Patent Description:
In recent years, an increasing number of vacant houses has been attracting attention as a nationwide social problem. The increase in the number of vacant houses is known to have an adverse effect on the local environment, disaster prevention, and crime prevention, and various countermeasures have been developed by national and local governments to deal with the problem of the growing number of vacant houses. To develop these countermeasures, it is highly important to know the number of vacant houses in a district.

PTL <NUM> discloses a technology by which, upon input of a survey condition relating to survey information and of a result of the survey, a vacant house information DB is referenced to search for the survey condition relating to house information and the survey result, at least contents of vacant house information and a house map are displayed, and the house map and the vacant house information relating to the search result are stored.

NPL <NUM> discloses a technology for generating a model capable of estimating a vacant house ratio.

In the technologies as described above, a technology for grasping the state of vacant houses in a desired district with as little effort as possible is needed.

Accordingly, an object of the present disclosure is to provide a vacant house determination device, a vacant house determination method, and a program that solve the problems described above.

According to a first aspect of the present invention, a vacant house determination device includes: a formula deriving unit for deriving a vacant house probability calculation formula with respect to a predetermined district, based on map data that at least includes coordinates of a vacant house surveyed among houses included in a map, attribute information regarding a structural object, and topographic information; a vacant house probability calculating unit for calculating the vacant house probability with respect to each of the houses included in the map data, by using the vacant house probability calculation formula; and a vacant house determining unit for determining whether a house having the vacant house probability equal to or more than a predetermined value is a vacant house, based on machine learning data for presuming that a house in a captured image is a vacant house and image information regarding a site of a house having the vacant house probability equal to or more than a predetermined value, the machine learning data being generated based on, among pieces of pixel information representing sites of the houses in the captured image including the predetermined district, the pixel information representing a site of a house corresponding to the coordinates of the vacant house.

According to a second aspect of the present invention, a vacant house determination method includes: deriving a vacant house probability calculation formula with respect to a predetermined district, based on map data that at least includes coordinates of a vacant house surveyed among houses included in a map, attribute information regarding a structural object, and topographic information; calculating the vacant house probability with respect to each of the houses included in the map data, by using the vacant house probability calculation formula; and determining whether a house having the vacant house probability equal to or more than a predetermined value is a vacant house, based on machine learning data for presuming that a house in a captured image is a vacant house and image information regarding a site of a house having the vacant house probability equal to or more than a predetermined value, the machine learning data being generated based on, among pieces of pixel information representing sites of the houses in the captured image including the predetermined district, the pixel information representing a site of a house corresponding to the coordinates of the vacant house.

According to a second aspect of the present invention, a program causes a computer for a vacant house determination device to function as: formula deriving means for deriving a vacant house probability calculation formula with respect to a predetermined district, based on map data that at least includes coordinates of a vacant house surveyed among houses included in a map, attribute information regarding a structural object, and topographic information; vacant house probability calculating means for calculating the vacant house probability with respect to each of the houses included in the map data, by using the vacant house probability calculation formula; and vacant house determining means for determining whether a house having the vacant house probability equal to or more than a predetermined value is a vacant house, based on machine learning data for presuming that a house in a captured image is a vacant house and image information regarding a site of a house having the vacant house probability equal to or more than a predetermined value, the machine learning data being generated based on, among pieces of pixel information representing sites of the houses in the captured image including the predetermined district, the pixel information representing a site of a house corresponding to the coordinates of the vacant house. The program may be stored in a non-transitory recording medium.

According to the present disclosure, it is made possible to grasp the state of vacant houses in a desired district with as little effort as possible.

A vacant house determination device according to an example embodiment of the present invention will now be described with reference to the drawings.

<FIG> is a functional block diagram of a vacant house determination device according to the example embodiment.

A computer for a vacant house determination device <NUM> executes a vacant house determination program that is stored in advance. The vacant house determination device <NUM> includes individual functions, namely a control unit <NUM>, a formula deriving unit <NUM>, a vacant house probability calculating unit <NUM>, a learning unit <NUM>, a vacant house determining unit <NUM>, and a map generating unit <NUM>. As shown in <FIG>, the vacant house determination device <NUM> is communicatively connected to a database <NUM>.

The database <NUM> records, for example, map data and a captured image. The map data and the captured image are map data and a captured image that are associated with each other and include a predetermined district. A predetermined district may be a city, ward, town, village, land use zone, or the like. The map data includes, for example, the coordinates (latitude and longitude) of each surveyed vacant house among the houses present on a map including a predetermined district, the attribute information regarding a structural object, and the topographic information. Information regarding a structural object may be, for example, the coordinates of a road, the width information regarding a road, the identification information (structural object ID) regarding a major public facility (station, school, or the like). The topographic information includes information such as an altitude. The captured image may be an aerial photograph image, a satellite image, or the like. The captured image is preferably a high-definition image.

The database <NUM> records machine learning data generated by the vacant house determination device. The vacant house determination device <NUM> generates machine learning data for each district. The machine learning data is the data for presuming that a house in a captured image is a vacant house, on the basis of the pixel information regarding a site of a house corresponding to the coordinates of a vacant house, among pieces of pixel information regarding sites of houses in a captured image including a predetermined district.

<FIG> is a diagram illustrating a hardware configuration of the vacant house determination device.

As illustrated in <FIG>, a computer for the vacant house determination device <NUM> includes hardware such as a central processing unit (CPU) <NUM>, a read only memory (ROM) <NUM>, a random access memory (RAM) <NUM>, a hard disk drive (HDD) <NUM>, an interface <NUM>, and a communication module <NUM>.

<FIG> is a first diagram illustrating a process flow for the vacant house determination device.

The vacant house determination device <NUM> generates, in advance and using map data and captured image data, machine learning data to be used for presuming that a house is a vacant house. More specifically, the machine learning data is the data used for presuming that a house in a captured image is a vacant house, on the basis of the pixel information regarding a site of a house corresponding to the coordinates of a vacant house, among pieces of pixel information regarding sites of houses in a captured image including a predetermined district.

For example, the vacant house determination device <NUM> receives an input of an instruction to start machine learning from a user interface (step S101). Then, the learning unit <NUM> acquires the map data and the captured image that are recorded in association with the identification information (district ID) regarding a first district from the database <NUM> (step S102). The learning unit <NUM> acquires the coordinates of a surveyed vacant house from the map data (step S103). On the basis of the coordinates in the captured image corresponding to the coordinates of the vacant house, the learning unit <NUM> estimates the area of the site of the house at the coordinates (step S104). The learning unit <NUM> may estimate the area of the site, on the basis of the coordinates of the vacant house and of the range of colors representing trees surrounding the vacant house. In addition, the learning unit <NUM> may estimate the site of the vacant house on the basis of an image showing a road around the house. Any other method may be used to estimate the site of the vacant house. The learning unit <NUM> generates machine learning data for determining that an image of a certain area included in a certain captured image shows a vacant house, by using an image of a site of a vacant house, an image of a site of a non-vacant house, and a machine learning technique such as neural network learning (step S105). The learning unit <NUM> records the machine learning data in the database <NUM> in association with the identification information regarding the district (step S106). In addition, the learning unit <NUM> determines whether machine learning data has been generated for a pair of map data and a captured image for every district included in the database <NUM> (step S107). If the machine learning data has not been generated for every pair of map data and a captured image, the learning unit <NUM> repeats generation of machine learning data for any pair of map data and a captured image for which machine learning data has not been generated yet. Note that the learning unit <NUM> may sequentially acquire a plurality of captured images of a district corresponding to times, and may learn, on the basis of the transition of the captured images of the district, information about an element for determining that a house is a vacant house using the coordinates of a vacant house and an image of an area of a site of the vacant house. Examples of the element for determining that a house is a vacant house include an altitude X1, a nearest road width X2, a distance from the nearest station X3, a land use zone X4, and other elements such as how old the house is, how often cars enter/leave the site, how many plants are growing in the site, and how often houses in the district are rebuilt.

<FIG> is a second diagram illustrating a process flow for the vacant house determination device.

After generating the machine learning data in advance, the vacant house determination device <NUM> receives an input of identification information regarding a predetermined district (step S201). For example, the vacant house determination device <NUM> obtains identification information regarding a predetermined district from a user interface device on the basis of a user operation. In addition, the vacant house determination device <NUM> receives an instruction to start the processing. Accordingly, the control unit <NUM> in the vacant house determination device <NUM> acquires the map data, the captured image, and the machine learning data that are recorded in association with the identification information regarding the predetermined district from the database <NUM> (step S202).

The formula deriving unit <NUM> acquires the map data. The formula deriving unit <NUM> acquires the coordinates (latitude and longitude) of a surveyed vacant house, the attribute information regarding a structural object, and the topographic information that are included in the map data. From among the acquired pieces of information, the formula deriving unit <NUM> identifies a predetermined formula deriving parameter for each vacant house (step S203). Examples of the formula deriving parameter may be information pieces including the altitude of the vacant house, the width of a road nearest to the coordinates of the vacant house, the coordinates of a station nearest to the vacant house, the identifier of a land use zone in the district including the vacant house. More specifically, the formula deriving unit <NUM> identifies the altitude of the vacant house on the basis of the altitude recorded in the map data in association with the coordinates of the vacant house. In addition, the formula deriving unit <NUM> identifies the width (nearest road width) of a road recorded in the map data, the road being located at the coordinates closest to the coordinates of the vacant house. The formula deriving unit <NUM> also identifies the coordinates of a station nearest to the coordinates of the vacant house. Furthermore, the formula deriving unit <NUM> identifies the identification information regarding a land use zone that includes the coordinates of the vacant house, on the basis of a relationship between the coordinates of the vacant house and the coordinates of each land use zone recorded in the map data. The present example embodiment is described with an example in which the coordinates (latitude and longitude) of a surveyed vacant house, the attribute information regarding a structural object, and the topographic information are included in the map data; however, these pieces of information may be stored in the database <NUM> as separate data from the map data, and the vacant house determination device <NUM> may use the data to identify a formula deriving parameter for each vacant house as in the example.

The formula deriving unit <NUM> derives a vacant house probability calculation formula with regard to a district indicated in the map data, using both an identified formula deriving parameter corresponding to each vacant house and a regression analysis method (step S204). An equation (<NUM>) represents an example of the vacant house probability calculation formula. The formula deriving unit <NUM> derives a vacant house probability calculation formula by calculating the correlation coefficients A1, A2, A3, A4, and so on for the vacant house probability Y. In the equation (<NUM>), A represents a constant. Examples of the regression analysis method include a logistic regression analysis and a multiple regression analysis.

When the vacant house probability calculation formula has been derived by the formula deriving unit <NUM>, the vacant house probability calculating unit <NUM> uses the formula to calculate the vacant house probability Y of every unsurveyed house included in the map data (step S205). Specifically, the vacant house probability calculating unit <NUM> acquires the coordinates of an unsurveyed vacant house from the map data and, on the basis of the coordinates, identifies the altitude X1, the nearest road width X2, the distance X3 from the nearest station, and the value X4 of the land use zone identifier. The vacant house probability calculating unit <NUM> inputs these values to the vacant house probability calculation formula (<NUM>) to calculate the vacant house probability Y. The vacant house probability of a surveyed house may be <NUM>%. The vacant house probability calculating unit <NUM> records the vacant house probability data indicating the vacant house probabilities of individual houses included in the map data in the database <NUM> in such a way that the vacant house probability data is associated with the map data. In the case where the learning unit <NUM> has identified an element for determining that a house is a vacant house, the formula deriving unit <NUM> may reconstruct the formula for calculating the vacant house probability Y further including the term of the element.

When the vacant house probability calculating unit <NUM> has calculated the vacant house probability of every house included in the map data, the vacant house determining unit <NUM> identifies, as a vacant house candidate, any house that is included in the map data and has a vacant house probability equal to or more than a predetermined threshold of a vacant house probability at which it can be presumed that a house is a vacant house (step S206). The vacant house determining unit <NUM> constructs a neural network based on the machine learning data acquired in step S202 and, by using the neural network, determines whether the image of the house site of each vacant house candidate represents a vacant house (step S207). Note that the house site of each vacant house candidate in the map data can be estimated in a way similar to the way in step S104. The map generating unit <NUM> generates a vacant house map indicating a vacant house at the coordinates of the position of the house that has been determined to be a vacant house (step S208). For example, the map generating unit <NUM> plots a point of the position at which a vacant house has been determined to be present with a predetermined icon image (an image like a red dot) to generate the vacant house map. An output unit <NUM> outputs the vacant house map to a predetermined device (step S209). The output unit <NUM> may record the vacant house map in the database <NUM>.

Note that the vacant house determination device <NUM> may be allowed to repeat the processing from step S203 by using the map data of the vacant house map generated in step S208 as the map data to be acquired in step S202. Repeating the processing in steps S203 to S208 makes it possible to generate more accurate vacant house map data.

Through the above-described processing, the vacant house determination device <NUM> can determine that, on the basis of machine learning data, a house having a vacant house probability value equal to or more than a predetermined threshold is a vacant house. In addition, the vacant house determination device <NUM> can generate a vacant house map indicating the position of a house determined to be a vacant house in the district specified the user and can output the map to a desired device.

<FIG> is a diagram illustrating a minimum configuration of the vacant house determination device.

As shown in <FIG>, the vacant house determination device <NUM> includes functions of at least the formula deriving unit <NUM>, the vacant house probability calculating unit <NUM>, and the vacant house determining unit <NUM>.

The formula deriving unit <NUM> derives a vacant house probability calculation formula with respect to a predetermined district, on the basis of map data that at least includes the coordinates of a vacant house surveyed among houses included in the map, the attribute information regarding a structural object, and the topographic information.

The vacant house probability calculating unit <NUM> calculates the vacant house probability with respect to each of the houses included in the map data, using the vacant house probability calculation formula.

The vacant house determining unit <NUM> determines whether a house having a vacant house probability equal or more than a predetermined value is a vacant house, on the basis of the machine learning data for presuming that a house in a captured image is a vacant house and the image information regarding a site of the house having a vacant house probability equal to or more than a predetermined value, the machine learning data being generated on the basis of the pixel information representing a site of a house corresponding to the coordinates of a vacant house, among pieces of pixel information representing sites of houses in the captured image including a predetermined district.

Note that the above-described vacant house determination device <NUM> contains a computer system. The process of the processing steps described above is stored in the form of a program in a computer-readable recording medium, and the processing steps are performed by the computer through reading and executing the program. Examples of the computer-readable recording medium herein include a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, and the like. Alternatively, the computer program may be delivered to a computer via a communication line, and the computer that has received the delivered program may execute the program.

Furthermore, the aforementioned program may be a program for achieving some of the functions described above. Moreover, the program may be a so-called difference file (difference program) capable of achieving the above-described functions in combination with a program that is already recorded in the computer system. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the scope of the present invention as defined by the claims.

Claim 1:
A vacant house determination device comprising:
learning means for acquiring map data that includes a predetermined district and at least includes coordinates of a vacant house surveyed among houses included in a map, attribute information regarding a structural object, and topographic information, and a captured image that is associated with the map data and includes the predetermined district from a storage device, and generating machine learning data for presuming that a house in the captured image is the vacant house, the machine learning data being generated based on, among pieces of pixel information representing sites of the houses in the captured image including the predetermined district, the pixel information representing a site of a house corresponding to the coordinates of the vacant house;
formula deriving means for deriving a vacant house probability calculation formula with respect to a district specified by a user, based on the map data;
vacant house probability calculating means for calculating the vacant house probability with respect to each of the houses included in the specified district in the map data, by using the vacant house probability calculation formula; and
vacant house determining means for determining whether a house having the vacant house probability equal to or more than a predetermined value is a vacant house in the specified district, based on
machine learning data for presuming that a house in the captured image is a vacant house, the machine learning data being generated based on, among pieces of pixel information representing sites of the houses in the captured image, the pixel information representing a site of a house corresponding to the coordinates of the vacant house, and
image information regarding a site of a house having the vacant house probability equal to or more than a predetermined value;
map generating means for generating vacant house map information indicating a position of the house that has been determined to be a vacant house; and
output means for outputting the vacant house map information to a predetermined device or the storage device.