CN112287058A - Working method for intelligently detecting land red line through cloud computing - Google Patents
Working method for intelligently detecting land red line through cloud computing Download PDFInfo
- Publication number
- CN112287058A CN112287058A CN202011226003.9A CN202011226003A CN112287058A CN 112287058 A CN112287058 A CN 112287058A CN 202011226003 A CN202011226003 A CN 202011226003A CN 112287058 A CN112287058 A CN 112287058A
- Authority
- CN
- China
- Prior art keywords
- area
- red
- space
- line
- result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000003321 amplification Effects 0.000 claims description 3
- 238000011161 development Methods 0.000 claims description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 description 18
- 238000004458 analytical method Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/28—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Marketing (AREA)
- Human Resources & Organizations (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Remote Sensing (AREA)
- Educational Administration (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Processing Or Creating Images (AREA)
- Image Processing (AREA)
Abstract
The invention provides a working method for intelligently detecting a land red line through cloud computing, which comprises the following steps of: s1, acquiring the basic data of the landed red line, and importing the basic data into a big database; s2, carrying out ground red line space layer detection by a bottom line conflict detection method; and S3, calculating the area of the red and ground line by a space approval data monitoring method. Through cloud computing, the red line for land use intelligent detection is carried out, the detection result is obtained and displayed, the working efficiency is improved, and the red line for land use intelligent detection can be carried out quickly and efficiently.
Description
Technical Field
The invention relates to the field of geographic information, in particular to a working method for intelligently detecting a land red line through cloud computing.
Background
Because the application of big data is deepened and promoted constantly, any field is all carrying out intelligent technology with corresponding data upload high in the clouds big data platform, but the corresponding data that relates to geographic information among the prior art, can't accomplish accurate matching land used information, especially in the management process to the red line of land used is set up to specific area, can not be fast accurate acquire the early warning information of red line of land used, this needs technical staff in the field to solve corresponding technical problem promptly.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a working method for intelligently detecting a land red line through cloud computing.
In order to achieve the above object, the present invention provides a working method for intelligently detecting a red earth line through cloud computing, comprising the steps of:
s1, acquiring the basic data of the landed red line, and importing the basic data into a big database;
s2, carrying out ground red line space layer detection by a bottom line conflict detection method;
and S3, calculating the area of the red and ground line by a space approval data monitoring method.
Preferably, the S1 includes:
s1-1, uploading the coordinates of the inflection points of the red land lines to a large spatial database;
and S1-2, analyzing the uploaded inflection point coordinate file into a coordinate set, and calculating the space reference authentication information WKID of the projection coordinate system according to the range of the uploaded coordinate set.
Preferably, the S1 further includes:
s1-3, uploading the analyzed coordinate set and coordinate system to a space big database computing environment;
and S1-4, converting the coordinate set into a space geometric object A which can be used by a space big database computing environment by using Pyspark, analyzing the uploaded inflection point coordinate file into a coordinate set, and forming a projection coordinate system by using the uploaded coordinate set.
Preferably, the S1 includes:
s1-5, performing intersection operation on the space geometric object A and the permanent basic farmland protection area to obtain a result B, if the result B is empty, not calculating the area, otherwise, summing the area of the image spots, storing the result into a JSON file processed by Python, simultaneously issuing the intersection range into a space image layer, performing space control on the ecological protection red line, the permanent basic farmland and the natural protection area, performing space amplification operation according to the fineness of the obtained result, thereby performing global preview of the red-earth line, and then performing space reduction operation, thereby performing detail scanning of the red-earth line.
Preferably, the S2 includes:
s2-1, setting a land use range interval according to a bottom line conflict detection method, importing three layers of an ecological protection red line, a permanent basic farmland and a natural protection area, carrying out land data conflict detection judgment, calculating a corresponding land use conflict area if bottom line conflict occurs, and executing S2-2 if no conflict occurs;
s2-2, in the process of judging the bottom line conflict detection method, extracting the txt format red line file in the server, extracting the land use position and the land use application range, and analyzing by the bottom line conflict detection method.
Preferably, the S3 includes:
s3-1, calculating the area of the red earth line by a space approval data monitoring method, performing intersection operation on the space geometric object A and a specific region to obtain a result C, if the result C is empty, not calculating the area of the red earth line, otherwise, calculating the area of the red earth line, performing grouping area summation according to the specific region type, storing the result in a JSON file processed by Python, and simultaneously issuing the intersected range into a space map layer; the red-earth line calculation is carried out through linear engineering comparison and selection,
s3-2, performing intersection operation on the space geometric object A and a red land line to obtain a result D, if the result D is empty, not calculating the area, otherwise, performing the sum of the areas of the image spots, storing the result into a JSON file processed by Python, and simultaneously issuing the intersection range into a space image layer; and selecting a coverage range according to the layer attribute.
Preferably, the S3 includes:
S-A, if the region type is A natural protection region, extracting A core region of the natural protection region to perform grouping areA summation, extracting the areA of the intersecting land of the core region, and publishing the intersecting range into A space map layer;
S-B, extracting test areas of the natural protection area to sum in groups, extracting the intersecting area of the test areas, and publishing the intersecting range into a space map layer;
and S-C, extracting the buffer areas of the natural protection area to sum the grouped areas, extracting the intersecting area of the buffer areas, and issuing the intersecting range into a space image layer.
Preferably, the S1 includes:
the ground red line basic data comprises: permanent basic farmland protection area, ecological protection red line area, natural protection area and town development boundary area.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
through cloud computing, the red line for land use intelligent detection is carried out, the detection result is obtained and displayed, the working efficiency is improved, and the red line for land use intelligent detection can be carried out quickly and efficiently.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general schematic of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1 and 2, the invention discloses a working method for intelligently detecting a red earth line through cloud computing, which comprises the following steps:
when the process of using the red line of earth is carried out, the spatial prediction analysis is firstly carried out, and the method comprises the following steps:
s1, obtaining the changed land utilization data in the historical data, and uploading the changed land utilization data to a database;
s2, selecting corresponding change range data according to the land use change extraction instruction;
s3, performing corresponding data filtering according to the characteristics of the variation range data to obtain area data of land use variation;
and S4, forming a space layer by the changed area data, and displaying the prediction result of land use change in a map layer mode.
Preferably, the S2 includes:
s2-1, selecting land use change range data F and a first time interval A and a second time interval B to be carried out according to the land use change extraction instruction;
and S2-2, according to the change range data F, transmitting the layer parameters of the land use change data of the first time interval A and the land use change data of the second time interval B to a space big database environment.
Preferably, the S3 includes:
s3-1, according to the change range data F, filtering the land use change data of the first time interval A and the second time interval B to obtain a change range A of the first time interval1And a second time interval variation range B1；
S3-2, changing the first time interval in a range A1And a second time interval variation range B1Performing land use change space association to obtain land use associated data C, and adding Field for determining whether land area changes1；
S3-3, comparing the land name field of the first time interval change range A1 with the land name field of the second time interval change range B1 in the land use related data C, and if the land use changes, assigning the land name field value in A1 to be the land name field value in B1, otherwise, assigning no change.
S3-4, removing data with unchanged median value of Field1 in the land use related data C to obtain land use change data C1。
Preferably, the S4 includes:
s4-1, for C1The data in the method are grouped and summed according to a Field of Field1 to obtain the area of each land type change, and the result is stored in a JSON file processed by Python;
and S4-2, returning the JSON file processed by Python in the result to the database, and displaying the land use result according to the time interval change in a map layer mode.
Preferably, the historical data includes:
selecting annual land use variation range data F to be performed, and a first time interval A and a second time interval B, the land use variation range data F including district-county land use data, village-town land use data, and village-village land use data.
1. Firstly, importing 'three-zone three-line' data of a permanent basic farmland protection area, an ecological protection red line, a natural protection area and a town development boundary into a large spatial database environment in the space usage control calculation;
2. uploading inflection point coordinates of the red land utilization line to a large spatial database, setting a land utilization range interval according to a bottom line conflict detection method, importing three layers of an ecological protection red line, a permanent basic farmland and a natural protection area, carrying out land data conflict detection and judgment, calculating a corresponding land utilization conflict area if the bottom line conflict occurs, and executing the next step if the bottom line conflict does not occur;
in the process of judging the bottom line conflict detection method, extracting a txt format red line file in a server, extracting the land use position and the land use application range, and analyzing by the bottom line conflict detection method; wherein bottom line conflict is the minimum of the red-to-earth line;
3. analyzing the uploaded inflection point coordinate file into a coordinate set, and calculating space reference authentication information WKID of the projection coordinate system according to the uploaded coordinate range;
4. uploading the analyzed coordinate set and coordinate system to a spatial big database computing environment;
5. transforming the coordinate set into a space geometric object A which can be used by a space big database computing environment by using Pyspark, and projecting by using the coordinate system in the third step;
6. performing intersection operation on the spatial geometric object A and the permanent basic farmland protection area to obtain a result B, if the result B is empty, not calculating the area, otherwise, performing image spot area summation, storing the result into a JSON file processed by Python, and simultaneously issuing an intersection range into a spatial image layer, performing spatial control on an ecological protection red line, the permanent basic farmland and a natural protection area, performing spatial amplification operation according to the fineness of the obtained result, thereby performing global preview of the red-earth line, and then performing spatial reduction operation, thereby performing detail scanning of the red-earth line;
7. calculating the area of a red earth line by a space approval data monitoring method, performing intersection operation on a space geometric object A and a natural protection area to obtain a result C, if the result C is empty, not calculating the area, otherwise, calculating the area, performing grouped area summation according to the area types (a core area, a test area and a buffer area) of the natural protection area, storing the result into a JSON file processed by Python, and simultaneously issuing the intersected range into a space map layer; the red-earth line calculation is carried out through linear engineering comparison and selection,
8. performing intersection operation on the spatial geometric object A and the ecological protection red line to obtain a result D, if the result D is empty, not calculating the area, otherwise, performing sum of the areas of the image spots, storing the result into a JSON (Java server object notation) file processed by Python, and simultaneously issuing an intersection range into a spatial image layer; selecting a coverage range according to the layer attribute;
9. and returning the JSON file to the front end, and displaying the space conflict detection result in a space analysis report mode.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. A working method for intelligently detecting a land red line through cloud computing is characterized by comprising the following steps:
s1, acquiring the basic data of the landed red line, and importing the basic data into a big database;
s2, carrying out ground red line space layer detection by a bottom line conflict detection method;
and S3, calculating the area of the red and ground line by a space approval data monitoring method.
2. The working method of the intellectual detection system by cloud computing according to claim 1, wherein the S1 includes:
s1-1, uploading the coordinates of the inflection points of the red land lines to a large spatial database;
and S1-2, analyzing the uploaded inflection point coordinate file into a coordinate set, and calculating the space reference authentication information WKID of the projection coordinate system according to the range of the uploaded coordinate set.
3. The working method of the intellectual detection system by cloud computing according to claim 1, wherein the S1 further includes:
s1-3, uploading the analyzed coordinate set and coordinate system to a space big database computing environment;
and S1-4, converting the coordinate set into a space geometric object A which can be used by a space big database computing environment by using Pyspark, analyzing the uploaded inflection point coordinate file into a coordinate set, and forming a projection coordinate system by using the uploaded coordinate set.
4. The working method of the intellectual detection system by cloud computing according to claim 3, wherein said S1 includes:
s1-5, performing intersection operation on the space geometric object A and the permanent basic farmland protection area to obtain a result B, if the result B is empty, not calculating the area, otherwise, summing the area of the image spots, storing the result into a JSON file processed by Python, simultaneously issuing the intersection range into a space image layer, performing space control on the ecological protection red line, the permanent basic farmland and the natural protection area, performing space amplification operation according to the fineness of the obtained result, thereby performing global preview of the red-earth line, and then performing space reduction operation, thereby performing detail scanning of the red-earth line.
5. The working method of the intellectual detection system by cloud computing according to claim 3, wherein said S2 includes:
s2-1, setting a land use range interval according to a bottom line conflict detection method, importing three layers of an ecological protection red line, a permanent basic farmland and a natural protection area, carrying out land data conflict detection judgment, calculating a corresponding land use conflict area if bottom line conflict occurs, and executing S2-2 if no conflict occurs;
s2-2, in the process of judging the bottom line conflict detection method, extracting the txt format red line file in the server, extracting the land use position and the land use application range, and analyzing by the bottom line conflict detection method.
6. The working method of the intellectual detection system by cloud computing according to claim 1, wherein the S3 includes:
s3-1, calculating the area of the red earth line by a space approval data monitoring method, performing intersection operation on the space geometric object A and a specific region to obtain a result C, if the result C is empty, not calculating the area of the red earth line, otherwise, calculating the area of the red earth line, performing grouping area summation according to the specific region type, storing the result in a JSON file processed by Python, and simultaneously issuing the intersected range into a space map layer; the red-earth line calculation is carried out through linear engineering comparison and selection,
s3-2, performing intersection operation on the space geometric object A and a red land line to obtain a result D, if the result D is empty, not calculating the area, otherwise, performing the sum of the areas of the image spots, storing the result into a JSON file processed by Python, and simultaneously issuing the intersection range into a space image layer; and selecting a coverage range according to the layer attribute.
7. The working method of intellectual detection system with a red line through cloud computing according to claim 6, wherein the S3 includes:
S-A, if the region type is A natural protection region, extracting A core region of the natural protection region to perform grouping areA summation, extracting the areA of the intersecting land of the core region, and publishing the intersecting range into A space map layer;
S-B, extracting test areas of the natural protection area to sum in groups, extracting the intersecting area of the test areas, and publishing the intersecting range into a space map layer;
and S-C, extracting the buffer areas of the natural protection area to sum the grouped areas, extracting the intersecting area of the buffer areas, and issuing the intersecting range into a space image layer.
8. The working method of the intellectual detection system by cloud computing according to claim 1, wherein the S1 includes:
the ground red line basic data comprises: permanent basic farmland protection area, ecological protection red line area, natural protection area and town development boundary area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011226003.9A CN112287058B (en) | 2020-11-05 | 2020-11-05 | Working method for intelligently detecting land red line through cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011226003.9A CN112287058B (en) | 2020-11-05 | 2020-11-05 | Working method for intelligently detecting land red line through cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112287058A true CN112287058A (en) | 2021-01-29 |
| CN112287058B CN112287058B (en) | 2021-06-15 |
Family
ID=74351408
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011226003.9A Active CN112287058B (en) | 2020-11-05 | 2020-11-05 | Working method for intelligently detecting land red line through cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112287058B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040172174A1 (en) * | 2003-02-27 | 2004-09-02 | Julich Paul M. | System and method for computer aided dispatching using a coordinating agent |
| CN104156520A (en) * | 2014-07-31 | 2014-11-19 | 哈尔滨工程大学 | Linear projection based convex-polyhedron collision detection method |
| CN104746397A (en) * | 2015-01-30 | 2015-07-01 | 西南交通大学 | Determination method for minimum chamfer value of red lines of road intersection |
| CN105956345A (en) * | 2016-06-24 | 2016-09-21 | 江苏科技大学 | Modeling method for spherical guide way and sliding block joint part on basis of analysis of working conditions of boundary |
| CN107146026A (en) * | 2017-05-09 | 2017-09-08 | 南京市城市规划编制研究中心 | A kind of space planning land used difference automatic synchronization method |
| CN107862644A (en) * | 2017-12-21 | 2018-03-30 | 武汉大学 | A kind of analysis and evaluation method towards " three lines " conflict |
| CN109102193A (en) * | 2018-08-17 | 2018-12-28 | 湖南城市学院 | Geography designs ecological red line and delimit and management system and database, evaluation model |
| CN110413666A (en) * | 2019-05-31 | 2019-11-05 | 河南省科学院地理研究所 | A kind of multi-source heterogeneous data integration method of farmland quality |
| CN110889562A (en) * | 2019-12-09 | 2020-03-17 | 杨阳 | Dynamic city model system based on scenario planning |
-
2020
- 2020-11-05 CN CN202011226003.9A patent/CN112287058B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040172174A1 (en) * | 2003-02-27 | 2004-09-02 | Julich Paul M. | System and method for computer aided dispatching using a coordinating agent |
| CN104156520A (en) * | 2014-07-31 | 2014-11-19 | 哈尔滨工程大学 | Linear projection based convex-polyhedron collision detection method |
| CN104746397A (en) * | 2015-01-30 | 2015-07-01 | 西南交通大学 | Determination method for minimum chamfer value of red lines of road intersection |
| CN105956345A (en) * | 2016-06-24 | 2016-09-21 | 江苏科技大学 | Modeling method for spherical guide way and sliding block joint part on basis of analysis of working conditions of boundary |
| CN107146026A (en) * | 2017-05-09 | 2017-09-08 | 南京市城市规划编制研究中心 | A kind of space planning land used difference automatic synchronization method |
| CN107862644A (en) * | 2017-12-21 | 2018-03-30 | 武汉大学 | A kind of analysis and evaluation method towards " three lines " conflict |
| CN109102193A (en) * | 2018-08-17 | 2018-12-28 | 湖南城市学院 | Geography designs ecological red line and delimit and management system and database, evaluation model |
| CN110413666A (en) * | 2019-05-31 | 2019-11-05 | 河南省科学院地理研究所 | A kind of multi-source heterogeneous data integration method of farmland quality |
| CN110889562A (en) * | 2019-12-09 | 2020-03-17 | 杨阳 | Dynamic city model system based on scenario planning |
Non-Patent Citations (1)
| Title |
|---|
| 易丹 等: "江西省"三线冲突"空间特征及其强度影响因素", 《自然资源学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112287058B (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110135351B (en) | Built-up area boundary identification method and equipment based on urban building space data | |
| CN105893972B (en) | Automatic monitoring method for illegal building based on image and implementation system thereof | |
| CN103136393B (en) | A kind of areal coverage computing method based on stress and strain model | |
| CN109165272B (en) | Geographical information visual analysis system for high-resolution remote sensing image | |
| CN112115198A (en) | Urban remote sensing intelligent service platform | |
| CN111259955B (en) | Reliable quality inspection method and system for geographical national condition monitoring result | |
| CN110134752B (en) | Three-dimensional large-scene modeling data processing method and device | |
| CN102708587A (en) | Method and system for acquiring three-dimensional building information rapidly | |
| CN110427441B (en) | Railway external environment hidden danger detection and management method based on air-ground integration technology | |
| CN113340286B (en) | Method and equipment for analyzing surveying and mapping information data of land planning surveying and mapping project and computer storage medium | |
| Mishra et al. | Urban heat island in Kathmandu, Nepal: Evaluating relationship between NDVI and LST from 2000 to 2018 | |
| Aljumaily et al. | Voxel change: Big data–based change detection for aerial urban LiDAR of unequal densities | |
| CN112287059B (en) | Spatial prediction analysis method for acquiring land use change according to spatial big data | |
| CN112287058B (en) | Working method for intelligently detecting land red line through cloud computing | |
| CN114494881A (en) | Method, device and terminal for detecting remote sensing image change based on subdivision grid | |
| CN113989657A (en) | Method and device for detecting farmland range change based on invariant information sample screening | |
| CN116451900B (en) | Territorial space refined management method and system based on one graph engineering | |
| CN116151482B (en) | Method, device, equipment and medium for predicting mining earthwork of open-pit mining area | |
| CN113255593B (en) | Sensor information anomaly detection method facing space-time analysis model | |
| CN108763248B (en) | Water area space monitoring fine division method and system | |
| CN111858808A (en) | Automatic evaluation method for topographic map characteristic ground object precision based on massive real measuring points | |
| CN109657029A (en) | A kind of network platform prediction technique for project region | |
| Ahmadi et al. | Using cognitive information in the expert interface system for intelligent structuring and quality control of spatial data measured from photogrammetric or remotely sensed images | |
| CN116108312B (en) | Land change area calculation method and device based on single-image-spot result data | |
| CN116049342B (en) | Habitat quality monitoring method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |