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Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research. [ABSTRACT FROM AUTHOR]

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications. [ABSTRACT FROM AUTHOR]

An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images. [ABSTRACT FROM AUTHOR]

With the intensification of urbanization in human society, pollen allergy has become a seasonal epidemic disease with a considerable incidence rate, seriously affecting the healthy life of residents. Accurately classifying and recognizing major allergenic pollens for effective pollen monitoring and forecasting is of great practical significance for improving urban livability and citizens' quality of life. With the development of deep learning, automatic classification gradually replaces the process of manually recognizing pollen grains. Recently, Swin Transformer (SwinT) has demonstrated strong competitiveness in various tasks. In order to solve the problem of low resolution and complex background information of pollen images, we propose a novel classification framework titled Swin Transformer with Image Super-resolution Reconstruction Network (SwinT-SRNet) for pollen images classification. In the proposed SwinT-SRNet network, an image super-resolution reconstruction method based on the Efficient Super-resolution Transformer (ESRT) is designed to eliminate the blurring problem that arises when resizing low-resolution images to fit the training dimensions of the SwinT model. Furthermore, a high-frequency (HF) information extraction module is proposed to capture high-frequency information in images to provide richer information for the SwinT-SRNet classification network. Extensive experimental evaluations on a self-constructed allergic pollen dataset (POLLEN8BJ) in Beijing, China, as well as a public pollen dataset POLLEN20L-det, show that the SwinT-SRNet model achieves remarkable accuracies of 99.46% and 98.98%. Notably, even without pre-training weights, the model achieved 98.57% and 98.31% accuracy on the POLLEN8BJ and POLLEN20L-det datasets, which are 1.05% and 1.19% higher than SwinT, respectively. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization in China, the country's growing population brings great challenges to urban development. By mastering the refined population spatial distribution in administrative units, the quantity and agglomeration of population distribution can be estimated and visualized. It will provide a basis for a more rational urban planning. This paper takes Beijing as the research area and uses a new Luojia1–01 nighttime light image with high resolution, land use type data, Points of Interest (POI) data, and other data to construct the population spatial index system, establishing the index weight based on the principal component analysis. The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018. Then the population spatial distribution was visualize using GIS technology. After accuracy assessments by comparing the result with the WorldPop data, the accuracy has reached 0.74. The proposed method was validated as a qualified method to generate population spatial maps. By contrast of local areas, Luojia 1–01 data is more suitable for population distribution estimation than the NPP/VIIRS (Net Primary Productivity/Visible infrared Imaging Radiometer) nighttime light data. More geospatial big data and mathematical models can be combined to create more accurate population maps in the future. [ABSTRACT FROM AUTHOR]

Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas. [ABSTRACT FROM AUTHOR]

• Beijing has a rich resources of the potential green roofs (PGRs), accounting for 19.6% of the study area. • More than 10% of the runoff can be reduced by fully implementing of the PGRs in the north central downtown. • 13 of the 20 flooding underpasses would not have happened in the 7.21 Rainstorm, if only 20% of the PGRs had been greened. Although the study of hydrological performance of green roofs is of great significance to urban water security and sustainable development, large-scale roofs greening within a city range has not received sufficient attention. In this study, a typical urban area of 675 km2 within the fifth ring road of Beijing was chosen as a case study. The potential green roofs were defined before being identified from high resolution images. Under three retrofitting scenarios (100%, 50% and 20%) of extensive green roofs, the hydrological behaviour of potential green roofs was investigated at catchment units by employing the Soil Conservation Service Curve Number model. The behaviour was evaluated finally through two aspects of the runoff reduction effect and the flooding mitigation effect. According to this study, a 19.6% of the potential green roofs within the study area indicates a strong potential for the future greening. In the city scale, the average runoff reduction rates decrease from 9.38% to 6.13% with the increasing return period of rainfall events. Catchments in the core of northern city play key roles in reducing runoff, by more than 10% with the 100% implementation scenario. This study also showed that flooding in 13 of the 20 underpasses would have been avoided in the 7.21 Rainstorm if only 20% of the potential green roofs had been implemented. This study demonstrated a potentially positive hydrological behaviour contributed by roof greening in the future Beijing at a city scale, and effective runoff reduction can be achieved if only with a 50% greening of the potential green roofs. However, urban planners should take notice of the predicament that the deposit of high potential green roofs do not match spatially well to the severe flooding sites. [ABSTRACT FROM AUTHOR]

Abstract: In order to obtain higher resolution for neutron radiography with thicker organic materials, neutron radiography with several hundred keV neutrons has been investigated. The experiments were carried out at the 4.5 MV Van de Graaff at Peking University. The 7Li(p, n) 7Be reaction was chosen to produce several hundred keV neutrons. A thin 6Li-based neutron converter was utilized in the experiment. The results showed that radiography with several hundred keV neutrons has greater penetration than thermal neutron radiography. [Copyright &y& Elsevier]

Most serious geotechnical problems related to rock engineering are generated by the existence of faults. Detection of fault (especially active faults) is the first step to conduct any further in-depth studies in faulted rock mass or rock blocks. In this paper, we investigated the plausibility to image fault zones by passive seismic surveys using recorded ambient noise. The test site is in the north suburb of Beijing, northern China along the south bank of the northwest-southeast running Wenyuhe River, where the NNE-striking Huangzhuang-Gaoliying fault (HGF, active in the Holocene) is crossing. In general, the results are in good agreement with previous studies using active source seismics conducted in this area along the HGF and existing borehole drillings in the vicinity. Undoubtedly, Active source seismic reflection technique is the best technique to image fault in depth with high resolution. The drawback is its high cost and the necessity of using explosives, a practice usually prohibited in urban settings. In contrast, passive seismic surveys take the advantage of the ever-existing ambient noise source, and provide a low-cost technique, and fast deployment that can locate the fault effectively. Though the imaging resolution is lower compared to those obtained by active source seismic surveys, our passive seismic experiment at the Wenyuhe River site demonstrated that this approach is effective, especially for those faults with significant normal faulting component and the stratigraphy is significantly different on the two sides crossing the fault line. • Passive seismic survey using ambient noise was conducted at the Wenyuhe River site for the purpose to image the Huangzhuang-Gaoliying Fault. • The fault occurrence information deduced by passive seismic survey is in good agreement with active seismic survey results; though understandably with lower resolution. • Passive seismic survey can serve as a low-cost reconnaissance tool for those active fault detection projects merit higher resolution imaging using active seismic survey methods. [ABSTRACT FROM AUTHOR]