Systems and methods for a 3D model for visualization of landscape design

1. A computer-implemented method for visualization of landscape design, the method comprising, via one or more processors, sensors, servers, and/or transceivers:

receiving light detection and ranging (LIDAR) data generated from a LIDAR camera;

measuring a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data;

building a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation;

displaying a representation of the 3D model;

receiving object data from a user, wherein the object comprises one of: a patio; a shed; a garage; a fence; a tree; a flower; or a pathway; and

inputting, into a machine learning algorithm: (i) data of the 3D model of the landscape, and (ii) the received object data to generate a recommendation for placement of the object in the landscape;

wherein the machine learning algorithm is trained based upon preexisting data of object placement in landscapes.

2. The computer-implemented method of claim 1 , wherein the structure comprises:

a patio;

a shed;

a garage;

a fence; or

an outside of a room of a house.

3. The computer-implemented method of claim 1 , wherein the vegetation comprises:

a tree;

a plant; or

a flower.

4. The computer-implemented method of claim 1 , wherein the 3D model further comprises a pathway.

5. The computer-implemented method of claim 1 , wherein the structure and the vegetation are determined based upon processor analysis of the LIDAR data.

6. The computer-implemented method of claim 1 , further comprising, via the one or more processors, transceivers, sensors, and/or servers:

receiving drone data;

wherein the 3D model of the landscape is built further based upon the received drone data.

7. The computer-implemented method of claim 1 , further comprising, via the one or more processors, transceivers, sensors, and/or servers:

receiving global positioning system (GPS) data;

wherein the 3D model of the landscape is built further based upon the received GPS data.

8. The computer-implemented method of claim 1 , wherein the displayed representation of the 3D model comprises a 2D image of the landscape.

9. The computer-implemented method of claim 1 , wherein:

the LIDAR camera is positioned on the ground; and

the method further comprises, via the one or more processors, transceivers, sensors, and/or servers, receiving drone data from a drone, the drone data comprising: (i) radio detection and ranging (RADAR) data gathered by the drone, and (ii) photographic camera data gathered by the drone;

wherein the 3D model of the landscape is further built based upon the received drone data.

10. The computer-implemented method of claim 1 ,

wherein the machine learning algorithm comprises: a convolutional neural network (CNN); a deep neural network (DNN); or a recurrent neural network (RNN).

11. The computer-implemented method of claim 1 , further comprising, via the one or more processors, transceivers, sensors, and/or servers:

determining dimensional data of boundaries of the landscape based upon: (i) preexisting property data from a database, and (ii) the LIDAR data; and

overlaying the dimensional data of the boundaries onto the displayed representation of the 3D model.

12. A computer system configured for visualization of landscape design, the computer system comprising one or more processors, sensors, servers, and/or transceivers configured to:

receive light detection and ranging (LIDAR) data generated from a LIDAR camera;

measure a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data;

build a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation;

display a representation of the 3D model;

receive object data from a user, wherein the object comprises one of: a patio; a shed; a garage; a fence; a tree; a flower; or a pathway; and

input, into a machine learning algorithm: (i) data of the 3D model of the landscape, and (ii) the received object data to generate a recommendation for placement of the object in the landscape;

wherein the machine learning algorithm is trained based upon preexisting data of object placement in landscapes.

13. The computer system of claim 12 , further configured to, via the one or more processors, sensors, servers, and/or transceivers:

receive drone data; and

build the 3D model of the landscape further based upon the received drone data.

14. The computer system of claim 12 ,

wherein the machine learning algorithm comprises: a convolutional neural network (CNN); a deep neural network (DNN); or a recurrent neural network (RNN).

15. The computer system of claim 12 , further configured to, via the one or more processors, sensors, servers, and/or transceivers:

determine dimensional data of boundaries of the landscape based upon: (i) preexisting property data from a database, and (ii) the LIDAR data; and

overlay the dimensional data of the boundaries onto the displayed representation of the 3D model.

16. A computer system configured for visualization of landscape design, comprising:

one or more processors; and

a program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to:

receive light detection and ranging (LIDAR) data generated from a LIDAR camera;

measure a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data;

build a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation;

display a representation of the 3D model;

receive object data from a user, wherein the object comprises one of: a patio; a shed; a garage; a fence; a tree; a flower; or a pathway; and

input, into a machine learning algorithm: (i) data of the 3D model of the landscape, and (ii) the received object data to generate a recommendation for placement of the object in the landscape;

wherein the machine learning algorithm is trained based upon preexisting data of object placement in landscapes.

17. The computer system of claim 16 , wherein the executable instructions further cause the computer system to:

receive drone data; and

build the 3D model of the landscape further based upon the received drone data.

18. The computer system of claim 16 ,

wherein the machine learning algorithm comprises: a convolutional neural network (CNN); a deep neural network (DNN); or a recurrent neural network (RNN).

19. The computer system of claim 16 , wherein the executable instructions further cause the computer system to:

determine dimensional data of boundaries of the landscape based upon: (i) preexisting property data from a database, and (ii) the LIDAR data; and

overlay the dimensional data of the boundaries onto the displayed representation of the 3D model.