the Gaussian process. It is hoped that this article can provide some references for applying the collected massive environmental data.

Thermal Infrared Radiance Transfer Modeling of the Urban Landscape at Ultrahigh Spatial Resolution

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultra-high spatial resolution (1 cm to 1 m), and the model needs to be improved. This paper proposed a new ultra-high spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultra-high spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with the results of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects, and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape.

Monitoring of Land Use Change in Shijiazhuang City Based on Remote Sensing Data

With the rapid development of cities, urban land use monitoring has become the focus of increasing attention. In order to monitor the change of land use in Shijiazhuang City, the Sentinel-2 remote sensing data images from 2019 to 2022 were selected, processed by supervised classification and analyzed for dynamic changes. The result shows: the area of building land has shown an increasing trend, and the proportion of cultivated land occupied by urban expansion is the highest, and the change is most obvious in the city center; forest area increased; rivers show a tendency to expand, and the rivers represented by the Hutuo River have the most obvious changes. During the four-year period, the center of gravity of the city has changed, and the center of gravity of the city will move to the southwest in 2019-2020; the center of gravity of the city moves to the north from 2019 to 2022. Relevant land use changes can provide important references for urban development planning.

Construction of a System for Dynamic Monitoring of Pollutant Emissions by Transport Flows

The aim of this work is to resolve the urgent problem of building an urban monitoring system which monitors in real time emissions of pollutants from automobile traffic flows. Analysis of the initial data showed a statistically significant division of traffic lanes into homogeneous clusters. A comparative analysis of pollutant emissions was carried out for the three selected clusters with regard to two typical types of traffic at the intersection: free passage without stopping; and uniform acceleration after a complete stop at the stop line. This approach will significantly reduce the load on the computing units and practically implement a dynamic emission monitoring system based on the processing of data from street surveillance cameras.