Method and system for batch georeferencing of city maps based on map feature classification

Abstract

The present invention belongs to the field of geographic data registration technology, and discloses a method and system for batch geo-registration of city maps based on map feature classification. The method includes accurately classifying the collected target maps based on map features, and the classification includes dividing drawings with a high repetition rate of drawing content into the same category; using the classified maps in the same group, with the help of photo editor drawing software, completing unified batch processing of similar modern city maps, and loading external reference grid intersections; using the established external reference grid intersections to complete batch geo-registration of the same group of maps. The present invention is different from the idea of ​​starting from the internal elements of the map, and changes to the idea of ​​adding external reference control points to the map for geo-registration, and can also take into account the batch high-efficiency geo-registration method at the same time, with a low learning threshold, and in the field of modern city maps, it has a relatively wide popularization and promotion value.

Description

Technical Field

[0001] The present invention belongs to the technical field of geographic data registration, and in particular relates to a method and system for batch geographic registration of city maps based on map feature classification. Background Art

[0002] With the continuous acceleration of the process of archive digitization, the demand for digitization of paper historical archives (including text, pictures, maps, etc.) from all periods is facing an explosive growth trend.

[0003] The digitization of paper archives refers to the process of converting traditional paper archives from different historical periods (especially before the popularization of electronic archives) into digital formats through scanning, photographing, etc., and using computer technology for storage, management and utilization. This process usually includes archive sorting, scanning or photographing, image processing, optical character recognition (OCR), metadata recording, storage management, security protection, and provision of utilization. The digitization of paper archives not only improves the efficiency and modernization of archive management, ensures the sustainable preservation and safekeeping of precious historical archives, promotes the resource sharing of historical archive information, improves circulation efficiency and reduces dissemination costs, and plans mass information dissemination, as well as research needs for specific purposes or fields.

[0004] On the basis of scanning and storage, the digitization of paper archives of maps from historical periods faces the need for georeferencing and further vectorization to meet the wide range of interests in specialized field research (such as urban history research) and urban modeling. Especially in urban historical maps (here refers to urban maps of various historical periods), georeferencing is required before the vectorization process. Georeferencing is a process of matching image data with specific geographical locations on a specified map. The geographical features in the map image content, such as roads, buildings, infrastructure, etc., need to be compared and mapped one by one with the same geographical features on a specified modern map. These modern maps include contemporary electronic maps (digital maps), paper maps (papermaps) published by regular map publishers with precise geographical coordinate information, and a large number of online maps (such as open street maps, Google maps, Amap and other online maps (onlinemaps)). Simply put, it ensures that each pixel in the image can accurately correspond to a specific geographical location on the earth's surface. This process is crucial for remote sensing data processing, map making, geographic information systems (GIS), urban planning and other fields. Digital historical map archives that have been geo-referenced are usually placed on top of a designated modern map in the form of an overlay. The features on the two maps can be compared completely according to their real geographic spatial locations to achieve a variety of purposes such as specific historical research and contemporary restoration (reproduction).

[0005] Modern maps are the product of modern map survey and mapping technology, with clear geographic coordinate system, projected coordinate system, scale and other information. In contrast, before the birth of modern cartography, which was marked by large-scale triangulation and topographic mapping, aerial photogrammetry, map compilation and photoengraving printing technology, maps of certain historical periods, such as ancient maps, ancient maps, and city maps of the Qing Dynasty and before, did not have the prerequisites for accurate georeferencing. In contrast, modern city maps are drawings drawn on the basis of modern map survey maps with the help of modern drawing tools, using rulers and compasses, and some of them are published and disseminated through specific map printing technology. This type of paper map usually has relatively accurate geographic coordinate information and projected coordinate information, and the map content can be compared and mapped with modern urban geographic space, meeting the prerequisites for georeferencing.

[0006] A very small number of these modern city maps clearly record the geographic coordinate system and other information when the maps were drawn in the map printing or archival records. For this part of the map information, it may be possible to convert the entire drawing content to the same coordinate system as the reference map by converting and converting the geographic coordinate system of the georeferenced reference map (i.e. the designated modern map) to meet the need for accurate comparison of the map content. However, for the vast majority of modern cities, due to various historical reasons, the coordinate information was not disclosed when the maps were published or drawn, and the coordinate information was deliberately hidden, resulting in the inability to implement georeference by coordinate system conversion. Because of this, the georeferenced ideas and technical paths of modern city maps are very different from those of contemporary maps, and the time, manpower, and technical costs are often much higher than those of modern maps.

[0007] At present, manual georeferencing is time-consuming and laborious, but the cost of automated / semi-automated georeferencing technology is too high and it is still in its infancy, so it is not yet ready for large-scale promotion. How to balance the advantages and disadvantages of the two and find an efficient georeferencing method that can take into account automation or batching to a certain extent, can be applied to maps in a larger range (such as different map types), and has a low learning threshold and is suitable for large-scale promotion and use, is the current demand pain point in the field of urban historical maps. Therefore, it is of practical significance to find a breakthrough point for the georeferencing of modern urban maps based on this.

[0008] In terms of technical ideas, the current technical ideas for georeferencing of modern maps (including modern city maps) can be roughly divided into three types: georeferencing through control points (GCPs), georeferencing through chart features (chartographic charactes / elements), and georeferencing through gazetteers. The three ideas are detailed as follows:

[0009] The first method is georeferencing through control points. Control points (GCPs) refer to some points in the map whose geographic spatial locations are known. These points have precise geographic coordinates (such as longitude, latitude and altitude) on the ground in the real world. They provide a correction benchmark so that the features on the map correspond to the actual ground locations. A certain number of control points combined with the coordinate transformation settings (Transformation Settings) can complete the georeferencing of paper maps. Currently, georeferencing is performed through control points.

[0010] But at the same time, the current georeferencing path that relies on control points still has many shortcomings and room for improvement.

[0011] The first is to use control points for manual georeferencing. Control points are mostly features that remain unchanged in the actual geographic space, such as the outline points of buildings that continue to exist after they were built, as the mapping between the historical map of the city to be referenced and the contemporary map (whose precise coordinate information is known). This directly brings three problems: 1) The selection of control points mainly depends on the familiarity of the personnel implementing georeferencing with the map or the actual urban features, and the control points need to be screened and compared one by one, which requires a lot of manpower and time costs; 2) In order to achieve relatively accurate registration, there are certain requirements for the number of control points. Although conventional georeferencing does not require a certain number, usually 3-5 points are sufficient, for maps with a large map area, a considerable number of control points are required to achieve a relatively accurate and usable degree, which still requires a lot of manpower and time costs; 3) With the continuous development and evolution of the city, urban features are constantly changing, such as addition, disappearance, and change, which makes it impossible to map many points on the historical map with the contemporary map, greatly reducing the possibility of using control points in the map for georeferencing.

[0012] Secondly, automatic / semi-automatic georeferencing is performed with the help of control points to achieve fast, batch and other efficient processing purposes. At present, the methods of batch georeferencing are still relatively niche and not popular. The reasons are: first, the paper maps that need to be georeferenced have different characteristics, resulting in different ideas and specific methods of georeferencing; second, paper maps have a long history and uneven preservation quality, resulting in low pixel quality of images after electronicization, and common phenomena such as color mixing, and the resulting map technology processing costs are too high.

[0013] The second method: georeferencing through map features. Chart features (chartographic charactes / elements) usually refer to clearly recognizable graphic information or symbols on the map, such as the common survey and drawing marks "L", "X" or "T" shaped symbols. For a small number of maps, the real coordinate information of such marking symbols is known, and the marking symbols can be identified by manual or machine learning methods, and then georeferencing is directly implemented through coordinate transformation calculation. For most maps with unknown marking symbol coordinate information, the georeferencing principle is similar to that of control points, which is to find a one-to-one mapping of such symbols on historical maps and contemporary maps, and then implement georeferencing through coordinate transformation calculation. This idea also has high requirements for the map itself. It is often a geological survey map, or a longitude and latitude grid is required on the map to facilitate the capture and conversion of map features (usually "L", "X" or "T" shaped marking symbols, etc.), so the application scope is much smaller than the first control point idea.

[0014] The third method: georeferencing through a gazetteer. A gazetteer is a tool or data set for recording and managing place names. It contains the names, locations, types, and related attribute information of all geographic entities in a specific area. The main purpose of a gazetteer is to ensure the accuracy and consistency of geographic information by standardizing the use of place names, so that people can use it in map making, geographic information query, postal services, urban planning, and other activities that require precise geographic locations. It is available in paper, electronic, and online versions. The idea of ​​using a gazetteer for georeferencing is to map the place names on the map to be referenced with the external gazetteer with the help of an existing external gazetteer, and use the geographic coordinate information of the entry in the gazetteer to implement georeferencing. Currently, there are great limitations in using gazetteers for georeferencing: 1) There are currently few gazetteers available for georeferencing worldwide, so there are also fewer gazetteers available for georeferencing; 2) Most of the existing gazetteers are at global and national spatial scales, and there are very few gazetteers at provincial or even municipal scales. In addition, the spatial granularity of place names in global and national gazetteers is relatively coarse, making it impossible to establish a one-to-one mapping with local place names at the city and block scales; 3) Most gazetteers are contemporary gazetteers, and there are relatively few historical gazetteers. This means that many historical place names on urban historical maps cannot be mapped to the place names in existing gazetteers, so georeferencing cannot be implemented.

[0015] In summary, the current georeferencing paths at home and abroad are still mainly based on control points (GCPs), using map features and place names to implement automated / semi-automated georeferencing solutions, and the overall application scope is relatively small.

[0016] From the above-mentioned georeferencing technical ideas, it can be seen that no matter which idea is used, the basic focus is to capture the elements (points / blocks) in the map, including basic features such as buildings and infrastructure, as well as drawing elements such as symbols and signs. However, due to the ever-changing types, properties, and scales of maps, as well as the recognition problems caused by map pixels, georeferencing has low efficiency and high manpower, material, and time costs. Summary of the invention

[0017] In order to overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a method and system for batch geo-referencing of city maps based on map feature classification. Specifically, it relates to a method and system for batch geo-referencing of city maps based on map feature classification. The purpose of the present invention is to change the idea of ​​starting from the internal elements of the map to the idea of ​​adding external reference control points to the map for geo-referencing, and to simultaneously take into account a batch high-efficiency geo-referencing method, with a low learning threshold, and in the field of modern city maps, it has a wide range of popularization and promotion value.

[0018] The technical solution is as follows: A method for batch georeferencing of city maps based on map feature classification, comprising:

[0019] S1, classifying the collected target maps based on map features;

[0020] S2, using photo editor drawing software to process the classified maps of the same group, complete the unified batch processing of similar modern city maps, and load the external reference grid intersections;

[0021] S3, using the established external reference grid intersections, completes batch georeferencing of the same set of maps.

[0022] In step S1, the collected target maps are classified based on map features, including:

[0023] S101: Based on the time characteristics of drawing modern city maps, the first map classification is carried out;

[0024] S102: Based on the scale characteristics of modern city maps, conduct a second map classification.

[0025] In step S101, the first map classification is performed, including: the drawings of the same city construction period are classified into different categories, namely: city construction period group 1, city construction period group 2... city construction period group n;

[0026] In step S102, a second map classification is performed, including: based on different major categories and groups, further classification is performed according to the map scale in each group, and maps with the same or similar scales in each group are classified into a more detailed category, including: Group 1 with a scale of 1:100,000 and above, Group 2 with a scale of 1:50,000 and similar, Group 3 with a scale of 1:20,000 and similar, Group 4 with a scale of 1:10,000 and similar, and Group 5 with a scale of 1:5,000 and similar.

[0027] After S102 is performed, the map features of the same batch of drawings in the same group are confirmed to ensure that there are repeated overlapping parts in the map content. The repeated content includes: the outline or contour line of the urban area or block, and the outline of the urban road, block, plot or building.

[0028] In step S2, unified batch processing of similar modern city maps is completed, and external reference grid intersections are loaded, including:

[0029] S201: In the photo editor Photoshop software, the drawings of similar proportions are converted into proportions;

[0030] S202: Using the photo editor Photoshop software, redefine the drawing sheet range of each map;

[0031] S203: Taking the maximum map size added externally as the range, grid division is performed, and the entire map size is evenly divided into 9*9 grids to obtain 10*10=100 grid intersection points.

[0032] In step S201, the drawings of similar scale are converted into proportion, including: taking the standard map of the same group as a reference, scaling each map in proportion, and moving each drawing so that the same parts of the drawing contents of each map in the group are completely overlapped.

[0033] In step S202, the drawing sheet range of each map is redefined, including: selecting a sheet that is longer and wider than the largest map as the common sheet range of each map, and using the filling method of the photo editor Photoshop software to fill the drawings with smaller sheets to the specified sheet range, so that the positions of the drawings in the map are exactly the same without deviation;

[0034] The filling method of the photo editor Photoshop software includes: taking an obvious control point in the boundary of the map content as a reference point, calculating the distance from the reference point to the edge of the added external map sheet, and aligning each map with the help of the reference line tool of the Photoshop software so that the distance from the reference point to the edge of the drawing in each drawing is exactly the same.

[0035] In step S203, while obtaining 10*10=100 grid intersection points, the grid lines containing the largest drawing area are saved as a separate drawing file.

[0036] In step S3, batch georeferencing of the same set of maps is completed, including:

[0037] Use the external control points of the drawings shared by all maps in the same group as the conjugate points for georeferencing. Use the georeferencing command in QGIS software to save and share the georeferencing control points and all their attribute information. After manually georeferencing the first map in the group, the other maps in the group can be georeferenced. Figure 1 key to generate georeferencing results;

[0038] After completing batch georeferencing of the same set of maps, the root mean square error (RMSE) is used to evaluate the positioning accuracy achieved when aligning or registering a geospatial dataset with another reference dataset.

[0039] Another object of the present invention is to provide a city map batch georeferencing system based on map feature classification, and to implement the city map batch georeferencing method based on map feature classification, the system comprising:

[0040] The recognition and classification module of modern city maps is used to classify the collected target maps based on map features;

[0041] The map processing and loading external reference grid module is used to process the classified maps in the same group with the help of photo editor drawing software, complete the unified batch processing of similar modern city maps, and load the external reference grid intersections;

[0042] The batch georeferencing module for the same set of maps is used to complete the batch georeferencing of the same set of maps using the established external reference grid intersections.

[0043] In combination with all the above-mentioned technical solutions, the beneficial effects of the present invention are as follows: the present invention gets rid of the tedious manual process of manually searching and aligning control points one by one, and implements a batch geographic alignment method, which greatly improves the work efficiency of geographic alignment, regardless of the number of maps or the content of a single map, and saves manpower, material resources and time costs.

[0044] Compared with the existing technical ideas, the present invention is simple to operate and easy to use. The existing research on batch, automated / semi-automated georeferencing methods either requires a deep mathematical calculation or modeling foundation to implement complex and transformation calculation processes; or requires the use of complex programming techniques or machine learning (such as convolutional neural network technology). The technical threshold is too high for ordinary scientific researchers and ordinary map enthusiasts who are not in this professional field, and such methods are often limited to a certain type or a type of map, and have extremely high requirements for the drawing conditions of the map itself. In contrast, the present method only needs to master the basic operation steps of two conventional software (Photoshop and QGIS) to implement batch georeferencing of modern city maps, which are numerous and widely used maps, greatly improving the utilization efficiency of urban historical maps since modern times and reducing the difficulty of their promotion and application. The technical method of this invention can be widely used in direct fields involving historical maps such as geography, history, and cartography, and is also applicable to indirect disciplines involving the application of historical maps such as urban and rural planning, architecture, gardening, art, sociology, and ordinary historical map enthusiasts.

[0045] Compared with the existing technical ideas, the present invention is applicable to a wider range of maps. In modern cities where modern surveying and mapping technologies have been introduced and developed, there are a large number of historical city maps. Due to the continuity of urban construction, these maps generally contain a large amount of repeated / overlapping content, and the present invention has a relatively general applicability to such historical city maps. Among them, one advantage that is particularly worth emphasizing is that the present invention provides a convenient, feasible and efficient registration solution for the urban planning maps that exist in large numbers in modern city maps. Urban planning maps reflect the planning and envisioning schemes for urban construction at that time, of which a large part of the planning maps are further planning schemes based on the current status of urban construction, and only a small part are completely new schemes. There is no one-to-one corresponding control point between the planned content to be built in this type of urban planning map and the reference map, which results in the method of using control points to implement geographic registration of this part of the drawing content, which is either difficult to implement or has a very large registration error. With the help of the method of the present invention, for the first type of urban planning maps mentioned above (i.e., urban planning maps based on the current urban construction status), not only can geographic registration be implemented, but also batch geographic registration can be achieved, and the geographic registration error of the planning area can be greatly reduced, thereby improving the digital utilization efficiency of modern urban maps of planning type as a whole.

[0046] At the same time, because the added map external reference grid lines are of a unified standard, all maps in the same group share the same external reference points. During the georeferencing process, the cumulative error generated in the process of georeferencing each map one by one is greatly reduced, and the final spatial displacement error or coordinate conversion error is greatly reduced, which greatly improves the accuracy of georeferencing.

[0047] The batch geo-referencing technology provided by the present invention can be widely used in future digital city construction, smart city construction and other related fields. When this technology is applied, it can process more data within the same time limit, thereby greatly reducing the processing cost of unit data, while effectively reducing the investment in manpower costs, and providing indispensable basic data support for various fields. Existing geo-referencing methods mostly focus on capturing elements within the map, or rely on complex computer technology to achieve automatic / semi-automatic. This technology uses manual intervention to add the same external reference control points shared by multiple maps as the basis for unified geo-referencing. This method is simple and easy to operate, breaking through the barriers of high complexity and other existing technologies.

[0048] The present invention breaks through the limitations of traditional manual georeferencing and realizes batch operation of map georeferencing. It abandons the cumbersome process of determining control points one by one in manual georeferencing, and effectively overcomes the difficulty of identifying control points due to low resolution of drawings, further improving the accuracy and efficiency of georeferencing.

[0049] The present invention can achieve efficient geo-referencing in maps in the field of urban planning. And for those maps where it is difficult to capture control points in a certain part of the map (such as important road intersections, building outline points), or the map content cannot be mapped with contemporary maps, this technology can significantly improve the accuracy of geo-referencing, effectively making up for the shortcomings of traditional registration methods in processing such maps. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0051] Figure 1 It is a flow chart of a method for batch geo-referencing of city maps based on map feature classification provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of classifying the types of drawings to be registered provided by an embodiment of the present invention;

[0053] Figure 3 1 is a schematic diagram for comparing the range of maps in the same group provided by an embodiment of the present invention; (a) is a standard map in the group, (b) is a map with a reduced size compared to the standard map, and (c) is a map with an increased size compared to the standard map;

[0054] Figure 4 Schematic diagram of the relationship between the contents of drawings in the same group provided by an embodiment of the present invention; (a) is a standard map in the group, (b) is a drawing with added contents, (c) is a drawing with reduced contents, and (d) is a modified drawing;

[0055] Figure 5 A schematic diagram of the process of uniformly adding an external reference grid to the same group of drawings of the present invention;

[0056] Figure 6 A schematic diagram of the external reference line grid and intersection numbering of the present invention;

[0057] Figure 7 It is a schematic diagram of a city map batch geo-referencing system based on map feature classification provided by an embodiment of the present invention;

[0058] In the figure: 1. Recognition and classification module of modern city maps; 2. Map processing and loading external reference grid module; 3. Batch georeferencing module of the same group of maps. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0060] The innovation of the present invention is that the present invention can effectively achieve batch operations of georeferencing. Unlike most previous georeferencing technologies that focus on capturing map elements (points, blocks), the present invention introduces external unified reference points as the basis for georeferencing, and does not require the use of complex computer technologies or mathematical calculation formulas such as deep learning technology and Python. This feature greatly reduces the technical threshold and enables it to be widely used by researchers in different fields. At the same time, there is a relative lack of attention to existing georeferencing technologies for maps in the field of urban planning, and this method can effectively fill this gap and achieve efficient georeferencing of modern urban planning maps through batch operations.

[0061] Example 1. The ultimate goal of the present invention is to perform batch georeferencing operations on a large number (greater than 100) of modern city maps of various types in a more convenient, time-saving and efficient manner, so that they can be accurately superimposed on contemporary electronic maps (such as open street maps, Google Maps, etc.) and accurately compared with land features with accurate geospatial information.

[0062] At present, the idea of ​​automatic / semi-automatic georeferencing of historical maps is mainly based on how to efficiently capture map elements (points, blocks) and implement batch georeferencing, and there is basically no idea of ​​manual intervention to add external reference points.

[0063] The technical idea of ​​the present invention is to add reference control points outside the map by manual intervention, and the same external reference control points shared by multiple maps are used as the basis for unified georeferencing. The premise of this technical invention is to be tailored to the characteristics of modern urban historical maps. First, in the same (construction) period of each city, the content of the city map has great continuity, and the map often has exactly the same or very similar content, including legends, symbols and other drawing methods are highly similar, which provides the possibility for batch georeferencing; secondly, the city historical map produced in the same construction period often uses a certain map (such as a topographic map) as the base map, and other thematic contents are added on the basis of this map to derive a variety of thematic maps. This base map is shared by all drawings of this period, which constitutes the basis for batch georeferencing; thirdly, the city maps and their base maps in the same construction period are often drawn by the same organization or system, and the survey and drawing standards of their maps are often unified, including their geographic coordinate information. When adding external reference control points, it can be ensured that they are not affected by changes in the coordinate system of the map itself, and the same method can be used for batch georeferencing.

[0064] Therefore, the technical idea of ​​the present invention is to group maps of the same construction period and similar scale into one group, and implement batch geo-referencing within the same group by uniformly adding external reference grid lines of the maps to create common control points (i.e., conjugate points, Conjunctions).

[0065] The key points of the present invention include: 1) identifying map features and classifying them into different groups; 2) loading external grid reference lines to form conjugate points in drawings of the same category; 3) using conjugate points to achieve batch geo-registration of the same group of drawings, thereby greatly improving the map registration effect. The technical scheme and ideas of the present invention, the first and second steps are the premise and foundation of the present invention, the premise for the subsequent batch geo-registration work, and the innovation point of this invention, while the batch geo-registration work in the third step is the technical implementation focus of the present invention. The specific implementation steps and results of the simulation experiment combined with the drawing example are described as follows.

[0066] like Figure 1 As shown, the method for batch geo-registration of city maps based on map feature classification provided by an embodiment of the present invention includes:

[0067] S1, classifying the collected target maps based on map features;

[0068] The classification includes classifying drawings with high repetition rate of drawing contents into the same category, and the specific steps are as follows:

[0069] S101: Based on the map drawing time characteristics, the first map classification is performed. The goal of classification is to classify the drawings of the same city construction period into one of the major categories. The content expressed by the city map depends on the construction progress in different construction cycles of the city. City maps in the same construction cycle have many completely identical or very similar contents (including terrain and landform information, such as contour lines, elevations, etc., and urban construction content, such as streets, buildings, etc.). The division of map ages is first based on the clear age identification in the map, such as the city map contains clear drawing time information in the map title and legend, text description and other modules (such as 1893, or December 15, 1893). If there is no such information, the upper or lower limit of the map drawing time is determined based on the map archive information, such as the archive timestamp in the map, or the supporting text archive information in the archive where the map is located.

[0070] S102: Based on the scale characteristics of modern city maps, a second map classification is performed. That is, on the basis of each group in one of the major categories, further fine classification is performed in each group according to the map scale. The goal of this classification is to classify the maps with the same or similar scale in each group into a more detailed category, that is, the second major category. If the map scales are different, the fineness and granularity of the description of the objects in the map will be different. Only maps with the same or similar scales have similar or completely repeated map objects for alignment in step S2 and batch processing in step S3. From the experience of the present invention in processing modern city maps, the current drawing scales of modern city maps are roughly 1:200,000, 1:100,000, 1:50,000, 1:25,000, 1:12,500, 1:10,000, 1:7500, 1:6250, 1:5000, 1:4000, 1:2000 and other scales. According to the principle of classifying maps with the same or similar scales, maps with a scale of 1:100,000 or less are classified into one category; maps with a scale of about 1:25,000 are classified into one category; maps with a scale of about 1:10,000 are classified into one category; maps with a scale of 1:5,000 or more are classified into one category. After grouping, maps within each group of one of the major categories are further divided into Group 1 - 1:100,000 and above (Group A1), Group 2 - 1:50,000 and similar (Group A2), Group 3 - 1:20,000 and similar (Group A3), Group 4 - 1:10,000 and similar (Group A4) and Group 5 - 1:5,000 and similar (Group A5). (e.g. Figure 2 ).

[0071] After the above two levels of map classification, the modern city maps of a city can be classified into different groups, such as A1a1 group, A1a3 group, A2a4 group, etc. The drawings within each group have many common characteristics and meet the conditions for batch registration. These characteristics are:

[0072] 1) Have exactly the same geographic coordinate system;

[0073] 2) There are many overlapping parts in the map area, and only some local areas are reduced or increased. Figure 3 This is a schematic diagram for comparing the map ranges within the same group, with a map with a larger overlapping map range as a reference (e.g. Figure 3 (a) is the standard map in the group), and the range of other maps is reduced (e.g. Figure 3 (b) The size of the drawing is reduced compared to the standard drawing) or increased (such as Figure 3 The drawing in (c) is larger than the standard map).

[0074] 3) If there is a large amount of repeated content in the map, such as roads, buildings, facilities, etc., you can use software such as Photoshop to align and overlay the drawing content. Figure 4 A diagram showing the relationship between the contents of drawings in the same group, with a drawing in each group with more overlapping contents as a reference (e.g. Figure 4 (a) is the standard map in the group. The other drawings have obvious additions in terms of map content ( Figure 4 (b) The drawing content has increased) or decreased ( Figure 4 (c) the content of the drawing has been reduced), or the content has been significantly changed but the same Figure 4 (a) the same content ( Figure 4 (d) The drawing content has been changed);

[0075] Taking a historical map of a city as an example, among the maps selected for the simulation experiment of the present invention, there are cases where the map size is increased, which belongs to Figure 3 In case (c), the urban area in the east has been greatly expanded compared with the reference map; there are also cases where the content of the drawings has been increased, namely Figure 4 In case (b), compared with the reference map, the base map content in the map is almost unchanged, and only the hand-drawn content on the upper layer is added.

[0076] It should be noted that after two groupings, the same batch of drawings in the same group still need to further confirm the map features to ensure that there are repeated overlapping parts in the map content. The repeated content mainly includes: the outline or contour line of the urban area or block, urban roads, block plots, building outlines, etc. Drawings with very different map types or features (such as the content and features of urban planning maps and topographic maps within the urban area may be very different), or drawings with no repeated map content, should be eliminated separately. At a later stage, this drawing may need to be manually geo-referenced separately. However, this situation is an extremely rare case and does not affect the applicability of the method of the present invention.

[0077] S2, using photo editor drawing software to process the classified maps of the same group, complete the unified batch processing of similar modern city maps, and load the external reference grid intersections;

[0078] like Figure 5 As shown in the schematic diagram of the process of uniformly adding external reference grids to the same group of drawings, the present invention uses drawing software such as photo editor PS (Photoshop) to complete the unified batch processing of similar modern city maps after the same group of maps classified in step S1, and loads the external reference grid intersections, which specifically includes:

[0079] S201: In the photo editor Photoshop software, the drawings of similar scale are converted in proportion. That is, the standard map of the same group is used as a reference, and the other maps are scaled in the same proportion, and the drawings are appropriately moved and aligned, and finally the same parts of the drawings of the maps in the group are completely overlapped;

[0080] S202: Redefine the drawing sheet range of each map with the help of photo editor Photoshop software. You can choose a sheet that is slightly longer and wider than the largest map as the common sheet range of each map, and use the fill function of Photoshop software to fill the smaller sheets to the specified sheet range. At the same time, ensure that the position of the drawings in the map is exactly the same without deviation. The method here is to use the obvious control point in the boundary of the map content as a reference point, set its distance to the edge of the added external sheet, and ensure that the distance from this reference point to the edge of the drawing in each drawing is exactly the same.

[0081] S203: Taking the maximum map size added externally as the range, grid division is performed to divide the entire map size into 9*9 grids, so as to obtain 10*10=100 grid intersection points ( Figure 6 Schematic diagram of external reference line grid and intersection numbering). At the same time, the grid lines containing the largest drawing area must also be saved as a separate drawing file "T (Grids)" (such as Figure 6 for subsequent use.

[0082] After the above steps, each map in the same group has exactly the same map size, and the repeated drawing contents in each map can theoretically be completely aligned (in actual situations, there are certain errors due to various reasons such as hand-drawing errors and scanning errors, see the simulation experiment steps and contents below for details), and the grids in each map are exactly the same, and the positions and numbers of grid intersections are exactly the same. Figure 6 The 100 grid intersections in are the conjugate points (Conjunctions) for batch georeferencing of various maps in the subsequent step S3.

[0083] Exemplarily, the simulation experiment of step S2 includes:

[0084] In order to truly present the above-mentioned ideas of the present invention, the present invention takes a modern city map of a certain city as an example, selects a part of the standard street map drawings of a certain city in T01-T06 during the construction period of the 1930s for simulation experiments, and the drawing scale is 1:10000, and selects a part of the urban map drawings of a certain city in T07 during the construction period of the 1920s for simulation experiments, and the drawing scale is 1:10000; among them, for the maps of a certain city in this period, there is no drawing scale similar to 1:10000 in each group, and the case of this simulation experiment only involves one drawing scale; furthermore, the construction periods of other drawings in the T07 map area are slightly different, but because the drawings are of similar age, the urban construction conditions are not much different, and the drawing contents are repeated a lot, they are classified into one type of drawings.

[0085] The simulation experiment is carried out according to the schematic steps of step S2 above, and the steps and results are as follows:

[0086] Step 1: Install Photoshop. You can install Photoshop by following the download files and installation steps on the Adobe official website.

[0087] Step ②: Create a new Photoshop file. The drawing size of this simulation experiment is 12600 pixels × 8100 pixels, and the drawing resolution is 200 pixels / inch. On this basis, the map is summarized according to the map scale of 1:10,000. The scale calculation formula is:

[0088] ;

[0089] Where S is the map scale, is the measured distance in the map, The actual distance in reality. Here you can select the building outlines or streets that have existed in the city since its construction as the distance measurement value, and use the actual distance in the real world as the benchmark, convert it at a ratio of 1:10000, and determine the distance measurement value of the corresponding map in Photoshop.

[0090] Select the T01 map in the same group as the standard map, which is the first map to be processed in step S3 (the drawing number is T01). At the same time, use the perspective clipping tool in Photoshop software to deal with the perspective deformation of the map due to scanning. Other drawings in the same group are also corrected for map perspective deformation according to this method.

[0091] Step 3: Compare the content of other maps in the same group with the standard map and implement layered overlay. First, modify the transparency of the upper map, use the move tool in Photoshop to adjust the position of the upper map so that the content of the upper map overlaps with the content of the standard map. The goal is to roughly align the other maps in the experimental group with the standard map.

[0092] During this process, if there are maps of similar scale in the same group, you need to refer to the standard map in this group and scale the drawings in the same proportion (see Figure 5 , or as shown in the steps below).

[0093] Step 4: Add a background layer and select a map that is slightly longer and wider than the largest map as the common map for all maps.

[0094] Step 5: In PS, use the largest background map as the range to divide the grid. Use the "View - New Reference Line Layout" tool in the PS toolbar to set the number of columns and rows of the reference line grid to 9, and divide the entire map into a 9*9 grid to obtain 10*10 grid intersections. Then use the pen tool in Photoshop to draw a grid along the reference line to obtain the external reference line grid of the final map. In the subsequent registration process, each map shares the same grid, and these 100 intersections are the conjugate points (Conjunctions) for the subsequent geo-registration of each map.

[0095] Step 6: Save the grid lines with the maximum map size as a separate drawing file "T (Grids)" and save the file in JPG format. Export all map files with the maximum map size and external reference grid lines (T01-T07) separately, save the files in JPG format, and store them in the same folder for subsequent use.

[0096] The final result of the above steps is that each map in the same group has exactly the same background map, and its map content (at least the common map content, such as the base map) is basically aligned. The 9*9 grids in each map are exactly the same, and the positions of the 100 grid intersections are exactly the same.

[0097] Since the above maps were drawn in the 1930s, they involve a lot of hand-drawn content, and the maps will have problems such as displacement and unclear pixels during the scanning and electronicization process. Therefore, the maps in the same group may not be 100% accurately aligned during the alignment and superposition process. In this regard, the requirements can be appropriately relaxed according to the final drawing application requirements, and the position error can be within the allowable range. Here, taking the simulation experiment as an example, 10 points were randomly selected from the above 7 maps in the same group (the selected points are consistent with the error verification points selected for georeferencing in the third module to ensure that the errors in each stage can be compared and referenced) for error verification. The 10 points in the T01 map are used as reference points, and the distance error values ​​of the 10 points in the other 6 groups of maps in the same group and the reference points are measured. The results are shown in Table 1. According to the empirical value of the error calculation results, if the final result is for map browsing and naked eye retrieval, the average distance of the error point of the target point can be controlled within 30 pixels during the alignment process using Photoshop software.

[0098] Table 1. Pixel error between the six maps in the same group and the standard map (unit: pixel (px))

[0099]

[0100] S3, using the established external reference grid intersections, completes batch georeferencing of the same set of maps.

[0101] The technical idea is to use the external control points of the drawings shared by all maps in the same group as conjugate points for georeferencing. The georeferencing command of QGIS software can save and share the georeferencing control points and all their attribute information. After manually georeferencing the first map in the group (ie the standard map), other maps in the group can generate georeferencing results with one click.

[0102] According to the above technical ideas, the same group of map instances involved in S3 are simulated, and the steps and results are as follows:

[0103] Step 1: Download and install the software according to the prompts on the software official website. The present invention uses QGIS 3.38.3 version software as an example for simulation experiments.

[0104] Step 2: Open QGIS software and create a new project file. Use Google Maps loaded by QGIS plug-in HCMGIS as the modern reference map for alignment, and set the corresponding map coordinate system parameters according to the specific project requirements. The geographic coordinate system used in this simulation experiment is the WGS84 coordinate system, and the projection coordinate system is the Pseudo-Mercator coordinate system.

[0105] Another example, step 2 includes: opening the QGIS software interface, opening the menu bar project, and selecting New from the drop-down options to create a new project file. Then open the menu bar QGIS plug-in HCMGIS, select Basemaps and Google Maps from the drop-down options, and load Google Maps as a modern reference map for georeferencing. At the same time, according to the specific project requirements, set the corresponding map coordinate system parameters, open the EPSG:3857 option in the lower right corner of the QGIS interface, and select the engineering coordinate reference system in the pop-up engineering properties-CRS interface. The coordinate reference system used in this simulation experiment is EPSG-3857-WGS84, and the projection coordinate system is the Pseudo-Mercator coordinate system.

[0106] Step 3: Georeference the first standard map of the same group (T01). Open the QGIS software interface, open the menu bar layer (Layer), select the Georeferencer tool from the drop-down options, and perform the same group standard map (T01) reference. Open the Georeferencer interface;

[0107] Another example, step 3: georeference the first standard map (T01) of the same group. In the QGIS software interface, open the menu bar layer (Layer), select the georeferencer tool (Georeferencer) from the drop-down options to open the georeferencer interface;

[0108] The interface has a toolbar with a chart, and the icons related to georeferencing are from left to right:

[0109] (1) “Open raster”;

[0110] (2) “open vector”;

[0111] (3) Start georeferencing

[0112] (4) Generate GDAL script;

[0113] (5) Load ground control points (Load GCP points);

[0114] (6) Save GCP points as;

[0115] (7) Transformation settings;

[0116] (8) Add control point (add point);

[0117] (9) Delete control point (delete point);

[0118] (10) Move control point (move point).

[0119] Among them, the (8) add control point, (9) delete control point and (10) move control point commands in the interface are used to perform operations respectively, and important road intersections and building outline points in the standard map are selected as control points. This manual georeferencing randomly selects 70 relatively evenly distributed control points for georeferencing and maps their coordinates to the Google Maps selected above. In this simulation experiment, the linear method is selected as the transformation type in the transformation parameter setting of QGIS georeferencing (there are 7 transformation types in the QGIS georeferencing transformation setting interface, namely Linear, Helmert, Polynomial1, Polynomial2, Polynomial13, Thin Plate Spline and Projective).

[0120] In another exemplary embodiment, (select the menu bar "(1) Open Grid" option to open the standard map (T01) JPG file saved in step S2 (6). After the map is loaded, select the menu bar "(8) Add Control Point" option to select important road intersections and building outline points in the standard map as control points. In the pop-up input map coordinate interface box, click the "From Map Canvas" option, and then click the corresponding position of the selected control point in the Google Maps interface to add the Google Maps to the map. The coordinates in Maps are mapped to the control points of the standard map (T01). Using this method, 70 relatively evenly distributed control points are manually randomly selected in the standard map (T01). During this period, the selected control points can be adjusted using the menu bar "(9) Delete control points" and "(10) Move control points" options. After the control points are selected, open the menu bar "(7) Transformation settings" option, and adjust the transformation parameters and output settings in the pop-up transformation settings interface box. In the transformation parameter settings, linear is selected as the transformation type (in the QGIS georeferencing transformation settings interface, there are 7 transformation types, namely Linear, Helmert, Polynomial1, Polynomial2, Polynomial13, Thin Plate Spline, Projective), and WGS 84 is selected as the target CRS. In the output settings, the output file location is set, and other settings remain the default. After completing the above steps, select the menu bar "(3) Perform georeferencing" option to complete the registration of the first standard map (T01).

[0121] Step 4: Continue to use the QGIS Georeferencer command to align the external reference grid map "T(Grids)", and complete the georeference of points 0 to 99 one by one from left to right and from top to bottom. The goal of this georeference is to align the 100 control points in the T(Grids) map with the corresponding 100 grid intersections in the T01 map that has been aligned in step 3 above. And export the aligned control points to the control point file "Controls" (this file format is the ".points" suffix format) using the "Save Control Points As" command in the QGIS Georeferencer window interface.

[0122] Another example, step 4: continue to use the QGIS registration tool command to register the external reference grid map "T(Grids)". Select the menu bar "(1) Open Grid" option to open the reference grid drawing "T(Grids)" JPG file saved in step S2, step ⑥. Select the menu bar "(8) Add Control Points" option, click the intersection points of the grid one by one from left to right and from top to bottom, click the "From Map Canvas" option in the pop-up input map coordinate interface box, and then click the corresponding position of the control point on the standard map that has been registered in step 3 to map the coordinates of Google Maps to the control point of the grid. According to this method, add 0 to 99 control points one by one. After the control point selection is completed, select the menu bar "(7) Transformation Settings" option, and in the pop-up transformation settings interface box, adjust the transformation parameters and output settings to be consistent with step 3. Finally, select the menu bar "(3) Perform Geo-Registration" option to complete the registration of the external reference grid map "T(Grids)". After the registration is completed, select the "(6) Save Control Points As" option in the menu bar, set the file name and save type (.points) in the pop-up Save Ground Control Points interface box, and save the control point file "Controls" (this file format has the suffix ".points") of the external reference grid map for subsequent map registration.

[0123] Step 5: Continue to use the Georeferencer command of QGIS, use the "Open Raster" command to call in the second map (T02) of the same group in step S2, and use the "Load Control Points" command in the Georeferencer window interface of QGIS to import the point file C in step 4 above. Because all maps in the same group share an external reference grid line (that is, all maps in the same group have 100 conjugate points), the source map point coordinate information in the "Controls" file can be directly used by the second map, and the target point coordinate information is the real coordinate information of the point, which saves the step of re-finding and obtaining control points for the second map, which is equivalent to generating the control points and coordinate information of the second map with one-click operation. On this basis, set the corresponding parameters of georeferencing, such as the registration transformation options, and then click "Execute Georeferencing" to complete the registration of the second map.

[0124] Another example, step 5: continue to use the QGIS registration tool command to register the second map (T02). Select the menu bar "(1) Open Raster" option to open the second map (T02) JPG file saved in step 6 of step S2. Select the menu bar "(5) Load Control Points" option, and open the point file "Controls" saved at the end of step 4 in the pop-up loading ground control point interface. After loading the ground control points, the ground control point list can directly have the coordinate information of the external grid intersection points of the second map (T02). Because all maps in the same group share an external reference grid line (that is, all maps in the same group have 100 conjugate points), the point coordinate information in the "Controls" file can be directly used by the second map, thus eliminating the step of re-searching and obtaining control points for the second map, which is equivalent to generating the control points and their coordinate information of the second map with one click. After adding control points, select the menu bar "(7) Transformation Settings" option, and in the pop-up transformation settings interface box, adjust the transformation parameters and output settings to be consistent with step 3. Finally, select the "(3) Perform Georeferencing" option in the menu bar to complete the georeferencing of the second map (T02).

[0125] Step 6: Follow the method in step 5 to align the remaining maps in the same group (T02, T03, T04, T05, T06 and T07) one by one, and save the geo-referenced point files (.points) of each map one by one.

[0126] Another example, step 6: follow the method in step 5 to align the remaining maps in the same group (T02, T03, T04, T05, T06 and T07) in sequence. After each map is aligned, select "(6) Save Ground Control Points As" in the menu bar, set the file name and save type (.points) in the pop-up Save Ground Control Points interface box, and save the geo-referenced point files of each map one by one.

[0127] The above steps can complete the geo-reference of all drawings in the same group. Except for the first map in the same group, which is manually geo-referenced, all other maps have achieved batch geo-reference.

[0128] It should be noted that currently almost all batch georeferencing processes inevitably require manual georeferencing as an auxiliary means. Due to uncontrollable factors such as map deformation, there is still a possibility that the registration effect of individual maps is poor in the batch registration effect. Therefore, after completing the georeferencing of all maps in the same group, it is still necessary to further check based on the final registration effect, pick out the drawings with large georeferencing errors, and perform separate manual georeferencing.

[0129] Exemplarily, the geo-referencing effect of the present invention is verified as follows:

[0130] At present, the common practice for verifying the effect of georeferencing is the root mean square error (RMSE), which is a calculation method to measure the accuracy of geospatial data in spatial position. In georeferencing, RMSE is used to evaluate the positioning accuracy achieved when aligning or aligning a geospatial dataset (such as a map, remote sensing image, etc.) with another reference dataset (such as ground control points, high-precision maps, etc.).

[0131] Specifically, the root mean square error (RMSE) is calculated as:

[0132] ;

[0133] in, Indicates the reference data set The actual coordinates of the point, Represents the first The estimated coordinates of the point, is the number of data points involved in the calculation.

[0134] The smaller the RMSE value, the smaller the spatial position difference between the georeferenced data and the reference data, and the higher the georeferenced accuracy. Therefore, RMSE is one of the important indicators for evaluating the quality of georeferenced data.

[0135] Taking the above-mentioned root mean square error statistic as the standard, the error verification of 7 maps in the simulation experiment of this invention was carried out, and compared with the RMSE empirical value of existing research to illustrate the geographic registration effect of the present invention.

[0136] The specific steps are as follows:

[0137] Step i: Taking the map in the above simulation experiment as an example, 10 points are randomly selected for verification in each registered map in the same group. In order to facilitate comparison and reference with the alignment and superposition effect of Photoshop software in the second module of the present invention, this simulation experiment selects the same 10 points as those in the second module (these points are significant spatial points such as building outline points or road intersections that still exist today), and the 10 control points are distributed as evenly as possible in the map sheet. These 10 points are used as control points to be verified to verify the registration error.

[0138] Step ii: Get the actual coordinates of these ten points in each registered map. Taking point 1 as an example, the estimated coordinate values ​​of this point in all registered maps are (φt01, λt01), (φt02, λt02), (φt02, λt02), (φt03,λt03), (φt04, λt04), (φt05, λt05), (φt06, λt07) and (φt07, λt07). The coordinate information of each point is shown in Table 2.

[0139] Step iii: Get the actual coordinate values ​​of these 10 points in the real world, taking the first point as an example, that is, (φ0, λ0), see Table 2.

[0140] Table 2. Actual and estimated coordinates of 10 points in each map in the same group

[0141]

[0142] Step iv: According to the above formula for calculating the root mean square error (RMSE), calculate the RMSE value of each map (see the last row of Table 3) and the RMSE value of the estimated coordinates of the same point in different maps (see the last column of Table 3).

[0143] Table 3. RMSE calculation results

[0144]

[0145] Table 3 shows the RMSE calculation results of each map, as well as the RMSE calculation results of the same checkpoint in different maps. The RMSE values ​​of each map range from 26.329m to 72.089m. Among them, the T07 drawing was designed in a different construction period than other maps, and the error was also the largest (72.089). If T07 is used as a control group, it can be said that the batch georeferencing of maps in the same construction period has higher registration accuracy and better results. If we look at the RMSE values ​​of a single point in different maps, it ranges from 13.326m to 76.761m. Looking at the 7 maps of the simulation experiment, the average RMSE value of their georeferencing is 40.4m. If the T07 map with a large error is removed, the average value of the georeferencing RMSE values ​​of the remaining 6 maps is even smaller, at 35.1m.

[0146] Table 4 is the empirical reference value of RMSE for georeferencing collected so far. It can be seen from the table that the map scale feature will directly affect the georeferencing accuracy error of the map. For example, Howe N. (2019) et al. used map instances of about 1:2.5 million to 1:1.14 million for georeferencing, and its RMSE value was between 4-12 km; BAIOCCHI V. et al. (2013) used map instances of 1:5 thousand for georeferencing, and its RMSE value was between 0.55 km and 3.66 km. The map instance used in the present invention is 1:10,000, and the average value of the final RMSE value is 40.4 m. At present, no georeferencing project with the same map scale as the present invention has been found, but the basic principle of the distance from the estimated coordinate value of the verified point to the actual coordinate value is applicable to all current georeferencing projects. Compared with the unified error calculation standard RMSE, the georeferencing error table of the present invention is smaller than the values ​​in Table 4, and the registration effect is ideal. Therefore, the batch geo-referencing method of the present invention can achieve the expected geo-referencing effect and can basically meet the needs of subsequent different application scenarios.

[0147] Table 4. Geo-referenced error reference values

[0148]

[0149] The work of the above three modules is the design scheme for batch geo-registration of modern city maps. The idea of ​​the scheme is to classify the map features of modern city maps, and use the characteristics that maps of the same city construction period and similar scales have more identical or very similar features, classify drawings with a large amount of identical or repeated content into one category, and then use subsequent software processing and operation to achieve the effect of batch geo-registration. The number of maps processed in batches is related to the number of specific maps in the same group, depending on the city and map conditions, which may be 10 or 50 or more. In other words, after classification according to the characteristics of the city's historical maps, the more map drawings in the same group, the more efficient the batch geo-registration method used in the present invention, the better the effect, and the higher the promotion value.

[0150] The biggest advantage of this invention is that for maps in the same group, only the first standard map needs to be manually geo-referenced, and the remaining maps (regardless of the number) can be registered with one click. Taking a certain urban area in the urban case involved in the simulation experiment of this invention as an example, according to the inventor's manual geo-reference experience, a map range of 30 square kilometers requires 60-110 control points to achieve a good geo-reference effect for each map, which requires a lot of manpower and time costs. The method described in this invention greatly improves the geo-reference effect of maps in the same group. At the same time, in the same city, city maps with similar construction periods can also consider merging map groups to achieve batch geo-reference in different groups, further improving the registration efficiency. In addition, because the added map external reference grid lines are of a unified standard, all maps in the same group share the same external reference points. In the process of geo-reference, the spatial displacement error or coordinate conversion error generated during point-by-point reference is greatly reduced, and the accuracy of geo-reference is greatly improved.

[0151] Embodiment 2, as Figure 7 As shown, a city map batch georeferencing system based on map feature classification includes:

[0152] The recognition and classification module 1 of modern city maps is used to classify the collected target maps based on map features, wherein the classification includes classifying drawings with high repetition rates of drawing contents into the same category;

[0153] The map processing and loading external reference grid module 2 is used to complete the unified batch processing of similar modern city maps with the help of photo editor drawing software after the same group of maps are classified, and load the external reference grid intersection points;

[0154] The batch geo-referencing module 3 of the same set of maps is used to complete the batch geo-referencing of the same set of maps by using the established external reference grid intersections.

[0155] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for batch georeferencing of city maps based on map feature classification, characterized in that: By using manual intervention, we add the same external reference control points shared by multiple maps as the basis for unified georeferencing, without the need for deep learning technology, Python's complex computer technology or mathematical calculation formulas, and achieve efficient georeferencing of modern urban planning maps through batch processing; The method includes: S1, classifying the collected target maps based on map features; S2, using photo editor drawing software to process the classified maps of the same group, complete the unified batch processing of similar modern city maps, and load the external reference grid intersections; S3, using the established external reference grid intersections to complete batch georeferencing of the same set of maps; In step S1, the collected target maps are classified based on map features, including: S101: Based on the time characteristics of drawing modern city maps, the first map classification is carried out; S102: Based on the scale characteristics of modern city maps, conduct a second map classification; After S102, for the same batch of drawings in the same group, the map features are confirmed to ensure that there are repeated overlapping parts in the map content, and the repeated content includes: the outline or contour line of the urban area or block, the outline of the urban road, block, plot or building; In step S2, unified batch processing of similar modern city maps is completed, and external reference grid intersections are loaded, including: S201: In the photo editor Photoshop software, the drawings of similar proportions are converted into proportions; S202: Using the photo editor Photoshop software, redefine the drawing sheet range of each map; S203: Taking the maximum map size added externally as the range, grid division is performed, and the entire map size is evenly divided into 9*9 grids to obtain 10*10=100 grid intersection points; In step S201, the drawings of similar scale are converted into proportions, including: taking the standard map of the same group as a reference, scaling each map in proportion, and moving each drawing so that the same parts of the drawings of each map in the group are completely overlapped; In step S202, the drawing sheet range of each map is redefined, including: selecting a sheet that is longer and wider than the largest map as the common sheet range of each map, and using the filling method of the photo editor Photoshop software to fill the drawings with smaller sheets to the specified sheet range, so that the positions of the drawings in the map are exactly the same without deviation; The filling method of the photo editor Photoshop software includes: taking an obvious control point in the boundary of the map content as a reference point, calculating the distance from the reference point to the edge of the added external map sheet, and aligning the maps with the help of the reference line tool of the Photoshop software so that the distance from the reference point to the edge of the drawing is exactly the same in each drawing; In step S203, while obtaining 10*10=100 grid intersection points, the grid lines containing the largest drawing area are saved as a separate drawing file; In step S3, batch georeferencing of the same set of maps is completed, including: Use the external control points of the drawings shared by all maps in the same group as the conjugate points for georeferencing, and use the georeferencing command of QGIS software to save and share the georeferencing control points and all their attribute information. After manually georeferencing the first map in the same group, the georeferencing results for other maps in the group can be generated with one click.

2. The method for batch georeferencing of city maps based on map feature classification according to claim 1, characterized in that: In step S3, it also includes: After completing batch georeferencing of the same set of maps, the root mean square error (RMSE) is used to evaluate the positioning accuracy achieved when aligning or registering a geospatial dataset with another reference dataset.

3. The method for batch georeferencing of city maps based on map feature classification according to claim 1, characterized in that: In step S101, the first map classification is performed, including: the drawings of the same city construction period are classified into different categories, namely: city construction period group 1, city construction period group 2... city construction period group n; In step S102, a second map classification is performed, including: based on different major categories and groups, further classification is performed within each group according to the map scale, and maps with the same or similar scales in each group are classified into a more detailed category, including: Group 1 with a scale of 1:100,000 and above, Group 2 with a scale of 1:50,000 and similar, Group 3 with a scale of 1:20,000 and similar, Group 4 with a scale of 1:10,000 and similar, and Group 5 with a scale of 1:5,000 and similar.

4. A city map batch georeferencing system based on map feature classification, characterized in that: The system implements the method for batch georeferencing of city maps based on map feature classification as described in any one of claims 1 to 3, and the system comprises: The module for recognizing and classifying modern city maps (1) is used to classify the collected target maps based on map features; A map processing and loading external reference grid module (2) is used to process the classified maps of the same group with the help of a photo editor drawing software, complete the unified batch processing of similar modern city maps, and load external reference grid intersections; The batch geo-referencing module (3) of the same set of maps is used to complete the batch geo-referencing of the same set of maps by using the established external reference grid intersection points.

Images