Urban Space Regeneration

The main idea I pursued in my research is the regeneration of urban space, which helped to create a new civic space for a city. The details of urban space regeneration are designed to improve the quality of life, and it involves the use of urban public space. Based on the research and background study, my thesis explored to improve the urban environment, enhance urban space usage and improve city image in Beijing to regenerate it from a heavy industrial city in a garden city. In my research on regeneration of urban space I came across the example of the city named Dalian that I used as a guide. Dalian is now a well-known city in China, because of the success in transforming the city from a heavy industrial city in a garden city

Geochemical consequences of subduction zone metamorphism: Case studies of metamorphic rocks from Palaeozoic subduction zone complexes in Tianshan and Qilian Orogenic Belts, NW China

Subduction-zone metamorphism (SZM) is considered to be a major geochemical process on Earth of both petrological and geodynamic significance that triggers the subduction-zone magmatism and contributes to the mantle compositional heterogeneity. To understand SZM and elemental responses to SZM, detailed petrological and geochemical studies were conducted on metamorphic rocks of basaltic and sedimentary protoliths from two orogenic belts, i.e., Western Tianshan and North Qilian Mountain, in NW China. Based on the bulk-rock geochemistry of rocks from ultrahigh pressure metamorphic belt of Western Tianshan, different elemental mobility/immobility has been identified using the inter-elemental correlations. Mineral compositions have also been analyzed for the same rocks. The significant elemental hosts are phengitic muscovite, paragonite, garnet, epidote group minerals, rutile and titanite. Together with detailed petrography and considering a series of plausible metamorphic reactions, we conclude that it is the presence and stability of these minerals that largely controls the geochemical behaviors of chemical elements during SZM. In terms of both bulk-rock composition and mineral geochemistry for rocks from North Qilian Mountain, we conclude the same except the mobility of U, which may be attributed to the seafloor alteration rather than SZM. The consistent immobility of U, Th and light rare earth elements (LREEs), like high field strength elements (HFSEs), during SZM indicates that the enrichment of these elements in arc magmas is not caused by simple dehydrated aqueous fluids. Therefore, the traditionally accepted fluid flux induced-melting needs reconsideration in order to explain the arc signature in melts produced through subduction-zone magmatism. In addition, the lack of Rb/Sr-Sm/Nd (or Lu/Hf) correlation in these and other metabasites world-wide is inconsistent with the observed first-order Sr-Nd (or Hf) isotope correlation in oceanic basalts. Hence, the subducted residual ocean crust cannot be the major source materials for oceanic basalts although it can contribute to mantle compositional heterogeneity

The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area

The joint interpretation of very high resolution SAR and optical images in dense urban area are not trivial due to the distinct imaging geometry of the two types of images. Especially, the inevitable layover caused by the side-looking SAR imaging geometry renders this task even more challenging. Only until recently, the "SARptical" framework [1], [2] proposed a promising solution to tackle this. SARptical can trace individual SAR scatterers in corresponding high-resolution optical images, via rigorous 3-D reconstruction and matching. This paper introduces the SARptical dataset, which is a dataset of over 10,000 pairs of corresponding SAR, and optical image patches extracted from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical images. This dataset opens new opportunities of multisensory data analysis. One can analyze the geometry, material, and other properties of the imaged object in both SAR and optical image domain. More advanced applications such as SAR and optical image matching via deep learning [3] is now also possible.Comment: This manuscript was submitted to IGARSS 201

SAR Tomography via Nonlinear Blind Scatterer Separation

Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation. Retrieving the scattering profile along the mixed dimension (elevation) is typically solved by inversion of the SAR imaging model, a process known as SAR tomography. This paper proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that conventional linear separation methods, e.g., principle component analysis (PCA), can only partially separate the scatterers under good conditions. These methods produce systematic phase bias in the retrieved scatterers due to the nonorthogonality of the scatterers' steering vectors, especially when the intensities of the sources are similar or the number of images is low. The proposed method artificially increases the dimensionality of the data using kernel PCA, hence mitigating the aforementioned limitations. In the processing, the proposed method sequentially deflates the covariance matrix using the estimate of the brightest scatterer from kernel PCA. Simulations demonstrate the superior performance of the proposed method over conventional PCA-based methods in various respects. Experiments using TerraSAR-X data show an improvement in height reconstruction accuracy by a factor of one to three, depending on the used number of looks.Comment: This work has been accepted by IEEE TGRS for publicatio

Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.Comment: 33 pages with 2 figure