CN107229742A - A kind of method that city easily flood point is determined based on remote sensing big data - Google Patents
A kind of method that city easily flood point is determined based on remote sensing big data Download PDFInfo
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Abstract
The invention discloses a kind of method that city easily flood point is determined based on remote sensing big data, applied geography information systems technology is obtained the dem data in city by the remote sensing image data in city；Magnanimity dem data is stored in Hadoop distributed file storage system HDFS；The method for proposing to determine city easily flood point based on big data treatment technology, the parallel processing for determining the easily flooded point methods in city is realized by Map Reduce, quick to determine easily flooded point；And the locus of the easy flood point obtained with WebGIS technologies to calculating carries out visualization and shown on the electronic map；This method that city easily flood point is determined based on remote sensing big data that the present invention is provided, the easy flood that can efficiently and rapidly search out city relative to conventional method is put and intuitively shows the residing locus of the easy flood point in city, is had very high economic results in society in the early warning field of urban waterlogging disaster and is widely applied prospect.
Description
Technical field
The invention belongs to big data analysis technical field, city is determined based on remote sensing big data more particularly, to one kind
The method that easily flood is put.
Background technology
With the influence of quickening and the climate change of urbanization process, urban waterlogging disaster takes place frequently in recent years, it has also become
The severe challenge that China's urban development faces.In order to reduce the heavy losses that Urban Flood Waterlogging is caused, city should be determined first
All easy flood points and key monitoring and improvement are carried out to easily flood point.The existing method for finding easily flood point is mainly manually to survey on the spot
Survey, not only expend substantial amounts of manpower and materials, efficiency is very low, and can not quickly track because urban construction causes the change of easily flood point
Change situation, also can not intuitively obtain the spatial distribution of easily flood point, therefore also be difficult to the warning information of release quickly accumulated water point, give
The traffic trip and its lives and properties of city dweller is caused to perplex and lost.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, city is determined based on remote sensing big data the invention provides one kind
The method of city's easily flood point, its object is to improve to determine the city easily efficiency of flood point and to easy flooded point using big data analysis method
Spatial distribution intuitively shown.
To achieve the above object, city is determined based on remote sensing big data there is provided one kind according to one aspect of the present invention
Easily the method for flood point, comprises the following steps：
(1) remote sensing image data of survey region is converted into DEM (digital elevation model, Digital of Two-Dimensional Moment configuration
Elevation Model) data；
(2) by the distributed file system of the dem data distributed storage to Hadoop, the piecemeal for completing data is deposited
Storage；
(3) dem data stored to piecemeal, passes through elevation and each measurement point periphery phase of relatively more each measurement point
The elevation of adjacent measurement point judges whether each measurement point is low-lying point；And according to default threshold value to the low-lying point that finds
Judged to determine easily flooded point.
Preferably, the above-mentioned method that city easily flood point is determined based on remote sensing big data, its step (3) includes following sub-step
Suddenly：
(3.1) by the content positional information that according to space cutting is arranged of the dem data of Two-Dimensional Moment configuration per a line with
It is determined that the row and column where each measurement point；
(3.2) determine neighbours' point of each measurement point and obtain array (key, value)；
Wherein, key is the coordinate of neighbours' point of current measurement point, and value is the coordinate and elevation of current measurement point；
(3.3) point set is calculated according to the formation of the coordinate of the coordinate of current measurement point and its neighbours' point；
(3.4) calculated by Reduce and determine the central point for calculating point set, according to the elevation of the central point and week
The comparison of the elevation of side adjacent measurement points is calculated to judge whether it is low-lying point, and all low-lying points are carried out according to elevation threshold value
Judge to determine easily flooded point；
Wherein, elevation threshold value is surveyed according to survey region waterlogging data are determined；
Hadoop refers to a distributed system architecture, and the most crucial design of Hadoop frameworks is HDFS (distributed
File system, Hadoop Distributed File System) and Map-Reduce；HDFS provides distribution for big data
Storage, Map-Reduce provides calculating for big data；In the present invention, magnanimity dem data is carried out using HDFS distributed
Storage, parallel processing is realized using Map-Reduce；
Because city dem data amount is huge, in order to upgrade and reflect topography variation that urban construction is brought in time, in this hair
Above-mentioned Distributed Calculation, including two stages of Map and Reduce are used in bright step (3)；Data can be divided into multiple nodes,
Different rows may be distributed to different nodes；Data are distributed with processing in the Map stages, is realized easily in the Reduce stages
The judgement of flood point is calculated.
Preferably, the above-mentioned method that city easily flood point is determined based on remote sensing big data, the elevation of low-lying point and default
Elevation threshold value is compared, if the elevation of this low-lying point is smaller than elevation threshold value, and this low-lying point is just determined as easily flooded point,
Though being low-lying point its object is to filter some, the higher non-easy flooded point of elevation makes result of calculation more meet actual easy
The situation of flood point；
Preferably, the method that above-mentioned kind determines city easily flood point based on remote sensing big data, also comprises the following steps：
(4) according to the spacing between easily flood point row, column number residing in dem data matrix and its row, column and starting
The calculation of longitude ＆ latitude of point goes out the longitude and latitude of easily flood point；Wherein, starting point refers to first point of dem data；
(5) longitude and latitude and elevation information of obtained easy flood point are carried out visualization by WebGIS technologies and shown.
Preferably, the method that above-mentioned kind determines city easily flood point based on remote sensing big data, its step (5) includes following sub-step
Suddenly：
(5.1) electronic map of survey region is loaded in visualization terminal；
(5.2) longitude and latitude and altitude data of easy flood point are imported into electronic map, show the space distribution situation of easily flood point,
And shown the elevation of easy flood point on the electronic map with different colors.
In general, by the contemplated above technical scheme of the present invention compared with prior art, it can obtain down and show
Beneficial effect：
The existing method for finding easily flood point is mainly artificial field exploring, expends a large amount of manpower and materials, efficiency is very low, and
It can not efficiently track because the change of urban construction brings the situation of change of city easily flood point, also intuitively can not accurately obtain easily
The spatial distribution of flood point, therefore be also difficult to look-ahead and issue the position of accumulated water point；
The method that city easily flood point is determined based on remote sensing big data that the present invention is provided, by by current measurement point and its week
The adjacent multiple points in side are compared calculating to determine whether current measurement point is easy flooded point；And big number is combined by remotely-sensed data
According to processing, the terrain data of city magnanimity is stored using Hadoop distributed file storage system HDFS, using Hadoop
Map-Reduce realize distributed variable-frequencypump, the dem data of magnanimity is distributed into different nodes carries out parallel processings, dashes forward
Mass data computation rate bottleneck has been broken, the easily flooded point in quick determination city is realized.Its preferred scheme is realized to easily flood point
The visualization of spatial distribution shows, and the elevation of easy flood point is carried out rendering display on the electronic map with different colors；Phase
For conventional method, the present invention can reflect the topography variation that urban construction is brought in time, and the sky of easily flood point is understood accurate and visually
Between distribution situation, with very high economic results in society and extensive practical value.
Brief description of the drawings
Fig. 1 is that what is provided in the embodiment of the present invention determine the flow signal of the city easily method of flood point based on remote sensing big data
Figure；
Fig. 2 is the schematic diagram for the part dem data changed in embodiment；
Fig. 3 is using Hadoop Map-Reduce methods to carry out the schematic flow sheet of block parallel processing in embodiment.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, not
For limiting the present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below that
Not constituting conflict between this can just be mutually combined.
The method provided in an embodiment of the present invention that city easily flood point is determined based on remote sensing big data, applied geography information system
The remotely-sensed data in technical finesse city, sets up digital complex demodulation (Digital Elevation Model), by magnanimity
Dem data is stored in Hadoop distributed file storage system HDFS, is quickly counted by Map-Reduce based on magnanimity dem data
Calculate and determine city easily flood point and its spatial distribution, two-dimensional visualization is carried out in electronic map after obtained easy flooded point data is rendered
Display.
The method that city easily flood point is determined based on remote sensing big data that embodiment is provided, its flow is as shown in figure 1, including such as
Lower step：
(1) remote sensing image data for being analysed to region is converted into meeting the input of Hadoop programs by GIS-Geographic Information System
The dem data of the Two-Dimensional Moment configuration of txt forms；
In the present embodiment, Arcmap is first turned under ArcGIS, the remote sensing image data of survey region is loaded；Then beat
Open ArctoolBox, find by grid produce under grid turn ASCII instruments, then using the remote sensing image data of loading as defeated
Enter and conversion acquisition dem data is carried out after grid, configuration surroundings variable, setting outgoing route；As shown in Fig. 2 being institute in embodiment
The part dem data schematic diagram changed.
(2) the magnanimity dem data deposit Hadoop obtained distributed file storage system HDFS will be changed；
(3) to distributed storage in the dem data of each node, the respectively elevation by relatively more each measurement point and measurement
The elevation of adjacent eight points in point periphery is come whether judge this point be low-lying point；
If the elevation of the elevation of the measurement point eight measurement points more adjacent than the measurement point periphery is all low, then by the measurement point
It is determined as low-lying point.
The easy flood point in city and the topography variation that urban construction is caused are closely related, city DEM of the present invention based on magnanimity
Data, parallel processing is realized using Map-Reduce；In embodiment, dem data of the distributed storage on each node is used
Map-Reduce carries out block parallel processing, and the quick easy flooded point for determining city obtains easily flooded point data；Its flow is specifically as schemed
Shown in 3, including following sub-step：
(3.1) positional information for being arranged the content of every a line of the dem data of Two-Dimensional Moment configuration according to space cutting,
To determine the row and column where each measurement point, and then determine the position where each point measurement, to be easy to follow-up elevation to compare
And calculation of longitude ＆ latitude；
(3.2) determine i.e. current eight points calculated around point of neighbours' point of each measurement point, and obtain array (key,
value)；Wherein, key is the coordinate of neighbours' point of current point, and value is the coordinate and elevation of current point；
(3.3) it is that the current coordinate for calculating eight points around point is carried out the coordinate and all neighbours point of current measurement point
Collect and exclude the point outside wherein border, it is a calculating logic unit to be formed and calculate point set；
Wherein, the point outside border refers to a point calculated beyond point set, i.e., eight point institute groups of each central point and surrounding
Into calculate point set beyond point；
(3.4) central point for finding above-mentioned calculating point concentration is calculated by Reduce and judges whether it is low-lying point, root
The low-lying point found is filtered according to default elevation threshold value to determine easily flooded point；
In the present embodiment, by taking Wuhan City as an example, elevation threshold value is set according to the altitude data that Wuhan City surveys easily flood point
For 36 meters of height above sea level (height value of Wuhan City's highest easily flood point is 35.77 meters), elevation is less than to the low-lying point of the elevation threshold value
It is judged as easily flooded point；
(4) longitude and latitude and altitude data of easy flood point are imported into electronic map, shows the space distribution situation of easily flood point, and
The elevation of easy flood point is carried out into visualization on the electronic map with different colors to show；
It is specific as follows in the present embodiment：The map that Wuhan City is loaded on webpage calls corresponding API (application program volumes
Journey interface, Application Programming Interface), the longitude and latitude and altitude data of obtained easy flood point are led
Enter in the map and it is carried out to render displaying；In the present embodiment, the absolute elevation of easily flood point is lower, and the color for rendering use is got over
It is deep.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, it is not used to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention etc., it all should include
Within protection scope of the present invention.
Claims (5)
1. a kind of method that city easily flood point is determined based on remote sensing big data, it is characterised in that comprise the following steps：
(1) remote sensing image data of survey region is converted into the dem data of Two-Dimensional Moment configuration；
(2) by the distributed file system of the dem data distributed storage to Hadoop, the piecemeal storage of data is completed；
(3) dem data stored to piecemeal, is surveyed by the way that the elevation of relatively more each measurement point is adjacent with each measurement point periphery
The elevation of point is measured to judge whether each measurement point is low-lying point；And according to default elevation threshold value to the low-lying point that finds
Judged to determine easily flooded point.
2. the method as described in claim 1, it is characterised in that step (3) includes following sub-step：
(3.1) by the content positional information that according to space cutting is arranged of the dem data of Two-Dimensional Moment configuration per a line to determine
Row and column where each measurement point；
(3.2) determine neighbours' point of each measurement point and obtain array (key, value)；
Wherein, key is the coordinate of neighbours' point of current measurement point, and value is the coordinate and elevation of current measurement point；
(3.3) point set is calculated according to the formation of the coordinate of the coordinate of current measurement point and its neighbours' point；
(3.4) calculated by Reduce and determine the central point for calculating point set, according to the elevation of the central point and periphery phase
The comparison of the elevation of adjacent measurement point is calculated to judge whether it is low-lying point, and all low-lying points are judged according to elevation threshold value
To determine easily flood point；
Wherein, elevation threshold value is surveyed according to survey region waterlogging data are determined.
3. method as claimed in claim 2, it is characterised in that elevation and the elevation threshold value of low-lying point are compared, if low
The elevation of low-lying area point is less than the elevation threshold value, then the low-lying point is determined as into easily flooded point.
4. the method as described in claim 1, it is characterised in that also comprise the following steps：
(4) according to the spacing and the warp of starting point between easily flood point row, column residing in dem data matrix and its row, column
Latitude calculates the longitude and latitude of easily flood point；
(5) longitude and latitude and elevation information of easy flood point are carried out visualization by WebGIS technologies and shown.
5. method as claimed in claim 4, it is characterised in that the step (5) includes following sub-step：
(5.1) electronic map of survey region is loaded in visualization terminal；
(5.2) longitude and latitude and altitude data of easy flood point are imported into electronic map, shows the space distribution situation of easily flood point, and will
The elevation of easily flood point is shown on the electronic map with different colors.
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| CN108364129A (en) * | 2018-02-08 | 2018-08-03 | 广州地理研究所 | A kind of region ecology safety early alarming and forecasting method based on remote sensing big data |
| CN111243091A (en) * | 2020-04-09 | 2020-06-05 | 速度时空信息科技股份有限公司 | Massive DEM pyramid slice parallel construction method based on distributed system |
| CN111815117A (en) * | 2020-06-10 | 2020-10-23 | 河海大学 | Urban waterlogging tendency simulation evaluation method based on Grasshopper platform |
| CN113593191A (en) * | 2021-08-10 | 2021-11-02 | 安徽嘉拓信息科技有限公司 | Visual urban waterlogging monitoring and early warning system based on big data |
| CN116502029A (en) * | 2023-04-26 | 2023-07-28 | 上饶高投智城科技有限公司 | Smart city big data analysis and system based on Hadoop MapReduce |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108364129A (en) * | 2018-02-08 | 2018-08-03 | 广州地理研究所 | A kind of region ecology safety early alarming and forecasting method based on remote sensing big data |
| CN111243091A (en) * | 2020-04-09 | 2020-06-05 | 速度时空信息科技股份有限公司 | Massive DEM pyramid slice parallel construction method based on distributed system |
| CN111243091B (en) * | 2020-04-09 | 2020-07-24 | 速度时空信息科技股份有限公司 | Massive DEM pyramid slice parallel construction method based on distributed system |
| CN111815117A (en) * | 2020-06-10 | 2020-10-23 | 河海大学 | Urban waterlogging tendency simulation evaluation method based on Grasshopper platform |
| CN111815117B (en) * | 2020-06-10 | 2022-08-26 | 河海大学 | Grasshopper platform-based urban waterlogging susceptibility simulation evaluation method |
| CN113593191A (en) * | 2021-08-10 | 2021-11-02 | 安徽嘉拓信息科技有限公司 | Visual urban waterlogging monitoring and early warning system based on big data |
| CN116502029A (en) * | 2023-04-26 | 2023-07-28 | 上饶高投智城科技有限公司 | Smart city big data analysis and system based on Hadoop MapReduce |
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