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<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLII-3-W10-17-2020</article-id>
<title-group>
<article-title>DYNAMIC MONITORING METHOD OF ILLEGAL BUILDINGS USING SPATIOTEMPORAL BIG DATA BASED ON URBAN HIGH LYING ZONES</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Li</surname>
<given-names>B. S.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Tan</surname>
<given-names>Z. C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, Guangxi, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, Guangxi, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>07</day>
<month>02</month>
<year>2020</year>
</pub-date>
<volume>XLII-3/W10</volume>
<fpage>17</fpage>
<lpage>23</lpage>
<permissions>
<copyright-statement>Copyright: © 2020 B. S. Li</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-3-W10-17-2020.html">This article is available from https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-3-W10-17-2020.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-3-W10-17-2020.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-3-W10-17-2020.pdf</self-uri>
<abstract>
<p>&lt;p&gt;China's urban illegal buildings are emerging in an endless stream with a large number. There is a wide demand for urban illegal buildings monitoring in urban management departments, including Beijing, Shanghai, Guangzhou and other regions where urban management is facing increasingly serious problems of illegal buildings. It is urgent to solve the common problem of "urban disease" caused by urban illegal buildings, and a new automatic monitoring method that can reduce the cost of urban management is urgently needed. This kind of automated monitoring method of illegal buildings has a wide market demand in Urban Management and Law Enforcement and the Ministry of Land and Resources. Existing technologies cannot realize long-term, autonomous, rapid and intelligent dynamic real-time monitoring of urban illegal buildings, which leads to the problem that illegal buildings’ behaviors cannot be stopped in time. There is a possibility to solve these problems using the ubiquitous network of base stations in cities to monitor illegal buildings.&lt;/p&gt;&lt;p&gt;This paper proposes a dynamic monitoring method of illegal buildings using spatiotemporal big data based on urban high lying zones. Through the Spatiotemporal sensor network technology, the tilt-type stereo camera is set up at the high lying zones around the survey area. According to the real-time ambient temperature and humidity numerical data fed back by the temperature and humidity sensor, the tilt-type stereo camera uses intelligent time-lapse photogrammetry technology to obtain multiple stereo pairs. The tilted remote sensor transmits the multiple stereo pairs to the urban dynamic analysis service network using wireless transmission. The urban dynamic analysis service network will complete a series of analysis and processing operations without any human intervention, and then transmit the results of the analysis to the early-warning terminal successively through the base station, WIFI and other wireless transmission methods. Finally, the illegal building data is uploaded to the terminal. That is to say, the urban dynamic analysis service network can realize the intelligent, automatic analysis and processing of spatial analysis server and the operation of calling all database data and storing data.&lt;/p&gt;&lt;p&gt;The method proposed in this paper uses the high lying zones around the survey area to expand the monitoring range, improve the accuracy of monitoring data, realize long-term real-time monitoring, and fully utilize the characteristics of Spatiotemporal sensing network technology intelligent, autonomous, wireless transmission, etc., significantly reducing labor. It greatly shortens the process from the emergence to the discovery of illegal buildings in cities. The workload of monitoring has improved the efficiency of dynamic monitoring and warehousing of illegal buildings data in cities.&lt;/p&gt;</p>
</abstract>
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