KEYWORDS: Data modeling, Nanoimprint lithography, Sustainability, Lithium, Cell phones, Autocorrelation, Global Positioning System, Data integration, Data analysis, Statistical analysis
To create more vitality and sustainable cities, enhance quality of life for residents is the key issue of urban renewal. Multi-source data is main data source with ability of integration and analysis of data from multiple sources including POI, mobile phone signaling data, GPS track data, to better understand and improve the overall health and wellbeing of urban areas. In this paper, we proposed a framework of urban vitality estimation using mobile phone signaling data and POI data by combing Moran’s I to determine the suitable analysis grid. As a result, the area of triangle made up of Old city, Baishahe, and Yangjiapu of Weifang city has a high degree of vitality, as it has both a diverse mix of land uses, and some concentration of land uses in certain areas.
In the imaging process of optical image, there is an offset of building roof footprint in the image from the location of the real world because of the angle of sensor. The offset as one of the important elements establishment of urban basic geographic information has an impact on city planning. In this paper, we proposed to correct the building roof footprint offset in optical image using wall baselines extracted by morphological attribute profiles(MAPs) algorithm from SAR imagery. Firstly, MAPs algorithm was adopted to detect building connected components(CCs) from high-resolution SAR imagery, considering the ability of MAPs on detecting buildings of different sizes, shapes and directions. Then, the building wall baselines were extracted according to the skeleton thinning lines by applying alternating sequence morphological thinning on buildings CCs. Next, we built a line feature matching template based on similarity and distance for template matching calculation of building roof footprint characteristics extracted from optical image. Finally, building roof footprints offset was corrected pixel by pixel. The results show that the proposed method is feasible and the correction rate of building roof offset is improved compared with the shadow-based method
In order to improve the classification accuracy, quotient space theory was applied in the classification of polarimetric SAR (PolSAR) image. Firstly, Yamaguchi decomposition method is adopted, which can get the polarimetric characteristic of the image. At the same time, Gray level Co-occurrence Matrix (GLCM) and Gabor wavelet are used to get texture feature, respectively. Secondly, combined with texture feature and polarimetric characteristic, Support Vector Machine (SVM) classifier is used for initial classification to establish different granularity spaces. Finally, according to the quotient space granularity synthetic theory, we merge and reason the different quotient spaces to get the comprehensive classification result. Method proposed in this paper is tested with L-band AIRSAR of San Francisco bay. The result shows that the comprehensive classification result based on the theory of quotient space is superior to the classification result of single granularity space.
Phase unwrapping is a key step in InSAR (Synthetic Aperture Radar Interferometry) processing, and its result may directly affect the accuracy of DEM (Digital Elevation Model) and ground deformation. However, the decoherence phenomenon such as shadows and layover, in the area of severe land subsidence where the terrain is steep and the slope changes greatly, will cause error transmission in the differential wrapped phase information, leading to inaccurate unwrapping phase. In order to eliminate the effect of the noise and reduce the effect of less sampling which caused by topographical factors, a weighted least-squares method based on confidence level in frequency domain is used in this study. This method considered to express the terrain slope in the interferogram as the partial phase frequency in range and azimuth direction, then integrated them into the confidence level. The parameter was used as the constraints of the nonlinear least squares phase unwrapping algorithm, to smooth the un-requirements unwrapped phase gradient and improve the accuracy of phase unwrapping. Finally, comparing with interferometric data of the Beijing subsidence area obtained from TerraSAR verifies that the algorithm has higher accuracy and stability than the normal weighted least-square phase unwrapping algorithms, and could consider to terrain factors.
The ground subsidence phenomenon is more serious in Beijing, large-scale land subsidence seriously threats to urban planning and construction and the safety of residents. In order to study the subsidence condition, it is necessary to monitor land subsidence. Choosing 28 scenes Envisat ASAR images covering Beijing city from December 2003 to March 2009, permanent scatterer SAR interferometry (PSI) technique was applied to obtained time series land subsidence information. Then the trend characteristics and factors of subsidence were analyzed, comparing land subsidence result with the groundwater data and geological structure data. Comparison between the PSI-derived subsidence rates and leveling data obtained shows that the result of PSI is agreed with the leveling data. The results indicate that the PSI technique is capable of providing high-level accuracy subsidence information. The results show that:(1) The deformation rates derived PSI ranging from -45.80 to 4.36mm/a;(2) In the study area, the serious subsidence areas distribute in Chaoyang District, Shunyi District, Tongzhou District and Pinggu District;(3) The subsidence tends to become more and more concentrated in 6 years from 2003 to 2009.
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