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961.
962.
Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning. 相似文献
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Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach. 相似文献
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针对总变分(TV)模型在去噪时会产生阶梯效应、纹理细节信息丢失的问题,提出一种新的乘性噪声去除模型。该模型采用分数阶变分(FV)和二阶总广义变分(TGV)的正则项。FV的正则项在去噪时可保留图像的纹理信息,TGV的正则项在去噪时可改善阶梯效应。为有效求解该模型,先采用分裂法和交替方向法将原问题转化为两个相关的子问题,再利用原始对偶和梯度下降算法分别对子问题进行求解。对仿真图像进行实验的结果表明,新模型不仅可以提高图像的信噪比,而且可以更好地保留图像的边缘和纹理细节信息,改善阶梯效应。 相似文献
970.
大比例尺地理信息数据由于详尽的数据内容和精准的位置信息,在城市经济社会发展中发挥着重要作用。然而由于传统的服务方式的限制,数据的使用效率并不高。地理空间框架数据结合GIS技术和网络技术,将大比例尺地理信息数据使用方式从线下转到线上,提高了数据使用效率。而本文提出的主动式地理信息服务模式是一种全新的地理信息服务模式,将大比例尺地理信息服务数据与用户业务数据深度融合,最大限度地满足用户的业务需求,将是地理信息服务模式未来的发展方向。 相似文献