Systems and methods for a 3D model for visualization of landscape design
The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data; builds a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation; and displays a representation of the 3D model.
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;
displaying a representation of the 3D model;
receiving object data from a user; 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 3D model further includes a pathway.
3. 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.
4. 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.
5. The computer-implemented method of claim 1 , wherein the displayed representation of the 3D model comprises a 2D image of the landscape.
6. The computer-implemented method of claim 1 , wherein:
the LIDAR camera is positioned on a 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.
7. 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).
8. 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.
9. 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;
display a representation of the 3D model;
receive object data from a user; 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.
10. The computer system of claim 9 , 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.
11. The computer system of claim 9 ,
wherein the machine learning algorithm comprises: a convolutional neural network (CNN); a deep neural network (DNN); or a recurrent neural network (RNN).
12. The computer system of claim 9 , 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.
13. The computer system of claim 9 , further configured to, via the one or more processors, sensors, servers, and/or transceivers:
receive global positioning system (GPS) data; and
build the 3D model of the landscape is further based upon the received GPS data.
14. The computer system of claim 9 , wherein the displayed representation of the 3D model comprises a 2D image of the landscape.
15. A computer system configured for visualization of landscape design, comprising:
one or more processors; and
a non-transitory computer readable medium 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;
display a representation of the 3D model;
receive object data from a user; 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.
16. The computer system of claim 15 , 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.
17. The computer system of claim 15 ,
wherein the machine learning algorithm comprises: a convolutional neural network (CNN); a deep neural network (DNN); or a recurrent neural network (RNN).
18. The computer system of claim 15 , 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.
19. The computer system of claim 15 , wherein the executable instructions further cause the computer system to:
receive global positioning system (GPS) data; and
build the 3D model of the landscape is further based upon the received GPS data.
20. The computer system of claim 15 , wherein the displayed representation of the 3D model comprises a 2D image of the landscape.