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Graphic representation of the confusion matrix of the classification. Rows represent reference class and collums show classified data.
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Land-use classification of urban environments is usually limited by the number and complexity of the considered classes and the capability of the selected methodology for the efficient discrimination of these classes. Thus, this paper analyses and assesses the performance of a contextual object-based classification methodology in urban environments...
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... the urban classes, the lowest accuracies and the most unbalanced values were obtained for classes commercial buildings and religious buildings. The stu confusion matrix -graphically represented in Figure 5. - shows that commercial buildings had a poor performance and presented several misclassifications with industrial/warehouse and public buildings classes. ...
Citations
... Airborne LiDAR is widely used in land cover classification [4], marine resource detection [5,6], and vegetation detection [7] because of its high measuring efficiency, wide working range, high measuring accuracy, and ability to obtain the physical characteristics of the target. Because 532 nm blue-green light has good water permeability, laser radar at this wavelength is used to measure the depth of the seafloor. ...
Multi-channel airborne full-waveform LiDAR is widely used for high-precision underwater depth measurement. However, the signal quality of full-waveform data is unstable due to the influence of background light, dark current noise, and the complex transmission process. Therefore, we propose a nonlocal encoder block (NLEB) based on spatial dilated convolution to optimize the feature extraction of adjacent frames. On this basis, a coupled denoising encoder–decoder network is proposed that takes advantage of the echo correlation in deep-water and shallow-water channels. Firstly, full waveforms from different channels are stacked together to form a two-dimensional tensor and input into the proposed network. Then, NLEB is used to extract local and nonlocal features from the 2D tensor. After fusing the features of the two channels, the reconstructed denoised data can be obtained by upsampling with a fully connected layer and deconvolution layer. Based on the measured data set, we constructed a noise–noisier data set, on which several denoising algorithms were compared. The results show that the proposed method improves the stability of denoising by using the inter-channel and multi-frame data correlation.
... Particularly, many related studies in the field of remote sensing have been conducted by exploring the relationship between LST and vegetation coverage ratio/spatial pattern, fabricating 2D/3D morphology characteristics [38][39][40]. This can be combined with aerial images and LiDAR to track LUCC and obtain 3D information (such as building height, which is a fundamental factor in UHI) [41]. However, a major flaw lies in the fact that LST obtained from remote sensing is not a direct parameter signifying thermal sensation. ...
Rapid urbanization has led to drastic land-use/cover changes (LUCCs) and urban heat islands (UHIs), negatively altering the urban climate and air quality. LUCC’s significant impacts on human health and energy consumption have inspired researchers to develop nature-based solutions to mitigate UHIs and improve air quality. However, integrating GIS-CFD modeling for urban heat mitigation towards climate change adaptation was largely neglected for eco-sustainable urban design in rapidly urbanizing areas. In this study, (1) long-term LUCC and meteorological analysis were conducted in the Wuhan metropolitan area from 1980 to 2016; (2) to mitigate the adverse effects of LUCC under a speedy development process, the role and relevance of optimizing building morphology and urban block configuration were discussed; (3) and particular design attention in strategy towards climate change adaptation for environmental performance improvement was paid in Wuhan’s fast-growing zones. The results show that UHII in 1980 was less severe than in 2016. Air temperature (Ta) increased by 0.4 °C on average per decade in developing areas. This increases the severity of UHII in urban fringes. It is found obligatory for a nature-based design to adopt urban morphology indicators (UMIs) such as average building height (μBH), sky view factors (ψSVF), and building density (BD/λp = % of built area) towards these changes. Further, on-site measurement revealed that λp is the most effective indicator for increasing urban heat around the buildings and boosting UHII. Using UMIs and a combined three-in-one regulation strategy based on μBH of common building types of high-rise (BHA), mid-rise (BHB), and low-rise (BHC) buildings can effectively contribute to regulating Ta and air movement within block configuration. As a result of this study’s strategy, urban heat is mitigated via reinforcing wind in order to adapt to climate change, which impacts the quality of life directly in developing areas.
... In order to know the air quality at a specific time, the environmental protection department has set up monitoring stations with equipment that can automatically monitor air quality [6]. However, each station requires expensive construction costs and a large number of human resources to maintain them regularly, and the construction may be limited by urban landuse strategies [7,8]. As a result, the number of air quality monitoring stations is often regressor composed of a feedforward neural network and LSTM for air quality fine-grained analysis. ...
Due to the limited number of air quality monitoring stations, the data collected are limited. Using supervised learning for air quality fine-grained analysis, that is used to predict the air quality index (AQI) of the locations without air quality monitoring stations, may lead to overfitting in that the models have superior performance on the training set but perform poorly on the validation and testing set. In order to avoid this problem in supervised learning, the most effective solution is to increase the amount of data, but in this study, this is not realistic. Fortunately, semi-supervised learning can obtain knowledge from unlabeled samples, thus solving the problem caused by insufficient training samples. Therefore, a co-training semi-supervised learning method combining the K-nearest neighbors (KNN) algorithm and deep neural network (DNN) is proposed, named KNN-DNN, which makes full use of unlabeled samples to improve the model performance for fine-grained air quality analysis. Temperature, humidity, the concentrations of pollutants and source type are used as input variables, and the KNN algorithm and DNN model are used as learners. For each learner, the labeled data are used as the initial training set to model the relationship between the input variables and the AQI. In the iterative process, by labeling the unlabeled samples, a pseudo-sample with the highest confidence is selected to expand the training set. The proposed model is evaluated on a real dataset collected by monitoring stations from 1 February to 30 April 2018 over a region between 118° E–118°53′ E and 39°45′ N–39°89′ N. Practical application shows that the proposed model has a significant effect on the fine-grained analysis of air quality. The coefficient of determination between the predicted value and the true value is 0.97, which is better than other models.
... It is carried out through regional spatial plans, spatial plans for special-purpose are-as, and urban plans. Therefore, there is a prerequisite to developing land-use strategies as an instrument to combine disciplinary knowledge with information on climate change, political change, and environmental factors [40][41][42][43]. ...
The overuse of natural resources by humanity in recent decades has resulted in noticeable changes environment quality. Global environmental research is particularly interested in the topics of land use change and land cover. The Republic of Serbia has a diverse spectrum of landforms, with agricultural use taking up the largest portions, followed by forestry, water, and building land. Significant anthropogenic pressures (such as mining, deforestation, urbanization, and uncontrolled land use, among other things) have harmed Serbia's natural resources over the past two decades. This study examines the causes of specific trends in land-use change in Serbia, utilizing the CORINE Land Cover (CLC) database to track temporal and spatial changes in the major categories of land use and land cover from 1990 to 2018. The authors explained that focusing on the rational use of natural resources is the only way to promote sustainable development, legal alignment with EU law, and prompt adoption of harmonized laws and planning documents across all sectors.
... For example, the urban spatial structure, which includes the characteristics of urban streets, road widths, and heights and threedimensional forms of buildings [53], may affect STE by changing the ventilation conditions and thus the heat exchange process of the city. Finally, only the composition of land types was regarded without considering the configuration and function of land [22,25,54], which requires more accurate and effective classification methods in the identification of urban land types in future works [55]. ...
Surface thermal environment (STE) is closely related to the comfort and health of residents, affecting regional livability, and its spatial and temporal changes are deeply affected by the urbanization process. Considering there is a lack of effective comparative analysis on STE in different urbanized inhabited islands, the special geographical unit and vital human settlement environment, long-term spatiotemporal characteristics and impact factor quantitative analyses were performed in two inhabited islands via the RS and GIS methods. The results suggest that the surface heat amplitude of the highly urbanized Xiamen Island decreases, with the surface heat intensity continuing to increase from 2000 to 2020, while that of the lowly urbanized Kinmen Island is reversed. Although the land surface temperature (LST) of the two inhabited islands shows similar spatial distribution characteristics with evident cold/hot spots, the geographical distribution characteristics of high LST zones are significantly different, and the thermal landscape of Xiamen Island is more fragmented, discrete, and simple in shape, as revealed by the landscape metrics. We demonstrate that the area proportion between cooling land (water body and greenland) and warming land (bare land and impervious surface) is the most influential factor of LST in the two islands while the marine environment is a unique contributor to STE of inhabited islands compared with inland cities, where the seawater around the island can reduce LST over a range of distances, and the influence of elevation on LST is mostly indirect. These results provide a scientific basis and case support for understanding the STE situation of inhabited islands with different urbanization levels.
... Geospatial resources provided by EnviroAtlas provide open access to indicators based on EO data and allow for assessment at multiple extents and resolutions which are critical to broadly addressing national to subnational SDG goals and targets. Hermosilla et al. [8] analyze and assess the performance of a contextual object-based classification methodology and demonstrate its high potential to correctly and accurately discriminate and assign agricultural land use classes. The tested object-based classification methodology could be used to increase the frequency, efficiency, and detail level of urban studies. ...
Earth Observation (EO) is used to monitor and assess the status of, and changes in, the natural and manmade environment via remote sensing technologies, usually involving satellites carrying imaging devices. EO applications provide important inputs to governments in planning, implementing, and monitoring the progress of the 2030 Agenda for Sustainable Development. Along with other countries, Bulgaria has committed to all 17 Sustainable Development Goals (SDGs) and reflected them in its strategic documents. EO is one of the priority technologies for the development of the Bulgarian space sector. This paper analyzes how EO data could significantly help Bulgarian authorities in achieving and monitoring the progress of the SDG targets based on three specific EO monitoring pilot projects’ results (showcases) focused more on the policy management approach than scientific achievement. The first project showed the opportunities of EO data for integration of a national (local) geospatial database with the existing international networks for monitoring natural disasters and accidents. The second demonstrated the time series usage of EO data for water quality monitoring. The third project integrated remote sensing data from EO and in situ measurements with ancillaries’ data to provide phenology status and crop production forecast in a common geospatial database with the aim to support the Bulgarian agriculture sector modernization.
... Several authors have used LiDAR as the sole source to distinguish between vegetation types or species [62,63]. While information about the shape of the tree is important, in functional vegetation mapping, LiDAR is mainly used to discriminate between various types of vegetation based on height (e.g., [50,87]). Besides LiDAR technology, height information can also be derived from stereoscopic imagery [20,50]. ...
... Zhou et al. [88] included density-related features to capture the spatial structure of neighboring tree species and found it to be beneficial for defuzzifying an initial fuzzy classification based on high-resolution aerial imagery. Contextual features can also be used for the semantic mapping of functional vegetation types, where the plant configuration or the specific embedding of a vegetated area in the urban context plays an important role [30,87,110]. For example, Wen et al. [110] were able to distinguish between park, roadside and residential-institutional trees by taking the relation between trees within an area of predefined size into consideration. ...
Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.
... To obtain spatially upscaled panchromatic data, it seems rational first to teach some models (such as linear regressions, non-linear kernel regressions, random forest models, etc.) to associate between downscaled (to the resolution of~742 m) ISS-provided RGB data and actual panchromatic data, and afterward to apply the best-performing model to predict panchromatic lights from the levels of actual RGB data (of~20 m resolution). Alternatively, or in addition to, land-use data of fine spatial resolution, obtained from aerial imagery and LiDAR data (see for example [46][47][48]), might be used as an additional predictor for better discriminating between different lamp types. It is important to note that, given the good-performing models for all lamp types, a detailed spectrum might be easily restored for each pixel for the whole study area: for this sake, we should sum up the spectra of corresponding lamp types, weighted for their relative contributions. ...
Artificial night-time light (NTL), emitted by various on-ground human activities, has become intensive in many regions worldwide. Its adverse effects on human and ecosystem health crucially depend on the light spectrum, making the remote discrimination between different lamp types a highly important task. However, such studies remain extremely limited, and none of them exploit freely available satellite imagery. In the present analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, we match two data sources: (i) the radiometrically calibrated RGB image provided by the ISS (coarse spectral resolution data), and (ii) a set of in situ measurements with detailed spectral signatures conducted by ourselves (fine spectral resolution data). First, we analyze the fine spectral resolution data: using spectral signatures of standard lamp types from the LICA UCM library as endmembers, we perform an unmixing analysis upon NTL in situ measurements; by this, we obtain the estimates for relative contributions of the standard lamp types in each examined in situ measurement. Afterward, we focus on the coarse spectral resolution data: by using various types of statistical models, we predict the estimated relative contributions of each lamp type via RGB characteristics of spatially corresponding pixels of the ISS image. The built models predict sufficiently well (with R2 reaching ~0.87) the contributions of two standard lamp types: high-pressure sodium (HPS) and metal-halide (MH) lamps, the most widespread lamp types in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport.
... For many applications, the recognition of built-up structures is of major interest (e.g., [8,9]). Besides optical aerial or satellite imagery, LiDAR images (e.g., [10][11][12]) are also used. Varol et al. [13] combine LiDAR images and stereo KOMPSAT-3 data to detect illegal buildings in a part of Istanbul. ...
This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal "patterns of life" in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.
... Follow-up work will further classify relocation into various types according to the built-up area and the AHF level around the station. For example, light detection and ranging (LIDAR) allows for extracting really accurate 3D data on urbanization in the study areas, with great advantages in a very dynamic environment with fast changes related to rapid urban sprawl, especially for obtaining the accurate the classification of LULC [52]. Therefore, LIDAR techniques should be applied for investigating the impacts of urbanization on observational environmental of meteorological stations in the future. ...
Rapid increases in urban sprawl affect the observational environment around meteorological stations by changing the land use/land cover (LULC) and the anthropogenic heat flux (AHF). Based on remote sensing images and GIS technology, we investigated the impact of changes in both LULC and AHF induced by urbanization on the meteorological observational environment in the Yangtze River Delta (YRD) during 2000–2018. Our results show that the observational environments around meteorological stations were significantly affected by the rapid expansion of built-up areas and the subsequent increase in the AHF, with a clear spatiotemporal variability. A positive correlation was observed between the proportion of built-up areas and the AHF around meteorological stations. The AHF was in the order urban stations > suburban stations > rural stations, but the increases in the AHF were greater around suburban and rural stations than around urban stations. Some meteorological stations need to be relocated to address the adverse effects induced by urbanization. The proportion of built-up areas and AHF around the new stations decreased significantly after relocation, weakening the urban heat island effect on the meteorological observations and substantially improving the observational environment. As a result, the observed daily mean temperature (relative humidity) decreased (increased) around the new stations after relocation. Our study comprehensively shows the impact of rapid urban sprawl on the observational environment around meteorological stations by assessing changes in both LULC and the AHF induced by urbanization. These findings provide scientific insights for the selection and construction of networks of meteorological stations and are therefore helpful in scientifically evaluating and correcting the impact of rapid urban sprawl on meteorological observations.
