Journal of Geomatics Science and Technology

fa ارزیابی روش های به روزرسانی عوارض ساختمانی در بلوک های شهری Evaluation of Updating Methods in Building Blocks Dataset سامانه های اطلاعات مکانی GIS ترویجی Tarviji <div style="text-align: justify;"><span style="font-family:B Nazanin;"><span style="font-size:12.0pt;">با افزایش کاربرد دادههای مکانی در زندگی روزمره، تولید این دادهها از منابع اطلاعاتی مختلف با دقت و مقیاسهای متفاوت رشد وسیعی داشته است. تولید دادههای جدید نیاز به اختصاص زمان و هزینه بسیاری میباشد. از این رو یکی از راهکارهای کاهش هزینه، بهروزرسانی دادههای قدیمی در مقیاسهای مختلف با استفاده از دادههای جدید (تهیه شده در یک مقیاس) میباشد. یکی از رویکردهای بهروزرسانی دادهها بکارگیری مجموعه داده بزرگمقیاس بهروزشده بهعنوان داده مرجع برای بهروزرسانی مجموعه داده کوچکمقیاس است؛ به این صورت که عوارض تغییر یافته در دو مجموعه داده شناسایی و سپس با اصلاح تغییرات متناسب با مجموعه داده کوچکمقیاس بهروزرسانی انجام میشود. مسائل بهروزرسانی نقشه در مجموعه داده برداری از منظر نوع عارضه بهروزرسانیشده به سه دسته نقطهای، خطی و چندضلعی تقسیمبندی میشوند. یکی از دسته عوارض مهم از کلاس عوارض چندضلعی در محیطهای شهری، ساختمانها هستند که در نقشههای شهری از اهمیت بسزایی برخوردارند و به روزرسانی آنها در کاربردهای شهری از اولویت بالایی برخوردار است. در این مقاله سعی شده است با بررسی دقیق و جامع مطالعات پیشین، مسـائل بهروزرسانی عوارض چندضلعی (بویژه ساختمانها) از سه منظر روشهای خوشهبندی، استخراج الگو و روشهای بهروزرسانی مورد استفاده بررسی شوند. همچنین دستهبندی در هر دیدگاه و مزایا و معایب روشهای مختلف نیز در آن ارائه شده است.</span></span></div> <div style="text-align: justify;">With the increasing use of spatial data in daily life, the production of this data from diverse information sources with different precision and scales has grown widely. Generating new data requires a great deal of time and money. Therefore, one solution is to reduce costs is to update the old data at different scales using new data (produced on a similar scale). One approach to updating data is to use the updated large-scale dataset as reference data to update small-scale datasets; in this way, the modified features are identified in the two datasets and then updated by modifying the changes to fit the small-scale dataset. In terms of the type of the updated feature, map updating issues in the vector dataset are divided into three categories: pointwise, linear, and polygon. One of the most important features of the class of polygonal features in urban environments are the buildings that are vital in urban maps and their updating process in urban applications has a high priority. In this research, an attempt has been made to study the issue of updating polygonal features from different perspectives by a careful and comprehensive examination. These perspectives include spatial clustering methods, pattern extraction, and updating methods. <br> Spatial clustering methods are classified into five methods: natural principles, partition-based, graph-based, Region Merging, and density-based approaches. Each clustering method have been used in different studies according to Gestalt criteria (proximity, similarity, and continuity). In pattern extraction, different types of patterns have been studied in various studies while the linear pattern extraction as a sample has been comprehensively examined in this study.<br>   In the updating methods, three updating approaches including Propagating, Local, and Constraint-based are examined. In the Propagation updating approach, only large-scale data are updated, then these updates can be propagated into small-scale data. This update is especially used for the MRDB spatial database by propagating updates at various scales. Local updating consists of three steps: 1) Change detection between the recently updated large-scale dataset and the old small-scale dataset. 2) Integrating the discovered changes into the small-scale dataset (by quantifying and formulating these changes). 3) Ensuring that the consistency is maintained. In Constraint-based updating approach which demonstrates the necessities of this research, first, by grouping the buildings, useful information such as the area of the buildings, the average and the standard deviation of the separation distance between the buildings is obtained. Then, with the appropriate operators, Constraint-based generalization is performed to update the maps. <br> There are various criteria for evaluating the updating methods, while the Precision and Recall criteria have been used to evaluate the effectiveness of these problems. Precision criterion examines the ratio of the number of correctly matched features to the total number of features that that have been matched correctly or incorrectly. Recall criterion evaluates the ratio of correctly matched features to the total features that have been matched correctly or not matched. In other words, in the Precision criterion, the number of features that are incorrectly matched and in the Recall criterion, the number of features that are not matched, are effective in comparing the two criteria. The categorization mentioned in each perspective and the advantages and disadvantages of different methods are also presented in this research.</div> مجموعه داده های برداری, به روزرسانی, عوارض چندضلعی ساختمانی Vector Dataset, Updating, Building Polygon Features 213 226 http://jgst.issgeac.ir/browse.php?a_code=A-10-367-2&slc_lang=fa&sid=1 I. Shahmardani ایمان شاه مردانی abaspour@ut.ac.ir 10031947532846007549 10031947532846007549 No Tehran University بدون سازمان R. Aliabbaspour رحیم علی عباسپور Shahmardani.iman@ut.ac.ir 10031947532846007550 10031947532846007550 No بدون سازمان A. R. Chehreghan علیرضا چهرقان a.chehreghan@gmail.com 10031947532846007551 10031947532846007551 Yes بدون سازمان