Expected completed image generating program, expected completed image generating system, and expected completed image generating method

Abstract

To provide a new completion prediction image generation program, a completion prediction image generation system and a completion prediction image generation method for quickly generating a real completion prediction image closer to a scene after completion, and for presenting it to a customer.SOLUTION: Image data obtained by photographing a site to be an object of exterior construction work and information on the contents of the exterior construction work are acquired through a network, a predicted image after construction is generated from the acquired image data and the information by using an image generation AI, and the generated predicted image is presented through the network. Thus, since a completion prediction image close to a scene after completion can be presented to the customer through the Internet, customer satisfaction is improved, and the possibility of order reception of the construction is increased. In addition, by using the image generation AI, a realistic image close to a finished scene can be easily obtained in a short time, and thus cost reduction can also be achieved.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a program, a system, and a method for generating a completed image of a building when exterior construction work is carried out on the garden or site of the building. [Background technology]

[0002] Traditionally, when a building owner orders exterior construction work from a specialized construction company, i.e., the construction of necessary structures in the space outside the building or the construction of the grounds to make it easier to live in, the owner typically requests an estimate to find out in advance how long the construction will take and how much it will cost. The construction company that receives the estimate then obtains from the customer various information necessary for the construction, such as the type of structure to be constructed, the size and shape of the lot, and the address of the building, depending on the content of the work, and then calculates an appropriate estimate based on the information obtained and presents it to the customer.

[0003] However, when this information is obtained via email or telephone, it is inevitable that only a rough estimate can be presented. Moreover, many customers do not have an accurate understanding of the size (shape, area) of their building or garden, and the information is often inaccurate. As a result, when a contractor actually visits the site to inspect it, there is a large discrepancy between the information provided by the customer and the information the contractor actually obtains from its investigation, and as a result, the estimated price can be significantly higher than originally planned. Even when contractors explain the reasons for this in detail to customers, there are an increasing number of cases where customers develop a distrust for the contractor and the deal does not go through. On the other hand, there is also the concern that if a higher estimate is presented taking these circumstances into account in advance, the customer may cancel the order at that stage or go to another contractor.

[0004] Furthermore, customers who are not experts in construction find it difficult to visualize what the completed landscape will look like before placing an order for the desired construction. Therefore, if there is a discrepancy between the landscape that the customer imagined based on the sample object and the actual completed landscape, customer satisfaction may be unsatisfied, and in some cases, unnecessary trouble such as redoing the construction may occur. Furthermore, since the cost of these construction projects is generally high, customers may hesitate to place an order or even cancel the project if they cannot visualize the completed landscape.

[0005] For example, Patent Document 1 below proposes a method of using a computer or the Internet to input image data including land and its surrounding scenery into a computer, extract appropriate objects from a database storing image data of objects such as buildings and gardens, and combine them with the image data to generate a composite image, which is then distributed to the public via the Internet, thereby eliciting an image of the living environment of the property in advance in the customer's mind, giving them a sense of security, possibilities, and encouraging them to purchase. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-90634 Summary of the Invention [Problem to be solved by the invention]

[0007] However, as shown in Patent Document 1, a composite image that simply combines an object stored in a database with an image of a property or land tends to look unnatural because the color, size, texture, etc. of the property image and the object do not match, and it may only be able to display a rough image, or may end up looking more flashy than it actually is. Therefore, after the image is combined, manual adjustment of the color, size, texture, etc. of each part is required, which is time-consuming and labor-intensive.

[0008] Therefore, the present invention has been devised to solve these problems, and its purpose is to provide a new program, system, and method for generating a model image of a completed building that can quickly generate a realistic model image of the completed building that is closer to the landscape after completion and present it to customers. [Means for solving the problem]

[0009] In order to solve the above problem, the first invention is a completed image generation program that causes a computer to function as an information acquisition means that acquires image data of a site that is the target of exterior construction work and information related to the content of the exterior construction work via a network, a completed image generation means that generates a completed image of the site after construction using an image generation AI from the image data and information acquired by the information acquisition means, and a completed image presentation means that presents the completed image generated by the completed image generation means via a network.

[0010] Using such a computer program, for example, a customer who wants to have exterior construction work done on their home can take a photo of the site with a camera and send the image data along with information about the details of the exterior construction work via the Internet using an information processing device such as a PC or smartphone.The image generation AI will then generate an image of what the finished product will look like after construction based on the image of the site and the information about the details of the exterior construction work that was sent.

[0011] The completed image generated by image generation AI in this way is in harmony with the original image in terms of color, size, texture, etc., resulting in a realistic image that closely resembles the landscape after completion.By sending this completed image to the customer's information processing terminal via the Internet and presenting (displaying) it, the customer can get a concrete understanding of the actual landscape after completion, thereby improving customer satisfaction.In addition, by using image generation AI, realistic images can be obtained easily and in a short time, which also reduces costs.

[0012] Here, the image generation AI is not particularly limited, but examples that can be used include DALL-E, one of the functions of ChatGPT (registered trademark) 4-o, an AI chat service provided by OpenAI OpCo, LLC, Stable Diffusion provided by Stability Ai Ltd, Midjourney (registered trademark) provided by Midjourney Inc., NightCafe Creator provided by NightCafe Studio Pty Ltd, Craiyon provided by Craiyon LLC, Canva (registered trademark) provided by Canva Pty, Ltd, Fotor (registered trademark) provided by Everimaging Science and Technology Co., Ltd, Deep Dream Generator provided by Aifnet Ltd., and Artbreeder provided by Morphogen, Inc.

[0013] Furthermore, the exterior construction work referred to in this invention is not particularly limited, and includes not only the construction of artificial turf, as described below, but also all work to improve the environment of a building and its surroundings, such as landscaping the exterior of a building or site, creating gardens and approaches, garages and exteriors, etc., and more specifically, this includes gateposts, block fences, designing gardens and flower beds, planting, installing stairs and stone paving that are suited to the topography of the site, and concrete floors for garages.

[0014] The second invention is a program for generating a completed image according to the first invention, characterized in that the computer further functions as an area calculation means for inputting image data acquired by the information acquisition unit into a deep learning model that has been trained on images of various sites and the shape and area of ​​the sites to calculate the shape and area of ​​the site, and an estimate calculation means for calculating an estimate of the cost required for the exterior construction work from the shape and area of ​​the site calculated by the area calculation means and information on the content of the exterior construction work acquired by the information acquisition means, and the completed image presentation means functions to present the estimate calculated by the estimate calculation means together with the completed image.

[0015] Using such a computer program, not only can it present the image of the completed project as described above, but a deep learning model that has previously learned the shape and area of ​​the site by machine learning can calculate the shape and area of ​​the customer's site from the image data. An accurate estimate is then calculated from the obtained information on the shape and area of ​​the customer's site and the details of the exterior construction work, and this is presented to the customer via the Internet. This allows not only the image of the completed project but also an estimate for the construction work, thereby increasing the chances of winning an order.

[0016] A third invention is a program for generating a completed image of an artificial turf according to the first or second invention, wherein the exterior construction work is the construction of an artificial turf, and the information about the details of the exterior construction work is information about the artificial turf. By using such a computer program, a customer who wishes to install artificial turf in their garden or balcony, for example, can simulate in advance what the landscape will look like after the artificial turf is installed, allowing them to optimally select the color, area, type, etc. of the artificial turf, thereby improving customer satisfaction with the installation of the artificial turf.

[0017] The fourth invention is a system for generating a completed image, comprising an information acquisition unit that acquires image data of a site to be subjected to exterior construction work and information relating to the details of the exterior construction work via a network, a completed image generation unit that generates a completed image of the site using an image generation AI from the image data and information acquired by the information acquisition unit, and a completed image presentation unit that presents the completed image generated by the completed image generation unit via the network. With this configuration, the same actions and effects as those of the first invention can be obtained.

[0018] The fifth invention is the fourth invention, and further includes an area calculation unit that calculates the shape and area of ​​a site by inputting image data acquired by the information acquisition unit into a deep learning model that has been trained on images of various sites and the shape and area of ​​the site through machine learning, and an estimate calculation unit that calculates an estimate of the cost required for the exterior construction work from the shape and area of ​​the site calculated by the area calculation unit and information on the content of the exterior construction work acquired by the information acquisition unit, and the completion-imaging-image presentation unit presents the estimate calculated by the estimate calculation unit together with the completion-imaging image. With this configuration, the same functions and effects as those of the second invention can be obtained.

[0019] The sixth invention is the system for generating a completed image of the fourth or fifth invention, wherein the exterior construction work is the construction of artificial turf, and the information about the details of the exterior construction work is information about the artificial turf. With this configuration, the same effects and advantages as those of the third invention can be obtained.

[0020] The seventh invention is a method for generating a completed image, comprising: an information acquisition step of acquiring image data of a site to be subjected to exterior construction work and information relating to the content of the exterior construction work via a network; a completed image generation step of generating a completed image of the site from the image data and information acquired in the information acquisition step using an image generation AI; and a completed image presentation step of presenting the completed image generated in the completed image generation step via the network. This method provides the same effects and advantages as the first and fourth inventions.

[0021] The eighth invention is a method for generating a completed image according to the seventh invention, further comprising: an area calculation step of inputting image data acquired in the information acquisition step into a deep learning model that has been trained to learn images of various sites and the shapes and areas of those sites through machine learning, thereby calculating the shape and area of ​​the site; and an estimate calculation step of calculating an estimate of the cost required for the exterior construction work from the shape and area of ​​the site calculated in the area calculation step and information on the details of the exterior construction work acquired in the information acquisition step, wherein the completed image presentation step presents the estimate calculated in the estimate calculation step together with the completed image. This method provides the same effects as the second and fifth inventions.

[0022] A ninth aspect of the present invention is a method for generating a completed image of a building according to the seventh or eighth aspect of the present invention, characterized in that the exterior construction work is the construction of artificial turf, and the information about the details of the exterior construction work is information about the artificial turf. This method provides the same effects and advantages as the third and sixth aspects of the present invention. [Effects of the Invention]

[0023] According to the present invention, it is possible to present to customers via the Internet images of the completed building that are close to the landscape after completion, which improves customer satisfaction and increases the likelihood of receiving an order for the construction work. In addition, by using image generation AI, it is possible to easily and quickly obtain realistic images that are close to the landscape after completion, which also reduces costs. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a configuration diagram showing an embodiment of a system 100 for generating a completed image according to the present invention. [Figure 2] 1 is an explanatory diagram showing the hardware configuration of a system 100 for generating a completed image according to the present invention. [Figure 3] 1 is a block diagram showing the functions of a system 100 for generating a completed image according to the present invention. [Figure 4]FIG. 1 is a flowchart showing the flow of processing by the system 100 for generating a completed image according to the present invention. [Figure 5] This is a table showing an example of detailed content related to artificial turf in exterior construction work. [Figure 6] This is a conceptual diagram showing a deep learning model consisting of a multi-layer neural network. [Figure 7] (A) is a photo of the current site taken by the client, and (B) is a simulation of the site after it has been converted to artificial turf. [Figure 8] FIG. 2 is an explanatory diagram showing an operation image of a smartphone (information processing terminal) 200 operated by a customer. [Figure 9] FIG. 1 is a block diagram showing another embodiment of the system 100 for generating a completed image according to the present invention. [Figure 10] FIG. 10 is a flowchart showing another embodiment of the system 100 for generating a completed image according to the present invention. [Figure 11] 10 is a photograph showing an example of a site image used in the area calculation unit 140. FIG. [Figure 12] FIG. 10 is a photograph showing an example in which the area calculation unit 140 calculates the shape and area of ​​a site from an image of the site sent by a customer. [Figure 13] FIG. 1 is an explanatory diagram showing another embodiment of the system 100 for generating a completed image according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Fig. 1 shows one embodiment of a system 100 for generating a rendered image of a completed building according to the present invention. As shown in the figure, this system 100 for generating a rendered image of a completed building comprises an information processing device (computer) that operates in accordance with a program (software) for generating a rendered image of a completed building and a method (algorithm) for generating a rendered image of a completed building according to the present invention, and is capable of two-way communication with various information processing terminals 200 such as smartphones and PCs via the Internet N.

[0026] Figure 2 shows the hardware configuration of this system for generating a completed image of a finished product 100, which, like a general-purpose computer system (server), includes a CPU 101, RAM 102, CLC 103, ROM 104, interface (I / F) 106, storage device 107, and other components connected via a bus 105, and is configured to realize the following functions using dedicated computer programs (operating system, application software) stored in the storage device 107, etc. This interface (I / F) 106 is connected to an input device 108 such as a keyboard and an output device 109 such as a monitor, as well as an information and communications network such as the Internet N, and data can be sent and received via the Internet N to and from a customer's information processing terminal 200 (such as a smartphone, tablet, or PC).

[0027] These hardware resources and the software resources that execute some of the following processes can be prepared in-house, or paid or free cloud computing services provided by cloud service providers can be used. For example, by using services such as AWS (Amazon Web Services) provided by Amazon.com, Microsoft Azure provided by Microsoft Corporation, Oracle Cloud provided by Google LLC, or IBM Cloud provided by IBM (International Business Machines Corporation), the initial investment and operational man-hours can be significantly reduced, making it possible to realize the system of the present invention at low cost and relatively easily.

[0028] 3 is a block diagram showing the functions realized by this model-perfect-image generating system 100. As shown in the figure, this model-perfect-image generating system 100 is mainly composed of an information acquisition unit 110, a model-perfect-image generating unit 120, and a model-perfect-image presenting unit 130. The specific functions of each of these units will be described later, but they can be briefly explained as follows.

[0029] 1. Information acquisition section 110 The information acquisition unit 110 mainly has a function of acquiring image data of a photograph taken of the site where the exterior construction work is to be performed and information relating to the details of the exterior construction work from a customer (user) via the Internet N. 2. Rendered Image Generation Unit 120 The completion-prospect image generating unit 120 has a function of generating a completion-prospect image (image perspective) of the site after the exterior construction work is completed, mainly based on the information acquired by the information acquiring unit 110, using an image generating AI. 3. Finished image presentation unit 130 The completed-perspective image presenting unit 130 has a function of transmitting the completed-perspective image generated by the completed-perspective image generating unit 120 to the information processing terminal 200 of the customer (user) via the Internet N and presenting it.

[0030] The flow of the process of presenting a completed image using each of these functions will be explained below with reference to the flowchart in Figure 4. First, as an example of exterior construction work, an example will be explained in which a customer (user) who wants to install artificial turf in his or her home yard accesses the website of the system device 100 of the present invention, which is managed by a construction company, from the information processing terminal (smartphone) 200. As shown in step S100, when the customer (user) requests the presentation of a completed image of the artificial turf installation in his or her home yard, the information acquisition unit 110 of the system device 100 of the present invention acquires information necessary for generating the completed image from the customer via the Internet N.

[0031] The information required to generate this image of the completed project includes on-site photographs (image data) of the customer's site where artificial turf is to be installed, and information about the artificial turf to be installed, such as the type of artificial turf (high-durability artificial turf, high-touch artificial turf, artificial turf that resembles natural turf, etc.), artificial turf options (colored turf accents, partition materials, antibacterial properties, brickwork, etc.), and other construction work (wood decks, fences, gravel, stepping stones, etc.) required for artificial turf installation, as shown in Figure 5. In addition to this information, it is also possible to obtain information such as the residential area of ​​the customer for whom the work will be installed (Saitama / Tokyo / Chiba / Kanagawa / Ibaraki / Tochigi, or the eastern / western (northern, southern) parts of each prefecture), and, if necessary, personal information of the customer who made the inquiry (name, address, age, telephone number, email address).

[0032] Once the necessary information has been obtained from the customer, the process proceeds to step S102, where the completed image generation unit 120 generates a completed image (image perspective). To generate the completed image in step S102, an image generation AI (deep learning model) is used, which has previously undergone machine learning to process a huge number of images. Specifically, an image generation AI called DALL-E, which is one of the functions of ChatGPT (registered trademark) 4-o provided by OpenAI OpCo, LLC, can be used. Figure 6 shows an example of a deep learning model consisting of a multi-layer neural network that illustrates the concept of this image generation AI. When the aforementioned image data and information about the artificial turf (prompt or image) are input as input data, a completed image can be generated and output from this information after a few to several tens of seconds.

[0033] Once the completed image has been generated in this way, the process proceeds to the next step S104, where the completed image presentation unit 130 transmits the completed image to the customer's information processing terminal 200 via the Internet N, thereby completing the process. The completed image obtained by the image generation AI in this way is in harmony with the image sent by the customer in terms of color, size, texture, etc., resulting in a realistic image that closely resembles the landscape after completion.

[0034] Figure 7 shows an example of a projected image created using this image generation AI, in which an image of the current site taken by the customer to whom the artificial turf will be installed is combined with the type of artificial turf selected by the customer in step 100. This allows customers to visualize the landscape they will have when the artificial turf they selected is installed, simulating it. Therefore, by viewing this projected image, customers can get a concrete idea of ​​what the landscape will look like once completed, improving customer satisfaction. Furthermore, by using image generation AI, realistic images that closely resemble the landscape after completion can be easily and quickly obtained, which also reduces costs.

[0035] Figure 8 shows this process from the customer's perspective. When the customer accesses the website managed by the contractor from their smartphone, they upload the image file and necessary information to the website, following the instructions that encourage them to imagine what the finished product will look like when it is installed in their garden (A, Operation image 1). A thumbnail of the image they sent is then displayed, and after checking it, the customer taps the "Simulate" button (B, Operation image 1). After a while, an image of the finished product is sent, and by checking this image on their smartphone, they can easily imagine what the landscape will look like once the work is complete (C, Operation image 3, D, Operation image 3).

[0036] If a customer who has seen the image of the completed product is interested in the content, they can tap the "Inquire" button that appears alongside the image (D·Operation image 4), which will take them to the next screen (not shown), such as an inquiry screen, and they can smoothly move on to the next stage, such as specific negotiations and quotes.

[0037] Next, Figures 9 to 13 show a second embodiment of the system 100 according to the present invention. First, as shown in Figure 9, this embodiment further includes an area calculation unit 140, an estimate calculation unit 150, and an estimate presentation unit 160 in addition to the configuration shown in Figure 3, and is also capable of displaying an estimate of the cost required to actually carry out exterior construction work corresponding to the presented image of the completed building.

[0038] Here, the area calculation unit 140 calculates the shape and area of ​​the site based on the image data acquired by the information acquisition unit 110, and the estimate calculation unit 150 has a function of calculating an estimate of the cost required for the exterior construction work based on the shape and area of ​​the site calculated by the area calculation unit 140 and information on the details of the exterior construction work acquired by the information acquisition unit 110. In addition, the estimate presentation unit 160 has a function of transmitting and presenting the estimate calculated by the estimate calculation unit 150 together with the completed-image generated by the completed-image generation unit 120 to the customer's information processing terminal 200 via the Internet N.

[0039] Figure 10 shows the flow of this series of processes. As in the above embodiment, once a completed image has been generated in step 102, the process proceeds to the next step S106, where the area calculation unit 140 uses AI (artificial intelligence) to calculate the shape and area of ​​the customer's site. This area calculation unit 140, like the image generation AI described above, is made up of a deep learning model consisting of a multi-layered neural network as shown in Figure 6. This deep learning model has previously been trained by machine learning a large number of images of the sites around various buildings to be installed with artificial turf, as well as the shape and area of ​​those sites, as shown in Figure 11, for example.

[0040] When an image of the site sent by a customer is input as input data into the input layer of this deep learning model, its shape (dimensions) and area are calculated in the middle layer, and the results are output as output data from the output layer. For example, when an image of the site (A) sent by a customer is input into this deep learning model as shown in Figure 12, the total area can be accurately calculated from its shape and the dimensions (lengths) of each part, as shown in Figure 12 (B).

[0041] Once the shape and area of ​​the customer's site have been calculated in this way, the process moves to the next step S108, where the estimate calculation unit 150 calculates an estimate based on the shape and area of ​​the site and various information about the customer's desired artificial turf, taking into account the time required to install the artificial turf, labor costs, and raw material costs.Then, the process moves to the final step S110, where the calculated estimate is transmitted and presented to the customer's information processing terminal 200.

[0042] Figure 13 shows this process flow from the customer's perspective. As in the previous embodiment, the customer accesses the contractor's website from their smartphone and requests a simulation by uploading an image file and the necessary information to the website in accordance with the instructions urging them to visualize what the finished product will look like when artificial turf is installed in their garden. After a while, an image of the finished product is sent, along with the site area and estimated cost (D·Operation Image 4). If the customer is interested in the simulation (image of the finished product) and the estimated cost, they can tap the "Inquiry" button to move on to the next stage of detailed negotiations.

[0043] In this way, according to this embodiment, not only the image of the completed project but also the estimated cost of the work can be presented at the same time, which increases the possibility of winning an order. Even if an order is not awarded, it also reduces the frequency of workers visiting the site to inspect the work and direct negotiations between the customer and employees for estimates, which leads to cost savings. In addition, since there is basically no direct contact with the customer during the estimate stage, it also reduces the stress of the employees involved.

[0044] In this embodiment, an estimate process is performed after first transmitting and presenting a model image (simulation) as shown in the flow chart of Fig. 4. However, the simulation and estimate may be performed simultaneously, or the estimate may be presented first and then the model image may be presented, as shown in the operation chart of Fig. 13. Furthermore, a plurality of model images and corresponding estimates may be presented simultaneously.

[0045] Furthermore, after sending the image of the completed project, a process may be provided to accept the customer's detailed requests, such as "this part does not need to be worked on" or "the part in the upper right corner of the image is not included, but I would like this part to be worked on as well," and the image of the completed project may be re-created and presented in response to the requests. Furthermore, although this embodiment uses an example of artificial turf, it can be applied to other exterior construction work as is. For example, it can be applied to exterior construction work such as pouring concrete in a garden, laying bricks, and installing a wooden deck. [Explanation of symbols]

[0046] 100...Complete image generation system 110…Information acquisition department 120...Complete image generation unit 130...Presentation of completed image 140…Area calculation section 150...Estimate calculation section 160... Estimate presentation section 200...Information processing terminals (PCs, smartphones) N…Internet

Claims

1. Computer, an information acquisition means for acquiring image data of a site to be subjected to exterior construction work and information relating to the details of the exterior construction work via a network; A completion image generation means for generating a completion image after construction using an image generation AI from the image data and information acquired by the information acquisition means; a computer program for generating a complete image, the computer causing the computer to function as a complete image presentation means for presenting the complete image generated by the complete image generation means via a network;

2. 2. The program for generating a completed image according to claim 1, The computer an area calculation means for inputting the image data acquired by the information acquisition unit into a deep learning model that has been machine-learned to acquire images of various sites and the shapes and areas of those sites, and calculating the shapes and areas of those sites; The information acquisition means further functions as an estimate calculation means for calculating an estimate of the cost required for the exterior construction work from the shape and area of ​​the site calculated by the area calculation means and information about the details of the exterior construction work acquired by the information acquisition means; The completion image generating program is characterized in that the completion image presenting means functions to present the estimated amount calculated by the estimate amount calculating means together with the completion image.

3. 3. The program for generating a completed image according to claim 1, The exterior construction work is the construction of artificial turf, and the information about the contents of the exterior construction work is information about the artificial turf.

4. an information acquisition unit that acquires image data of a site that is the target of exterior construction work and information about the details of the exterior construction work via a network; a completion-prospect image generation unit that generates a completion-prospect image after construction using an image generation AI from the image data and information acquired by the information acquisition unit; a completion image presentation unit that presents the completion image generated by the completion image generation unit via a network;

5. 5. The system for generating a completed image according to claim 4, an area calculation unit that inputs the image data acquired by the information acquisition unit into a deep learning model that has been machine-learned to acquire images of various sites and the shapes and areas of those sites, and calculates the shapes and areas of those sites; an estimate calculation unit that calculates an estimate of the cost required for the exterior construction work based on the shape and area of ​​the site calculated by the area calculation unit and information about the details of the exterior construction work acquired by the information acquisition unit; The completion image generating system is characterized in that the completion image presenting unit presents the estimated price calculated by the estimate price calculation unit together with the completion image.

6. 6. The system for generating a completed image according to claim 4, The exterior construction work is the construction of artificial turf, and the information about the contents of the exterior construction work is information about the artificial turf.

7. an information acquisition step of acquiring image data of a site to be subjected to exterior construction work and information relating to the details of the exterior construction work via a network; a completion image generation process for generating a completion image after construction using an image generation AI from the image data and information acquired in the information acquisition process; a completion-prospective-image presenting step of presenting the completion-prospective-image generated in the completion-prospective-image generating step via a network.

8. The method for generating a completed image according to claim 7, an area calculation step of inputting the image data acquired in the information acquisition step into a deep learning model that has been machine-learned to acquire images of various sites and the shapes and areas of those sites, and calculating the shapes and areas of those sites; an estimate calculation step for calculating an estimate of the cost required for the exterior construction work from the shape and area of ​​the site calculated in the area calculation step and information about the details of the exterior construction work acquired in the information acquisition step; The method for generating a completed image is characterized in that the completed image presenting step presents the estimated price calculated in the estimate price calculation step together with the completed image.

9. 9. The method for generating a completed image according to claim 7 or 8, The method for generating an image of a completed project is characterized in that the exterior construction work is the construction of artificial turf, and the information about the contents of the exterior construction work is information about the artificial turf.

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