JP2003302898A - Map analyzing device and program for realizing the same - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a program for highly accurately analyzing a complex scene image pickup picture by adaptively using map data of various accuracy. <P>SOLUTION: The program for adaptively carrying out the optimum analysis in accordance with the accuracy or the difference in the accuracy of a map and a picture is realized by calling up 102 the scene image pickup picture 100 and the map 101, using the attribute information of the scene image pickup picture and the map to discriminate 103 the accuracy of the map to the picture, carrying out detailed shape analysis 104 when the map is discriminated to be high in accuracy, carrying out approximate shape analysis 105 when the map is discriminated to be low in accuracy, and outputting 106 the result of analysis. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method, a system, and a program for realizing a system for detecting a target object by analyzing a landscape image such as a satellite image or an aerial photograph by a computer, and more particularly to using map data. Involved in analysis processing.

[0002]

2. Description of the Related Art In recent years, high-resolution satellite images of over 1 m have become widely available, and it has become possible to obtain information on small-scale features and moving objects not only from aircraft but also from satellites. The image data of these high-resolution satellites is provided in a digital format suitable for computer processing, so it is necessary to manually check each one and perform a huge amount of manual work. Higher work support is expected. However, it has become clear that the high resolution results in an abundance of information, but at the same time, an object is too visible to form a very complicated image. As one of the approaches to solve this, instead of using the image alone, map information is used together as knowledge about the image, and the information is comprehensively analyzed.
There is a method called. For example, if you use a map figure and its attribute information, you can narrow down the analysis target range appropriately,
It is possible to perform efficient analysis processing only within the attention area. Here, particularly in the analysis of high-resolution images, the accuracy of the map data used for the analysis becomes a problem. For example, the accuracy of generally available map data is often relatively low for high-resolution satellite images, etc., and the shape of map graphic data is often schematic. If this is used for the analysis process as it is, there is a problem that many erroneous detections occur near the boundary of the attention area due to the difference in accuracy between the image and the map. In order to avoid this problem, in the example of the conventional map use analysis process shown in FIG. 10, an analysis target range exclusion mask 1000 is created using a boundary map, and an enlarged mask 1001 in which the mask region is expanded is used. Therefore, the analysis was performed with suppressed false detection. However, since the “near the boundary of the attention area” 1002, which is often important in business, is excluded from the analysis target range, there is a problem in use that the analysis information of the portion most interesting to the user cannot be obtained. there were.

[0003]

SUMMARY OF THE INVENTION In order to solve the above problems, the present invention adaptively uses map data of various accuracies to analyze a complex landscape image with high accuracy and uses a map-based image analysis. It is an object to provide a method, a system, and a program for realizing the system.

[0004]

In order to achieve the above object, the outline of the invention disclosed in the present application will be described. The map of the acquired range of the captured image is called from the storage means, and the attribute information of the map and the image are acquired. Disclosed is a map analysis method for controlling the subsequent processing according to the result of determining the map accuracy using the attribute information of. More specifically, when the determination result is high precision, detailed shape analysis processing is performed using the map and the image, and when the determination result is low precision, the rough shape is used using the map and the image. An analysis process is performed, and the result of the analysis process is output to the output means.

[0005]

BEST MODE FOR CARRYING OUT THE INVENTION An embodiment of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a diagram showing an example of a processing procedure in an embodiment of the present invention. Image data 100 to be analyzed by a predetermined method
Is acquired and the range of the image is specified from the attribute information and the like. Further, the map data of the image range is called 102 from the map data 101 managed by the storage means and its attribute information. Here, depending on the case, there may be multiple map data in the same area, but the highest precision or the latest map data is automatically selected by referring to the map attribute information, or selected by the user. Recall the map data most suitable for identifying the area of interest. Next, using the image attribute information and the map attribute information, the accuracy of the map data with respect to the image data is determined 103.
As a result of the accuracy determination, if the map data is sufficiently accurate as compared with the accuracy of the image data, the detailed shape analysis processing 10
4 is executed, and if the map data has a lower accuracy than the accuracy of the image data, the rough shape analysis processing 105 is executed. Finally, the obtained analysis result is output to the output means 106. With the above processing procedure, small scale low precision maps, large scale or user-maintained high precision maps, images of various resolutions, etc. can be optimized according to the accuracy of the map and images used for analysis and their differences. The processing can be adaptively processed. That is, when a high-precision map is used, the target object detection process can be performed using the detailed shape of the map figure as a mask. In addition, when the low-precision map is used as a mask, the information that prompts the user to make an appropriate judgment can be provided together with the detection result in consideration of the possibility of erroneous detection that occurs near the boundary that is practically important. That is, it is possible to present highly reliable analysis results even when using maps with various accuracies. Here, an example of a method for determining the accuracy of map data will be described. The attribute information that can be used for determining the accuracy of the map includes accuracy index (positional accuracy, scale), reliability (rating, high-confidence-confirmed flag information given by the user after confirming the reliability of the map data), and the like. is there. Above all, information about the creation and modification of map data is important in the rating evaluation of reliability, and the worker or work organization, time information (latest, old), method of creation or modification (manual input by the user, existing map). A copy of the database, reading of a paper map), etc. can be used as the judgment material. Further, as the attribute information indicating the accuracy of the image, there are an accuracy index (resolution, position accuracy), shooting information (shooting organization, shooting date and time), and the like. As a method for determining the accuracy by integrating these attribute information, quantitative evaluation comparing the accuracy index of the map and the image, or "the reliability is A rank"
Various methods such as qualitative evaluation can be considered. In particular, by prioritizing the image analysis map database maintained and operated by the user by qualitatively evaluating the workers and the work organization based on the premise that "the map data entered or modified by the user is reliable". You can also By these determinations, it becomes possible to adaptively select and execute the processing function according to the contents of the map data or the image data, or the business form of the user. The contents of the analysis process will be specifically described below. In the following description, an analysis program for identifying a sea area on an image as a region of interest using coastline map data, detecting a target ship from the region and grasping the ship traffic situation will be described as an example. Is not limited to this embodiment. In addition to this, it can be used for various purposes such as using maps of roads, administrative fields, land use, etc., detecting moving objects such as vehicles and aircraft, detecting features and specific natural phenomena. FIG. 2 is a diagram showing an example of the detailed shape analysis processing according to the embodiment of the present invention. The image in FIG. 2A is an example of an image in which the sea area 200 and the land area 201 that are the attention areas and the coastline 202 that is the boundary between the sea area 200 and the land area 201 are photographed. In order to correctly analyze the ship 203 docked at the berth, the jetty 204, and the ship 205 offshore, it is necessary to correctly specify the sea area 200 that is the attention area. Here, according to the detailed shape analysis processing using the high-accuracy coastline map 210, as shown in FIG. 2B, by setting only the sea area 211, which is the attention area, as the analysis area, the jetty 212 and the land area The inside noise can be excluded, and the berthing ship 213 and the offshore ship 214 can be correctly detected. That is, when an image is analyzed using a high-precision map, detailed analysis processing such as detection of a target object near the map boundary becomes possible. Further, FIG. 2B described above is an example of an output process of outputting the information of the detected target object to the detection position on the image or the map. This makes it possible to present the analysis processing result with high visibility. FIG. 3 is a diagram showing an example of a schematic shape analysis process according to the embodiment of the present invention. As a result of the above-described map accuracy determination processing, when the map of the attention area has low accuracy, the following rough shape analysis processing is executed. That is, in analyzing the image shown in FIG. 3A, the rough sea area 301 is specified by the low-precision coastline map 300 as shown in FIG. 3B. When this is processed as an attention area, fine misdetection 302, berthing ship 303, jetty 304, long misdetection 305 along the coastline, offshore ship 306, etc. are detected. As a remarkable difference from the case where a detailed map is used, there is a problem that erroneous detection occurs near the map boundary, and the erroneous detection of a part thereof reduces the reliability of the analysis information as a whole. Therefore, in the present invention, flag information indicating that detection is performed near the boundary is added to the analysis results near the boundary to improve the reliability of the analysis information. Figure 3 (c) shows
Boundary vicinity detection results 310 to 3 to which the flag information is added
A mark indicating that there is a possibility of false detection, such as “▲”, is displayed for No. 13, and conversely, the mark is not displayed for the offshore vessel 306 that has a low possibility of false detection, so that the user's attention is drawn. It is an example of display output.
By referring to this flag information, the user can make an appropriate judgment regarding the reliability of the detection result, and the work efficiency can be improved by narrowing down the detection result to be visually confirmed. FIG. 4 is a diagram showing an example of a false detection removal process in the vicinity of a map boundary according to the embodiment of the present invention. This is to improve the accuracy of the analysis by efficiently removing the false detection detected near the boundary by referring to the above-mentioned flag information. For example, the low-precision map 4 shown in FIG.
In the analysis processing using 00, the offshore vessel 404 and the like can be correctly analyzed, but various false detections such as the vessel 401 including a part of the land area, the part of the vessel 402, and the land area 403 can be performed near the boundary of the attention area. Will occur. Therefore, only the detection result of the target to which the flag information is added, that is, the detection result in the vicinity of the land boundary where there is a possibility of erroneous detection, is applied the analysis determination processing for verifying in detail whether or not the erroneous detection. As a result, the detection result 410 including the ship and the partial ship 411
Is adopted and the land area 412 is excluded, so that the accuracy of the analysis can be improved. In addition, as shown in FIG. 4 (b), the “▲” mark indicates that the above-mentioned ship is an offshore ship 41.
3 is displayed separately from the “●” mark or the like, so that highly reliable and highly visible analysis information can be presented. As a result, the flag information is not limited to merely providing information to the user, but can be utilized in analysis processing to improve analysis accuracy. Furthermore, since detailed analysis determination processing is performed focusing on the detection result near the boundary, it is possible to perform high-precision analysis processing at high speed as compared with the case where the same processing is performed for all detection results. Here, various methods are conceivable for the analysis determination processing, but if there is knowledge of the shape of the target object, a method of determining it as a template is conceivable. For example, if you create a vessel shape template and apply it to a detected target, and the degree of agreement is high,
If it is low, it can be evaluated as false detection. As a result, FIG.
In the example of the land area false detection 420 of (c), since the degree of coincidence is low, it can be excluded as an false detection. Further, the partial ship of FIG. 4D is divided at the center of the hull by the low-precision map 400, but a part 421 that partially matches the template.
Therefore, in this example, the area is determined to be a ship together with the area 422 outside the attention area, that is, inside the land mask. Also,
A ship including a part of the land area in FIG. 4E adopts a part matching the template as the ship 423, and the remaining land area 42.
In this example, 4 is removed as noise. In the rough shape analysis process, the target object detected near the boundary is identified by the flag determination, the detailed analysis determination process is performed on the detected target object, and erroneous detection near the boundary is suppressed. Even when a low-precision map is used, high-speed analysis processing can be realized while suppressing erroneous detection near the boundary. FIG. 5 is a diagram showing an example of correction processing of a low-precision map according to the embodiment of the present invention. Here, an example in which low-precision map data is corrected by a collation process that refers to the accuracy determination contents of a map and an image will be described as a pre-process in the above-described rough shape analysis process. First, with respect to the low precision map 500 as shown in FIG. 5A, the matching processing range 501 is specified by determining the precision information in the attribute information and the image attribute information. For example, if the map data position error is 10 m, the image resolution is 1 m, and the image position error is 5 m, ±
The collation processing range can be specified as ± 15 pixels by calculation such as (10 m / 1 m + 5 m / 1 m). Further, as the matching processing method, for example, matching processing using an edge analysis image can be used. That is, first, a line segment of a map figure to be matched is selected, an edge analysis image within the collation processing range is created centering on the line segment, and the strength and direction of the edge on the line segment and the edge analysis image, Select a location with a high degree of matching agreement in consideration of the length, etc., and use it as the correction line segment candidate. In the example shown in FIG. 5B, since there is only one correction line segment candidate 510 on the upper side of the map,
This is the corrected map. On the right side of the map, due to noise from the target object 511 or the like, correction line segment candidates 512 on the right side of the target object, correction line segment candidates 513 on the left side of the target object,
Although a plurality of candidates such as the correction line segment candidate 514 which is a true boundary can be obtained, the correction line segment candidate 514 having the highest matching matching degree is selected and set as the corrected map 520. As a result, it is possible to appropriately correct low-accuracy map data with various accuracies in consideration of the difference in accuracy from the image. Also,
The accuracy of the analysis can be improved by analyzing the vicinity of the boundary using the corrected map 520. That is, it becomes possible to detect not only the target object 521 which was easy to detect even before the correction but also the target object 522 which could not be correctly detected in the low-precision map as it is. Further, the corrected map obtained by the above processing is stored and stored in the storage means together with the attribute information such as the correction processing content. As attribute information, information that can be determined to be a high precision map in the above map precision determination processing, such as the name of the worker, the institution to which it belongs, the date and time when the correction processing was performed, and the resolution of the image data used for the correction, is added. As a result, it is possible to apply the corrected map to the detailed shape analysis process as high-precision map data. That is, by repeating the analysis process, it is possible to realize an analysis program having a learning function of reusing a low-precision map as a high-precision map. Further, a function of confirming the correction processing content and a user interface for inputting additional corrections if necessary may be provided to improve the reliability of the correction processing before storing. The image and the low-precision map are collated with reference to the image attribute information and the map attribute information, the position or shape of the low-precision map is corrected using the collation result, and the corrected map and its attribute information are stored in a storage means. The low-precision map can be corrected by the above-mentioned processing characterized by being stored in, and high-precision analysis processing can be performed by using it. In addition, by storing the appropriate attribute information together, the corrected map can be reused as a high-precision map in the subsequent analysis. Figure 6
FIG. 6 is a diagram showing an example of a schematic shape analysis process by using simple map creation according to an embodiment of the present invention. If the map acquisition fails in the above area map acquisition processing, it is necessary to analyze without using the map. However, if the entire image is analyzed as it is, a large number of land noises may be erroneously detected, and appropriate suppression thereof is required. Here, in some cases, the rough shape of the area can be extracted by analyzing the image in consideration of the characteristics such as the brightness and the shape of the attention area. For example, assuming that the attention area is the sea area as shown in FIG. 6, it is possible to obtain a rough shape of the sea area by generally dividing the image into areas to extract a wide area with dark brightness. By using this as a simple map creation function and applying the obtained shape as the simple map 600 to the above-described outline shape analysis processing, the attention area 601 can be specified and the vessel 602 in the sea area can be detected. That is, although the target object 203 and the like near the boundary cannot be detected, it is possible to collectively prevent erroneous detection of land areas. This allows
Even in an area where no map exists, analysis processing using a program of the same configuration can be performed, and highly reliable analysis information can be obtained. Further, it is also possible to improve the map accuracy by the above correction processing and then use it for analysis. Figure 7
It is a figure showing an example of output processing in one embodiment of the present invention. Various output methods are conceivable other than the example of outputting the detected target object information to the detected position on the image or the map. For example, FIG. 7A is an example 700 of outputting information of detected targets as a list. By combining the shape of the target object detected in the detection processing, the added boundary vicinity detection flag, the attribute information, and the like, it is possible to output the analysis processing results as catalog information.
Further, FIG. 7B is an example 701 of outputting a document related to the detected target object. It can be output in a format that is easy for users to use, such as documents directly connected to work and summary information of analysis results. By presenting these effective analysis information, it is possible to realize a program that can help improve the efficiency of user operations. FIG. 8 is a diagram showing an example of the target object detection processing in the embodiment of the present invention. Analytical work to detect targets from a wide area of interest by acquiring images automatically or in response to user instructions using image data that is updated or added regularly or irregularly Is possible. In such analysis work, it is important to effectively notify the user of the processing result at an appropriate timing. Figure 8 (a)
Is an example 800 of displaying and outputting a notification interface on a user terminal. As a result, it is possible to continue the target object detection process even while the user is not heading to the terminal executing the analysis program, and to notify that the target object has been detected when the user heads for the terminal. Further, various output devices such as sound output and lamp lighting may be used to call the user's attention, or an interface may be displayed and output to a user terminal connected through a network. Figure 8
(B) is an example 80 of notifying the user by using email.
It is 1. As a result, it is possible to provide immediate notification of detection information with a high degree of urgency, and to provide information to distant or multiple users. Further, by using a combination of telephone or FAX in addition to mail, it is possible to promptly notify a user who is not connected to the network. Figure 8
(C) is an output example 802 of the work record. In particular, in the automatic detection process, when the target is not detected, the user is not notified, a work record in a format that can be referred to is output as necessary, and the analysis process can be ended. Further, even if the target object is detected, by leaving the work record, it is possible to create consistent record information. The attention area may be set in advance or may be automatically set according to the input image. Especially, when the target is detected by the detection process in which the attention area is automatically set according to the image, etc. In this case, by presenting detailed information including the detection position or the attention area, effective notification can be performed. Further, the above list or document may be used for notification. In this way, a landscape image that is regularly or irregularly updated or added is acquired, and when a target object is detected in the attention area on the image or in the vicinity of the boundary, the detection information is notified to the user, and the target object is detected. If is not detected, the work record is output. As a result, it becomes possible to support the work of detecting a target object over a wide area, which has been manually performed by the user. FIG. 9 is a diagram showing an example of an image analysis system according to an embodiment of the present invention. It is possible to use the image analysis system 900 by installing a program that performs the above-described processing in a computer. An image database 901, a map database 902, and the like are connected to the system, and a data acquisition processing unit 903 that acquires analysis data in an appropriate range from them, a data analysis processing unit 904 that performs analysis processing using these data, and The data output processing unit 905 outputs the analysis result. Further, the data analysis processing unit 904 includes an accuracy determination processing unit 906 that performs the map accuracy determination process, a detailed shape analysis processing unit 907 that performs the detailed shape analysis process, and a general shape analysis processing unit 908 that performs the general shape analysis process. It is composed of
In order to execute these processes, the data acquisition processing unit 903 may be activated by the user's analysis data instruction operation or automatic activation in response to an event such as addition of new image data. Further, an embodiment such as an analysis information distribution service that distributes the analysis processing result using the above system as a server, and an analysis program distribution service that distributes the analysis program itself on the network are also possible. With these, the present invention can be applied to various forms or purposes of business.

[0006]

According to the present invention, it is possible to adaptively execute optimum processing according to the accuracy of an image and a map used for analysis and the difference between them, and thus the general-purpose analysis has a wide range of application and high flexibility. A program can be realized. That is, without using high-precision maps, low-precision maps and user-input maps,
Furthermore, it is possible to perform analysis processing freely corresponding to various situations such as no map, and highly accurate and highly reliable analysis information can be obtained from a complicated high-resolution landscape image. Further, according to the present invention, in the analysis of the vicinity of the boundary of the attention area, which is often important in business, even when a low-precision map is used, information that suppresses false detection and prompts the user to make an appropriate decision is provided. Can be provided. As a result, the reliability of the analysis information as a whole can be increased, and it can be applied to various jobs. Further, according to the present invention, the corrected low-precision map data, the user-input map, etc. can be reused as the high-precision map, and the learning function for improving the analysis accuracy can be realized by repeating the processing. . As a result, the degree of freedom in constructing and operating the image analysis system and the map database is increased, and the system can be quickly started up, and the high-precision map database can be gradually prepared in daily operations. Further, according to the present invention, it is possible to provide high-level work support for analysis work, such as automatic detection of a target object over a wide area, which has been manually performed by the user. With these effects, the user can efficiently obtain high-value-added and high-precision analysis information while reducing the amount of work involved in preparation and maintenance of images and map data, thus reducing the workload of image analysis work. It can be significantly reduced.

[Brief description of drawings]

FIG. 1 is a diagram showing an example of a processing procedure according to an embodiment of the present invention.

FIG. 2 is a diagram showing an example of a detailed shape analysis process according to the embodiment of the present invention.

FIG. 3 is a diagram showing an example of a schematic shape analysis process according to an embodiment of the present invention.

FIG. 4 is a diagram showing an example of erroneous detection removal processing near a map boundary according to an embodiment of the present invention.

FIG. 5 is a diagram showing an example of correction processing of a low-precision map according to the embodiment of the present invention.

FIG. 6 is a diagram showing an example of a schematic shape analysis process using simple map creation according to an embodiment of the present invention.

FIG. 7 is a diagram showing an example of output processing according to an embodiment of the present invention.

FIG. 8 is a diagram showing an example of target object detection processing according to an embodiment of the present invention.

FIG. 9 is a diagram showing an example of an image analysis system according to an embodiment of the present invention.

FIG. 10 is a diagram showing an example of conventional map use analysis processing using an enlarged mask.

[Explanation of symbols]

Reference numeral 100 ... Image, 101 ... Map, 102 ... Region map calling processing, 103 ... Map accuracy determination processing, 104 ... Detailed shape analysis processing, 105 ... Outline shape analysis processing, 106 ... Output processing, 200 ... Sea area, 201 ... Land area, 202 ... coastline, 2
03 ... berthing vessel, 204 ... jetty, 205 ... offshore vessel,
210 ... High-precision map, 211 ... Region of interest, 212 ... Jetty, 213 ... Vessel on shore, 214 ... Vessel offshore, 300 ...
Low-precision map, 301 ... Region of interest, 302 ... Fine false detection, 303 ... Vessel on dock, 304 ... Jetty, 305 ... Long false detection, 306 ... Vessel offshore, 310 ... Detection result near boundary, 311 ... Detection result near boundary, 312 ... Boundary detection result, 313 ... Boundary detection result, 400 ... Low accuracy map, 401 ... Vessel including some land area, 402 ... Part of vessel, 403 ... Land area, 404 ... Offshore vessel, 410 ... Vessel 411 ... Partial vessel, 412 ... Land area, 413 ... Offshore vessel, 420 ... Land area false detection, 421 ... Template partial matching point, 422 ... Mask inner area, 423 ... Ship, 4
24 ... Land area, 500 ... Low-precision map, 501 ... Collation processing range, 510 ... Correction line segment candidate, 511 ... Target object, 512 ...
Correction line segment candidate, 513 ... Correction line segment candidate, 514 ... Correction line segment candidate, 520 ... Corrected map, 521 ... Target object, 52
2 ... Target, 600 ... Simple map, 601 ... Region of interest, 6
02 ... Vessel, 700 ... List, 701 ... Document,
800 ... Notification interface, 801 ... Notification email,
802 ... Work record, 900 ... Image analysis system, 901
… Image database, 902… Map database, 90
3 ... Data acquisition processing unit, 904 ... Data analysis processing unit, 9
05 ... Data output processing unit, 906 ... Accuracy determination processing unit, 9
07 ... Detailed shape analysis processing, 908 ... Schematic shape analysis processing,
1000 ... Mask, 1001 ... Enlarged mask, 1002 ...
Near the boundary of the area of interest.

Continuation of front page (51) Int.Cl. ⁷ Identification code FI theme code (reference) G09B 29/00 G09B 29/00 Z (72) Inventor Norio Tanaka 216 Totsuka-cho, Totsuka-ku, Yokohama-shi, Kanagawa Incorporated company Hitachi Factory Defense System Business Division F-term (reference) 2C032 HB11 HB12 HC09 HC13 HD01 5B050 BA02 BA17 CA07 EA06 EA07 EA15 EA19 FA02 FA09 FA14 5B057 AA13 AA14 DA07 DA08 DB02 DC09 DC16 DC33 DC36 5L096 BA12 FA06 GA10 JA11

Claims (7)

[Claims] 1. A step of obtaining a captured image of a landscape, a step of calling a map in the range of the image from the map information stored in a storage means, and a step of using the attribute information of the map and the attribute information of the image. A step of determining the accuracy of the map with respect to the image; a step of performing a detailed shape analysis process using the map and the image when the determination result is high accuracy; and a step of performing the detailed shape analysis processing using the map and the image. A program for causing a computer to execute a step of performing a rough shape analysis process using a map and the image, and a step of causing the output means to output the result of the analysis process. 2. The detailed shape analysis processing includes the steps of superimposing the map and the image, extracting a target area boundary in the image, and detecting a target object from the extracted area. In addition to the step of the detailed shape analysis processing, the rough shape analysis processing includes a step of adding a flag indicating that the detection target near the area boundary exists near the boundary, and the detection target. 2. The program according to claim 1, further comprising a step of causing the output means to output an object and the flag information together. 3. The rough shape analysis process uses a step of identifying a target object detected near the boundary from the flag information, and attribute information of the target object stored in a storage means for the target object. And a step of performing a more detailed analysis determination process than the detailed shape analysis process. 4. The rough shape analysis processing comprises a step of collating the image with the low precision map with reference to the precision determination result, and a step of changing information of the low precision map using the collation result. The program according to any one of claims 1 to 3, further comprising: a step of storing attribute information including the changed map and information of the change in the storage means. 5. A step of determining the presence or absence of a map in the image range before the step of calling the map,
5. If there is no map, the method includes the steps of creating a simple map from the image, and the step of creating and using the created simple map to perform the rough shape analysis process. Program described in either. 6. A step of outputting detection information when the target object is detected, and a step of recording a work record in a recording means when the target object is not detected. The program according to any one of 1 to 5. 7. A map information analysis device comprising a landscape image pickup means, a recording means having map information, and a control section, wherein the control section is acquired from the map information stored in the storage means by the landscape image pickup means. The step of calling a map in the range of the image, the attribute information of the map and the attribute information of the image are used to determine the accuracy of the map with respect to the image, and subsequent analysis processing steps are changed based on the determination result. A map information analysis device characterized by: