1. A method for geo-demographic classification based on satellite imagery, comprising: receiving satellite imagery data of a geographic area; processing the satellite imagery data using a computer system to identify one or more features of the geographic area; extracting demographic data associated with the geographic area; training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and outputting the classified geographic area to a user.

2. The method of claim 1, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.

3. The method of claim 1, wherein the demographic data comprises census data, consumer data, or social media data.

4. The method of claim 1, further comprising validating the accuracy of the machine learning model using ground truth data.

5. A system for geo-demographic classification based on satellite imagery, comprising: a computer system configured to receive and process satellite imagery data of a geographic area; a database in communication with the computer system, the database storing demographic data associated with the geographic area; a machine learning module in communication with the computer system, the machine learning module trained using the demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and an output device in communication with the computer system, the output device configured to output the classified geographic area to a user.

6. The system of claim 5, wherein the computer system is further configured to extract the demographic data from the database.

7. The system of claim 5, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.

8. The system of claim 5, wherein the demographic data comprises census data, consumer data, or social media data.

9. The system of claim 5, further comprising a validation module configured to validate the accuracy of the machine learning model using ground truth data.   GEO-DEMOGRAPHIC CLASSIFICATION TECHNIQUE Abstract A technique for geo-demographic categorization based on satellite imagery may be included in certain embodiments of the present disclosure. This method may comprise obtaining satellite imagery data of a geographic region as part of its process. Moreover, embodiments may comprise the processing of the satellite imaging data by a computer system in order to identify one or more properties of the geographical region being analysed. In certain embodiments, there is also the possibility of retrieving demographic data linked with the region in question. In certain embodiments, the classification of a geographic area into one or more demographic segments may additionally include training a machine learning model using the extracted demographic data and the recognised one or more attributes of the geographic area. Outputting the identified geographic region to a user is another possibility that might be included in embodiments. Fig. 1 , Claims:Claims :

1. A method for geo-demographic classification based on satellite imagery, comprising: receiving satellite imagery data of a geographic area; processing the satellite imagery data using a computer system to identify one or more features of the geographic area; extracting demographic data associated with the geographic area; training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and outputting the classified geographic area to a user.

2. The method of claim 1, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.

3. The method of claim 1, wherein the demographic data comprises census data, consumer data, or social media data.

4. The method of claim 1, further comprising validating the accuracy of the machine learning model using ground truth data.

5. A system for geo-demographic classification based on satellite imagery, comprising: a computer system configured to receive and process satellite imagery data of a geographic area; a database in communication with the computer system, the database storing demographic data associated with the geographic area; a machine learning module in communication with the computer system, the machine learning module trained using the demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and an output device in communication with the computer system, the output device configured to output the classified geographic area to a user.

6. The system of claim 5, wherein the computer system is further configured to extract the demographic data from the database.

7. The system of claim 5, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.

8. The system of claim 5, wherein the demographic data comprises census data, consumer data, or social media data.

9. The system of claim 5, further comprising a validation module configured to validate the accuracy of the machine learning model using ground truth data.

Description:GEO-DEMOGRAPHIC CLASSIFICATION TECHNIQUE
Field of the Invention
[0001] The invention relates to system and method for a population statistical data modelling. Moreover, the present disclosure provides method for geo-demographic classification based on satellite imagery.

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Geo-demographic classification is a technique used to categorize individuals or households based on their location and demographic characteristics. This technique involves analysing various types of data, including demographic data, socioeconomic data, and geographic data, to create a profile of individuals or households in a particular area. This profile can then be used to identify patterns and trends in the population and to inform policy and decision-making.
[0004] Technological advancements have significantly impacted the field of geo-demographic classification, allowing for more accurate and efficient analysis. Here are some of the technological tools and techniques disclosed in patent literature are discussed below.
[0005] The CN109886171A (By: UNIVERSITY BEIJING) - The embodiment of the invention provides a segmentation method and device for a remote sensing image geographic scene. The method comprises the steps of extracting spatial structure characteristics ofeach geographic object in a target remote sensing image; according to the spatial structure characteristics of all the geographic objects, performing aggregation to generate multiple layers of different types of homogeneous pattern spots; and performing spatial superposition and intersection on multiple layers of different types of homogeneous image spots to obtain a geographic scene unit in thetarget remote sensing image. According to the remote sensing image geographic scene segmentation method and device provided by the embodiment of the present invention, by extracting spatial structurecharacteristics of geographic objects and performing multi-layer graph aggregation, the automatic geographic scene oriented remote sensing image segmentation is realized, the urban functional region modeling extraction and the spatial division are facilitated, and the result can be applied to urban functional region mapping, urban planning, urban resource distribution and management and urban landscape ecological survey.
[0006] The US9619703B2 (By: TATA CONSULTANCY SERVICES) - A method and system is provided for geo-demographic classification of a geographical region. The present application discloses an unsupervised learning method and system for analyzing satellite imagery and multimodal sensory data in fusion for geo-demographic clustering. The present application also discloses an inexpensive and faster method and system for geo-demographic classification of a geographical region.
[0007] The WO2014151681A2 (By: NIELSEN) - Methods and apparatus to estimate demography based on aerial images are disclosed. An example method includes analyzing a first aerial image of a first geographic area to detect a first plurality of objects, and estimating a demographic characteristic of the first geographic area based on the first plurality of objects.
[0008] The CN110704565B (By: QUANZHOU NORMAL UNIVERSITY) - The invention discloses a demographic data gridding modeling method based on remote sensing and GIS. The method comprises: carrying out grid division on the research area; according to night light and vegetation indexes for reflecting/representing population spatial distribution and human activity intensity, calculating the population spatial distribution and the human activity intensity; obtaining county-level administrative unit boundaries and demographic data of the county-level administrative unit boundaries; under the support of GIS spatial analysis, constructing a demographic data gridding model directly facing a grid unit through weighted case and weighted least square regression analysis of SPSS, and inverting demographic data attached to an administrative unit into each grid to display spatial distribution of population in a grid mode. The method has universality, the influence of the scale effect can be effectively avoided, the processing process is simplified, the data source has universality and sustainability, and the population space analysis precision can be better improved.
[0009] Traditional methods of geo-demographic classification rely on demographic and socioeconomic data, which can be costly and time-consuming to collect. Thus, there is need of tech advancement in this domain
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The following paragraphs provide additional support for the claims of the subject application.
The invention relates to system and method for a population statistical data modelling. Moreover, the present disclosure provides method for geo-demographic classification based on satellite imagery.
[00013] Embodiments of the present disclosure may include a method for geo-demographic classification based on satellite imagery, including receiving satellite imagery data of a geographic area. Embodiments may also include processing the satellite imagery data using a computer system to identify one or more features of the geographic area. Embodiments may also include extracting demographic data associated with the geographic area. Embodiments may also include training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments. Embodiments may also include outputting the classified geographic area to a user.
[00014] In some embodiments, the identified one or more features of the geographic area may include natural features, built environment features, or transportation infrastructure features. In some embodiments, the demographic data may include census data, consumer data, or social media data. In some embodiments, the method may include validating the accuracy of the machine learning model using ground truth data.
[00015] Embodiments of the present disclosure may also include a system for geo-demographic classification based on satellite imagery, including a computer system configured to receive and process satellite imagery data of a geographic area. Embodiments may also include a database in communication with the computer system, the database storing demographic data associated with the geographic area.
[00016] Embodiments may also include a machine learning module in communication with the computer system, the machine learning module is trained using the demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments. Embodiments may also include an output device in communication with the computer system, the output device configured to output the classified geographic area to a user.
[00017] In some embodiments, the computer system may be further configured to extract the demographic data from the database. In some embodiments, the identified one or more features of the geographic area may include natural features, built environment features, or transportation infrastructure features. In some embodiments, the demographic data may include census data, consumer data, or social media data. In some embodiments, the system may include a validation module configured to validate the accuracy of the machine learning model using ground truth data.

Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a flowchart illustrating a method for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a block diagram illustrating a system for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure.
[00021] FIG. 3 is a modified block diagram further illustrating the system (from FIG. 2) for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure.
[00022] FIG. 4 is a detailed block diagram further illustrating the system (from FIG. 2) for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure.
Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00026] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00027] The invention relates to system and method for a population statistical data modelling. Moreover, the present disclosure provides method for geo-demographic classification based on satellite imagery.
[00028] FIG. 1 is a flowchart that describes a method for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include receiving satellite imagery data of a geographic area. At 120, the method may include processing the satellite imagery data using a computer system to identify one or more features of the geographic area. At 130, the method may include extracting demographic data associated with the geographic area. At 140, the method may include training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments. At 150, the method may include outputting the classified geographic area to a user.
[00029] In some embodiments, the identified one or more features of the geographic area may comprise natural features, built environment features, or transportation infrastructure features. In some embodiments, the demographic data may comprise census data, consumer data, or social media data. In some embodiments, the method may include validating the accuracy of the machine learning model using ground truth data.
[00030] FIG. 2 is a block diagram that describes a system 200 for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure. In some embodiments, the system 200 may include a computer system 210 configured to receive and process satellite imagery data of a geographic area. The system 200 may also include a database 220 in communication with the computer system 210, the database 220 storing demographic data 420 associated with the geographic area. The system 200 may also include a machine learning module 230 in communication with the computer system 210, the machine learning module 230 can be trained using the demographic data 420 and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments. The system 200 may also include an output device 240 in communication with the computer system 210, the output device 240 configured to output the classified geographic area to a user. In some embodiments, the computer system 210 may be further configured to extract the demographic data 420 from the database 220. In some embodiments, the system 200 may include a validation module configured to validate the accuracy of the machine learning model using ground truth data.
[00031] FIG. 3 is a modified block diagram that further describes the system 200 (from FIG. 2) for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure. In some embodiments, the identified one or more features of the geographic area, which can be built environment features, or transportation infrastructure features
[00032] FIG. 4 is a detailed block diagram that further describes the system 200 (from FIG. 2) for geo-demographic classification based on satellite imagery, according to some embodiments of the present disclosure. In some embodiments, the demographic data 420 may include census data 422, consumer data 424, and social media data 426, which can be utilized in determination of socioeconomic factors such as occupation, family status, or income
[00033] A technique for geo-demographic categorization based on satellite imagery may be included in certain embodiments of the present disclosure. This technique may comprise obtaining satellite imagery data of a geographic region as part of its process. Moreover, embodiments may comprise the processing of the satellite imaging data by a computer system 210 in order to identify one or more properties of the geographical region being analysed. In certain embodiments, there is also the possibility of retrieving demographic data 420 linked with the region in question. In certain embodiments, the classification of a geographic area into one or more demographic segments may additionally include training a machine learning model using the extracted demographic data 420 and the recognised one or more attributes of the geographic area. Outputting the identified geographic region to a user is another possibility that might be included in embodiments.
[00034] Natural characteristics, built environment features, or transportation infrastructure features might be among the features of the geographic region that are designated as being among the one or more features of the area in some implementations. In various examples, the demographic data 420 may include census data 422, consumer data 424, and social media data 426. The correctness of the machine learning model may be validated using ground truth data in some implementations of the technology.
[00035] A system for geo-demographic categorization based on satellite imaging, may contain a computer system 210 that is designed to receive and analyse satellite imagery data of a geographic region. The database 220 that is in contact with the computer system 210 and that stores demographic data 420 related with the geographic region is another component that may be included in embodiments.
[00036] The machine learning module 230 that is in communication with the computer system 210 and that has been trained with demographic data 420 and the identified one or more features of the geographic area in order to classify the geographic area into one or more demographic segments may also be included in some embodiments. An output device 240 that is in communication with the computer system 210 and that is designed to output the categorised geographic region to a user may also be included in embodiments.
[00037] In some implementations, the computer system 210 could have an additional configuration that allows it to get demographic data 420 from the database. Natural characteristics, built environment features, or transportation infrastructure features might be among the features of the geographic region that are designated as being among the one or more features of the area in various implementations. In various examples, the demographic data 420 may comprise demographic data 420 may include census data 422, consumer data 424, and social media data 426. A validation module that is capable of validating the precision of the machine learning model by using ground truth data may be included in the system in certain implementations of the system.
[00038] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context
[00039] As used herein, the term “wireless communication network” or “network interface” refers to a network following any suitable wireless communication standards, such as LTE-Advanced (LTE-A), LTE, Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and so on. Furthermore, the communications between network devices in the wireless communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future.
[00040] As used herein, the term “network device” refers to a device in a wireless communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. The “network device” or “terminal device” or “computing device” may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to the wireless communication network or to provide some service to a terminal device that has accessed the wireless communication network. The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA), portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, wearable terminal devices, vehicle-mounted wireless terminal devices and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment”, “computing device”, “network device” and “UE” may be used interchangeably.
[00041] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00042] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00043] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00044] All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00045] The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.
[00046] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00047] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors
 

Claims
I/We Claim:
1. A method for geo-demographic classification based on satellite imagery, comprising:
receiving satellite imagery data of a geographic area;
processing the satellite imagery data using a computer system to identify one or more features of the geographic area;
extracting demographic data associated with the geographic area;
training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and
outputting the classified geographic area to a user.
2. The method of claim 1, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.
3. The method of claim 1, wherein the demographic data comprises census data, consumer data, or social media data.
4. The method of claim 1, further comprising validating the accuracy of the machine learning model using ground truth data.
5. A system for geo-demographic classification based on satellite imagery, comprising:
a computer system configured to receive and process satellite imagery data of a geographic area;
a database in communication with the computer system, the database storing demographic data associated with the geographic area;
a machine learning module in communication with the computer system, the machine learning module trained using the demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and
an output device in communication with the computer system, the output device configured to output the classified geographic area to a user.
6. The system of claim 5, wherein the computer system is further configured to extract the demographic data from the database.
7. The system of claim 5, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.
8. The system of claim 5, wherein the demographic data comprises census data, consumer data, or social media data.
9. The system of claim 5, further comprising a validation module configured to validate the accuracy of the machine learning model using ground truth data.
 

GEO-DEMOGRAPHIC CLASSIFICATION TECHNIQUE
Abstract
A technique for geo-demographic categorization based on satellite imagery may be included in certain embodiments of the present disclosure. This method may comprise obtaining satellite imagery data of a geographic region as part of its process. Moreover, embodiments may comprise the processing of the satellite imaging data by a computer system in order to identify one or more properties of the geographical region being analysed. In certain embodiments, there is also the possibility of retrieving demographic data linked with the region in question. In certain embodiments, the classification of a geographic area into one or more demographic segments may additionally include training a machine learning model using the extracted demographic data and the recognised one or more attributes of the geographic area. Outputting the identified geographic region to a user is another possibility that might be included in embodiments.

Fig. 1 , Claims:Claims
I/We Claim:
1. A method for geo-demographic classification based on satellite imagery, comprising:
receiving satellite imagery data of a geographic area;
processing the satellite imagery data using a computer system to identify one or more features of the geographic area;
extracting demographic data associated with the geographic area;
training a machine learning model using the extracted demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and
outputting the classified geographic area to a user.
2. The method of claim 1, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.
3. The method of claim 1, wherein the demographic data comprises census data, consumer data, or social media data.
4. The method of claim 1, further comprising validating the accuracy of the machine learning model using ground truth data.
5. A system for geo-demographic classification based on satellite imagery, comprising:
a computer system configured to receive and process satellite imagery data of a geographic area;
a database in communication with the computer system, the database storing demographic data associated with the geographic area;
a machine learning module in communication with the computer system, the machine learning module trained using the demographic data and the identified one or more features of the geographic area to classify the geographic area into one or more demographic segments; and
an output device in communication with the computer system, the output device configured to output the classified geographic area to a user.
6. The system of claim 5, wherein the computer system is further configured to extract the demographic data from the database.
7. The system of claim 5, wherein the identified one or more features of the geographic area comprises natural features, built environment features, or transportation infrastructure features.
8. The system of claim 5, wherein the demographic data comprises census data, consumer data, or social media data.
9. The system of claim 5, further comprising a validation module configured to validate the accuracy of the machine learning model using ground truth data.