Patent Publication Number: US-2023141639-A1

Title: Methods and systems for using trained generative adversarial networks to impute 3d data for facilities management and operations

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/982,174, entitled “Imputation of 3D Data Using Generative Adversarial Networks”, and filed on Nov. 7, 2022, which is a continuation of U.S. patent application Ser. No. 17/031,580, entitled “Imputation of 3D Data Using Generative Adversarial Networks”, and filed on Sep. 24, 2020, which claims priority to U.S. Provisional Application No. 62/967,315, entitled “Imputation of 3D Data Using Generative Adversarial Networks” and filed on Jan. 29, 2020, each of which is incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure is generally directed to methods and systems for imputation of three-dimensional data using generative adversarial networks, and more particularly, for using a trained generative adversarial network to fill in missing data in a 3D point cloud. 
     BACKGROUND 
     Three-dimensional (3D) point clouds may include one or more gaps. The gaps may be caused by a side effect of a photogrammetric technique for generating the 3D point clouds (e.g., structure-from-motion). The gaps may be due to physical limitations of imaging of a scene. For example, imaging a scene may result in multiple unfilled spaces (e.g., black spaces) around/in structure vertical walls, under trees, etc. Gaps are generally an artifact of overhead, perpendicular imaging, wherein an imaging device (e.g., a drone) cannot “see” around objects in space. Point cloud gaps may be created, alternatively or in addition, intentionally. For example, a user may want to remove all trees from a 3D point cloud. 
     Interpolation is a relatively simple conventional technique for filling gaps in photographic data. Interpolation works by, for example, averaging pixel values around gaps. However, interpolation ignores contextual information when filling gaps. For example, interpolation may add an eyebrow to a person&#39;s face that matches the pixel values surrounding the missing eyebrow (e.g., the pixel values of the face, eye socket, forehead, etc.). However, the filled gap may not appear to be an eyebrow to a human observer, and may be jarring and not useful for practical purposes. Similar problems adhere to interpolation when used to fill gaps in other structures, such as terrain maps. 
     Inpainting is a known technique for 2D filling gaps in 2D space that takes contextual information into account, and therefore, provides human viewers with more accurate and lifelike gap filling representations. For example, conventional techniques may be able to fill in a facial feature of a person (e.g., a missing eyebrow). However, inpainting in 3D is not a conventional technique. Conventional techniques may include additional drawbacks as well. 
     BRIEF SUMMARY 
     The present embodiments may relate to, inter alia, filling gaps in 3D point clouds in a way that appears natural to a human viewer. In one aspect, a computer-implemented method for using a trained generative adversarial network to improve vehicle orientation and navigation includes: (i) loading a semantically-segmented 3D point cloud into a virtual reality simulation environment; (ii) processing the 3D point cloud to identify at least one agricultural area; and (iii) displaying an output based on the processing of the 3D point cloud in the virtual reality simulation environment, wherein the output includes at least one attribute corresponding to the agricultural area. 
     In another aspect, a computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: (i) load a semantically-segmented 3D point cloud into a virtual reality simulation environment; (ii) process the 3D point cloud to identify at least one agricultural area; and (iii) display an output based on the processing of the 3D point cloud in the virtual reality simulation environment, wherein the output includes at least one attribute corresponding to the agricultural area. 
     In yet another aspect, a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: (i) load a semantically-segmented 3D point cloud into a virtual reality simulation environment; (ii) process the 3D point cloud to identify at least one agricultural area; and (iii) display an output based on the processing of the 3D point cloud in the virtual reality simulation environment, wherein the output includes at least one attribute corresponding to the agricultural area. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The Figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each Figure depicts one embodiment of a particular aspect of the disclosed system and methods, and that each of the Figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following Figures, in which features depicted in multiple Figures are designated with consistent reference numerals. 
         FIG.  1    depicts an exemplary computing environment  100  for implementing the imputation of three-dimensional (3D) data using generative adversarial networks, according to one embodiment. 
         FIG.  2 A  depicts an exemplary environment including a 3D scene constructed by performing structure-from-motion techniques, according to one embodiment. 
         FIG.  2 B  depicts an exemplary environment including a plurality of scenes corresponding to the scene of  FIG.  2 A , according to one embodiment. 
         FIG.  3    depicts an exemplary generative adversarial network, according to one embodiment. 
         FIG.  4    depicts an exemplary photogrammetry environment, according to one embodiment. 
         FIG.  5    depicts an exemplary photogrammetry environment, according to one embodiment. 
         FIG.  6 A  depicts exemplary 3D ground truth image data corresponding to a road, according to one embodiment. 
         FIG.  6 B  depicts exemplary 3D generative image data, according to one embodiment. 
         FIG.  6 C  depicts an exemplary 3D output of a generative adversarial network, according to one embodiment. 
         FIG.  6 D  depicts exemplary ground truth image data, according to one embodiment. 
         FIG.  6 E  depicts exemplary generative image data, according to one embodiment. 
         FIG.  6 F  depicts an exemplary output of a generative adversarial network, according to one embodiment. 
         FIG.  6 G  depicts exemplary 3D ground truth image data, according to one embodiment. 
         FIG.  6 H  depicts exemplary 3D generative image data, according to one embodiment. 
         FIG.  6 I  depicts an exemplary 3D output of a generative adversarial network, according to an embodiment. 
         FIG.  7    depicts an exemplary computer-implemented method for training a generative adversarial network, according to one embodiment and scenario. 
     
    
    
     The Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein. 
     DETAILED DESCRIPTION 
     Overview 
     The embodiments described herein relate to, inter alia, imputation of three-dimensional (3D) data using Generative Adversarial Networks (GANs), and more particularly, for filling in missing information in 3D point clouds using a trained GAN. 
     The present techniques may use techniques from the field of photogrammetry and/or structure-from-motion (SFM). Photogrammetry is the science of making measurements from photographs. Structure-from-motion is a photogrammetric range imaging technique for estimating three-dimensional (3D) structures from two-dimensional (2D) objects. The 3D structures estimated in SFM may be used to construct one or more point clouds. A point cloud is a 3D data set comprising individual points that represents a scene (e.g., a courtyard including a church, the interior of a room, a model of a heart, etc.). SFM allows planar images to be converted to a 3D model such as a point cloud. The 3D point cloud may be randomly modified and used to train a GAN to fill in holes in the 3D point cloud, and/or other 3D point clouds that were not used to train the GAN. 
     The present techniques are useful for filling in information missing from images (i.e., imputing image data), whether the missing information (e.g., a gap) is due to lack of fidelity caused by imperfect perspective/capture and/or information purposefully removed from images (trees or other objects that an analyst seeks to scrub from a final model). 
     Exemplary Computing Environment 
       FIG.  1    depicts an exemplary computing environment  100  for implementing, inter alia, the imputation of three-dimensional (3D) data using generative adversarial networks. 
     The environment  100  may include an unmanned aerial vehicle (i.e., a drone)  102  in communication with a client computing system  104 , a network  106 , and a server  108 . The drone  102  may capture image data of one or more structure  110 , for example. In some embodiments, the structure  110  may be another object, of a different scale/size. 
     The drone  102  is remote from the server  108  and may be any suitable unmanned aerial vehicle. For example, the drone  102  may include a lightweight (e.g., Magnesium alloy) frame, one or more interchangeable cameras including 5.2K (or higher) video and supporting video codecs (e.g., CinemaDNG, ProRes, etc.). The drone  102  may include a high-speed camera, and other features such as obstacle detection/avoidance. The drone  102  may include landing gear. The one or more cameras of the drone  102  may be rotatable. The drone  102  may be programmable and/or operator controlled, and may include a first person video pilot camera. 
     The drone  102  may be purchased as a commercial-off-the-shelf (COTS) product or custom built. In some embodiments, the present techniques may be utilized by an entity (e.g., a government/military) using proprietary drone  102  hardware that is not available for purchase by the general public. The drone  102  may capture 2D and/or 3D video data to a local storage device and/or stream the video data to another component of the environment  100 , such as the server  108 , via the network  106 . While  FIG.  1    depicts only a single drone  102 , the drone  102  may be in communication with numerous other drones similar to the drone  102  (and/or a command drone) via the network  106  and/or other networks. For instance, the drone  102  may be part of a drone swarm or a swarm of drones. 
     The network  106  may include any suitable combination of wired and/or wireless communication networks, such as one or more local area networks (LANs), metropolitan area networks (MANs), and/or wide area network (WANs). As just one specific example, the network  106  may include a cellular network, the Internet, and a server-side LAN. As another example, the network  106  may support a cellular (e.g., 4G, 5G, etc.) connection to a mobile computing device of a user and an IEEE 802.11 connection to the mobile computing device. While referred to herein as a “server,” the server  108  may, in some implementations, include multiple servers and/or other computing devices. Moreover, the server  108  may include multiple servers and/or other computing devices distributed over a large geographic area (e.g., including devices at one or more data centers), and any of the operations, computations, etc., described below may be performed in by remote computing devices in a distributed manner. 
     The client  104  may include hardware and software components implemented in one or more devices permanently and/or temporarily affixed to, or otherwise carried on or within, the drone  102 . For example, some or all of the components of the  104  may be built into the drone  102  or affixed elsewhere within/on the drone  102  (e.g., via a USB or other data port of the drone  102 ). In one embodiment, a portion of the client  104  may be implemented using a mobile computing device (e.g., a smart phone of the user). The client  104  may include specialized hardware (e.g., one or more sensors) and computer-executable instructions for retrieving and/or receiving drone video data from the drone  102 . In some cases, the client  104  may be implemented using components of the drone  102  and a mobile computing device. The client  104  may include a processor  120 , a memory  122 , a display  124 , a network interface  126 , and a global positioning system (GPS) unit  128 . The processor  120  may be a single processor (e.g., a central processing unit (CPU)), or may include a set of processors (e.g., a CPU and a graphics processing unit (GPU)). 
     The memory  122  may be a computer-readable, non-transitory storage unit or device, or collection of units/devices, that includes persistent (e.g., hard disk) and/or non-persistent memory components. The memory  122  may store instructions that are executable on the processor  120  to perform various operations, including the instructions of various software applications and data generated and/or used by such applications. In the exemplary implementation of  FIG.  1   , the memory  122  stores at least a collection module  130  and a processing module  132 . Generally, the collection module  130  is executed by the processor  120  to facilitate collection of video data from the drone  102  and the processing module  132  is executed by the processor  120  to facilitate the bidirectional transmission of drone data (e.g., a still image, image metadata such as IMU, etc.) between the client  104  and the server  108  (e.g., sending drone data collected from the drone  102  to the server  108 , receiving instructions related to the collection of data from the server  108 , receiving/retrieving drone data, etc.). 
     The display  124  includes hardware, firmware and/or software configured to enable a user to interact with (i.e., both provide inputs to and perceive outputs of) the client  104 . For example, the display  124  may include a touchscreen with both display and manual input capabilities. In some embodiments, the client system  104  may include multiple different implementations of the display  124  (e.g., a first display  124  associated with the drone  102  and a second display  124  associated with a mobile computing device of the user). 
     The network interface  126  may include hardware, firmware and/or software configured to enable the drone  102  and/or client  104  to wirelessly exchange electronic data with the server  108  via the network  106 . For example, network interface  126  may include a cellular communication transceiver, a WiFi transceiver, and/or transceivers for one or more other wireless communication technologies (e.g., 4G and/or 5G). 
     The GPS unit  128  may include hardware, firmware and/or software configured to enable the client  104  to self-locate using GPS technology (alone, or in combination with the services of server  108  and/or another server not shown in  FIG.  1   ). Alternatively, or in addition, the client  104  may include a unit configured to self-locate, or configured to cooperate with a remote server or other device(s) to self-locate, using other, non-GPS technologies (e.g., IP-based geolocation). 
     In some embodiments, the collection module  130  (or other software stored in the memory  122 ) provides functionality for collecting drone data from the drone  102 . Drone data may include one or more images captured from a capture device, GPS location data, or other metadata (e.g., IMU). The collection module  130  may include instructions for accessing a bus or API of the drone  102  to retrieve/receive the drone data. The collection module  130  may receive/retrieve the drone data in real time as the data is generated by the drone  102 , in batches (e.g., periodically every N minutes or more frequently, wherein N is a positive integer) and/or at the end of a drone  102  flight session. When the collection module  130  is integral to the drone  102 , the collection module  130  may access the drone data via a wired connection. When the collection module is not integral to the drone but is integral to another component (e.g., a mobile device of the user), the collection module  130  may access the drone data via a wireless connection (e.g., WiFi internet, Bluetooth, etc.). 
     Using the drone  102  advantageously allows the operator of the present techniques to fly ore frequently/and cost-effectively than manned aircraft/satellite imaging. For example, the imaging can be updated daily/weekly or, in some cases, more frequently. Moreover, data may be processed as it is captured by the drone, decreasing overall latency of the environment  100 . The processing module  132  provides functionality for processing drone data from the drone  102 . The processing module  132  may retrieve/receive data from the collection module  132  and may transmit data to/from the database  136 . The processing module  132  may transmit data to/from the server  108 . The collection module  130  may collect data from one or more sensors and may store collected data in the database  150 . 
     The drone  104  may further include a sensor  140 , an electronic database  150 , and an input/output device  152 . The sensor  140  may include one or more sensors associated with the drone  102  (e.g., an airspeed sensor) and/or a mobile device of the user (e.g., an accelerometer). The sensor  140  may provide data (e.g., sensor readings) to applications (e.g., the collection module  130 ). Many types of sensors may be used, such as cameras, video cameras, and/or microphones. In some embodiments, sensors may read particular drone data. 
     The database  150  may be any suitable database (e.g., a structured query language (SQL) database, a flat file database, a key/value data store, etc.). The database  150  may include a plurality of database tables for storing data according to data storage schema. The database  150  may include relational linkages between tables, and may allow complex data types such as image blob data to be stored and queried. 
     The I/O device  152  may include hardware, firmware and/or software configured to enable a user to interact with (i.e., both provide inputs to and perceive outputs of) the client  104 . For example, the display  124  may include a touchscreen with both display and manual input capabilities. In some embodiments, the I/O device  152  includes a keyboard, one or more speakers, a microphone, etc. Via the I/O device  152 , the user may configure instructions that cause the client  104  to transmit drone data to the server  108  via the network  106 . 
     In some embodiments, the I/O device  152  and/or another module may include instructions for sending/receiving remote control instructions from a user. For example, the user may use the drone  102  remote controller (not depicted) that is coupled to the drone wirelessly to navigate/pilot the drone  102 , and/or to view live aerial video of the drone  102 . 
     The server  108  may include a network interface  158 , a processor  160 , and a memory  162 . The server  108  may include one or more transceivers configured for wireless communication over one or more radio frequency links. 
     The network interface  158  may include hardware, firmware and/or software configured to enable the server  108  to exchange electronic data with the telematics system  104  via network  106 . For example, network interface  158  may include a wired or wireless router and a modem. The processor  160  may be a single processor (e.g., a central processing unit (CPU)), or may include a set of processors (e.g., a CPU and a graphics processing unit (GPU)). 
     The memory  162  is a computer-readable, non-transitory storage unit or device, or collection of such units/devices, that may include persistent (e.g., hard disk) and/or non-persistent memory components. The memory may store one or more modules comprising sets of computer-executable instructions, such as a spatial data module  164 , a photogrammetry motion module  166 , a machine learning training module  168 , and a machine learning operation module  170 . The memory  162  may store data generated and/or used by the modules. 
     The spatial data module  164  may receive/retrieve data from the processing module  132  of the client  104 . Specifically, the spatial data module  164  may receive/retrieve drone data (e.g., 2D images, 3D images, and image/drone metadata). The spatial data module  164  may store the received/retrieved data/metadata in the memory  162  and/or in another location (e.g., in an electronic database). The spatial data module  164  may include instructions for associating video data with other data (e.g., metadata). For example, the spatial data module  164  may associate one or more image with a respective GPS location and/or IMU information received from the client  104 . The spatial data module  164  may provide data to the photogrammetry motion module  166 . 
     The photogrammetry motion module  166  may include instructions for generating 3D point clouds from 2D image data. The photogrammetry motion module  166  may be used to generate a 3D model using 2D drone data captured by an image capture device of the drone  102 . Once the drone  102  has captured several images corresponding to a scene, the photogrammetry motion module  166  may generate a 3D point cloud corresponding to the scene by analyzing the 2D drone data. The 3D point cloud may be stored in an electronic database, wherein the 3D point cloud is usable by other modules (e.g., the ML training module  168 ) for various purposes. 
     The ML training module  168  may be generally configured to load, create, train, and/or store ML models for use by the server  108  and/or the client  104 . For example, the ML training module  168  may include instructions for training a generative adversarial network ML model by analyzing ground truth data and sample generative data, as further described below. Specifically, the ML training module  168  may train a GAN to probabilistically fill holes in a 3D scene, such as those gaps caused by the inherent physical limitations of overhead imaging. The ML training module  168  may use the 3D point clouds generated by the photogrammetry motion module  166  as training data. In some embodiments, the ML training module  168  may subdivide a single 3D point cloud into many training examples, wherein each training example includes randomly generated holes. 
     For example, the ML training module  168  may retrieve/receive a 3D point cloud corresponding to a physical scene. The ML training module  168  may use as ground truth data the unaltered 3D point cloud. The ML training module  168  may use as the random sample data a modified copy of the 3D point cloud, wherein the modified copy of the 3D point cloud includes 3D holes added to the modified copy of the 3D point cloud at random locations. The ML training module  168  may randomly simulate holes that are in a variety of shapes (e.g., arbitrarily-shaped holes, geometrically-shaped holes, etc.). For example, the holes may be of different sizes and shapes (e.g., spherical shapes, cylindrical shapes, cubic shapes, rectilinear shapes, polygonal shapes, irregular shapes, etc.). 
     The ML training module  168  may train only on the removed portions. For example, in the “Swiss cheese” of the modified 3D point cloud, the points corresponding to the randomly-generated holes may be removed and retained as the ground truth points. The GAN may be trained using only those points as target points, rather than using the entire 3D image, in some embodiments. The process of removing portions from the 3D point clouds may be known as extraction. Any 3D region may be removed (e.g., a 3D polygonal region, a 3D square region, etc.). 
     In general, the ML training module  168  may train models by, inter alia, establishing a network architecture, or topology, and adding layers that may be associated with one or more activation functions (e.g., a rectified linear unit, softmax, etc.), loss functions and/or optimization functions. Multiple different types of artificial neural networks may be employed, including without limitation, recurrent neural networks, convolutional neural networks, and deep learning neural networks. Data sets used to train the artificial neural network(s) may be divided into training, validation, and testing subsets; these subsets may be encoded in an N-dimensional tensor, array, matrix, or other suitable data structures. 
     Training may be performed by iteratively training the network using labeled training samples. Training of the artificial neural network may produce byproduct weights, or parameters which may be initialized to random values. The weights may be modified as the network is iteratively trained, by using one of several gradient descent algorithms, to reduce loss and to cause the values output by the network to converge to expected, or “learned,” values. 
     In one embodiment, a regression neural network may be selected which lacks an activation function, wherein input data may be normalized by mean centering, to determine loss and quantify the accuracy of outputs. Such normalization may use a mean squared error loss function and mean absolute error. The artificial neural network model may be validated and cross-validated using standard techniques such as hold-out, K-fold, etc. In some embodiments, multiple artificial neural networks may be separately trained and operated, and/or separately trained and operated in conjunction. 
     In another embodiment, the trained ML model may include an artificial neural network (ANN) having an input layer, one or more hidden layers, and an output layer. Each of the layers in the ANN may include an arbitrary number of neurons. The plurality of layers may chain neurons together linearly and may pass output from one neuron to the next, or may be networked together such that the neurons communicate input and output in a non-linear way. In general, it should be understood that many configurations and/or connections of ANNs are possible. 
     The input layer may correspond to a large number of input parameters (e.g., one million inputs), in some embodiments, and may be analyzed serially or in parallel. Further, various neurons and/or neuron connections within the ANN may be initialized with any number of weights and/or other training parameters. Each of the neurons in the hidden layers may analyze one or more of the input parameters from the input layer, and/or one or more outputs from a previous one or more of the hidden layers, to generate a decision or other output. The output layer may include one or more outputs, each indicating a prediction or an expected value. 
     In some embodiments and/or scenarios, the output layer includes only a single output. For example, a neuron may correspond to one of the neurons in the hidden layers. Each of the inputs to the neuron may be weighted according to a set of weights W 1  through Wi, determined during the training process (for example, if the neural network is a recurrent neural network) and then applied to a node that performs an operation α. The operation α may include computing a sum, a difference, a multiple, or a different operation. In some embodiments weights are not determined for some inputs. Neurons of weight below a threshold value may be discarded/ignored. The sum of the weighted inputs, r 1 , may be input to a function which may represent any suitable functional operation on r 1 . The output of the function may be provided to a number of neurons of a subsequent layer or as an output of the ANN. 
     A processor or a processing element may be trained using supervised or unsupervised machine learning, and the machine learning program may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs. For example, a GAN trained using terrain for a portion of a large 3D scene may be able to generalize about unseen portions of the terrain. 
     The ML operation module  170  may load a model (e.g., a GAN) trained by the ML training module  168  from the memory  162  or another location. For example, the ML operation module  170  may load a trained ML model and pass a series of parameters (e.g., a 3D point cloud of a scene including holes, whether the holes are imaging artifacts or created by an administrator). The ML operation module  170  may receive from the trained GAN a copy of the 3D point cloud wherein the holes are all probabilistically filled using the generator portion of the GAN. The generated 3D point cloud with filled holes may be stored in the memory of the server  162  or in another location (e.g., in an electronic database of the server  108 ). 
     The server  180  may further include an input device  180  and an output device  182 . The input device  180  may include hardware, firmware and/or software configured to enable a user to interact with (i.e., provide inputs to) the server  108 . The output device  182  includes hardware, firmware and/or software configured to enable a user to interact with (i.e., provide inputs to) the server  108 . By using the input device  180  and the output device  182 , the user may configure the modules of the server  108 , inspect data stored in the memory  162  of the server  180 , and perform other operations. 
     In operation, a user may cause the drone  102  to overfly a scene. The drone  102  may follow a pre-determined flight path programmed into the memory  122  of the drone  102  and/or may be piloted remotely by the user. For example, the user may override the pre-programmed drone  102  flight path. The collection module  130  of the drone  102  may capture images of the scene according to a pre-determined logic/time interval, and/or at the initiation of the user (e.g., via the remote control). The collection module  130  may capture 2D images. The processing module  132  may transmit the captured 2D images and/or additional data respective to each image (e.g., GPS coordinates, metadata, etc.) immediately or after a delay to the server  108  via the network  106 . 
     The spatial data module  164  may receive the images and/or data. The spatial data module  164  may associate the images and/or data by, for example, adding a sequential identifier to each image. The spatial data module  164  may store the images in an electronic database such that the sequential orientation is preserved. In some embodiments, the spatial data module  164  may analyze the 2D images and reject/discard those for which no metadata is available, or those which are corrupted/blank or blurred. 
     Once the spatial data module  164  has stored the images, the photogrammetry motion module  166  may analyze the images to generate a 3D point cloud. The photogrammetry motion module  166  may utilize structure-from-motion techniques to plot points in the 3D point cloud from overlapping points within multiple of the 2D images stored by the spatial data module  164 . The photogrammetry motion module  164  may store the generated 3D point cloud in the electronic database, optionally in association with the plurality of 2D images used to generate the 3D point cloud. A user may rotate the generated 3D point cloud and view the point cloud in 3D space. Each point in the point cloud may include a 3D coordinate value (e.g., X,Y,Z) and an R,G,B color value taken from the images used to create the tie points. 
     Once the 3D point cloud is generated, the ML operation module  170  may analyze the 3D point cloud using an ML model trained by the ML training module  168  to fill in the holes of the 3D point cloud. GAN model training is described further below. In general, the ML training module  168  may use 3D point cloud training data that includes holes due to artifacts of an imaging process, and/or holes that are added into the 3D point cloud by a generator. The output of the ML operation module  170  may be a copy of the 3D point cloud generated by the photogrammetry motion module  166 , wherein any holes are filled by the GAN. 
     Exemplary Scene Capture 
       FIG.  2 A  depicts an exemplary environment  200  including a 3D scene  202  constructed by performing structure-from-motion techniques. The scene  202  may include one or more of capture locations  204  and one or more respective planar images  206 , wherein each of the respective planar images  206  corresponds to one of the capture locations  204 . Each of the capture locations  204  may correspond to a tie point  208 . Each of the capture locations  204  may correspond to a capture device of a flight device (e.g., a camera of a drone, such as the drone  102 ). The tie point  208  is a single recognizable feature of a structure  210  that each of the flight devices at each of the capture locations  204  have a vantage of. For example, the tie point  208  may correspond to a single feature of the structure  110  or the structure  210 , such as a church steeple. 
     It should be appreciated that the tie point may correspond to any pixel or collection of pixels that the planar images  206  commonly include. In one embodiment, a capture device at a location  204  may not have a clear view of the tie point  208 . In that case, the photogrammetry motion module  166  may analyze the planar image  206  and discard the planar image  206  generated by the capture device. Each respective capture location  204  may include a line connecting the capture location  204  to the tie point  208  and additional lines of sight connecting the capture location  204  to the edges of a planar image  206 . The lines of sight of each capture location  204  depicting a visualization of where the capture device was located when it captured the planar image  206  corresponding to the structure  210 . The photogrammetry motion module  166  may analyzes the planar images  206  to generate a point cloud. Lines of sight are further discussed with respect to  FIG.  4   , below. 
     In the example of  FIG.  2 A , the steeple of the structure  210  is the tie point  208 . The photogrammetry motion module  166  may include instructions for determining as many tie points  208  (e.g., trees, people, buildings, etc.) as possible from multiple images. For example, a point A and a point B may be visible in a first planar image  206  and a second planar image  206 , but appear in the respective planar images  206  to be of different perspective, due to differing locations of the respective capture devices used to capture the planar images  206 . The photogrammetry motion module may include instructions for extrapolate the connections between the point A and the point B and other points many times (e.g., hundreds of thousands/millions) to construct a collection of points observed from multiple perspectives. The photogrammetry motion module  166  may determine the source of change relative to one another of the points A and B, and save the source of change as the 3D space the points lie in. Once enough images are captured, the photogrammetry motion module  166  may find multiple tie points  208 , allowing triangulation of pixels per image in a 3D space/environment from 2D images (i.e., a 3D point cloud). 
     The photogrammetry motion module  166  may include instructions for tagging the 3D point cloud with additional image data for increasing density of the 3D point cloud. The photogrammetry motion module  166  may tag each 3D point in the 3D point cloud with geospatial metadata (X,Y,Z) and one or more scalar values (e.g., color or other metadata). For example, enhanced GPS (e.g., 2.5 cm accuracy) data may be collected by the drone and added to the 3D point cloud. Data from the drone  102  inertial measurement unit (IMU) may also be collected. The present techniques advantageously allow analysts to attain 3D spatial awareness (e.g., elevation) by building a 3D scene including x,y,z and color values at each respective coordinate from 2D images. 
     As described above, gaps are an unavoidable aspect of imaging. The gaps in the scene  202  appear as dark regions beneath trees and on the vertical surfaces of buildings. These are natural spots for holes to occur when a perpendicular camera capture angle is used, as the camera cannot see through solid objects. However, in some embodiments, another imaging type (e.g., LIDAR, infrared, near-infrared, thermal, etc.) may be combined with the photographic imaging of the drone  102  to provide a more complete model, with fewer gaps. Such additional/alternate imaging modalities may provide other information as well, such as vegetation health, fuel loads for wildfire analysis, dry brush, etc. 
       FIG.  2 B  depicts an exemplary environment  200  including scenes  222 -A- 222 -D, wherein each of the scenes  222 -A through  222 -D may correspond to the scene  202 . For example, the scenes  222 -A through  222 -D include a respective tie point  224  that corresponds to the tie point  208 , and a respective structure  226  that corresponds to the structure  210  of  FIG.  2 A  and the structure  110  of  FIG.  1   , for example. 
     The capture device (e.g., the drone  110  of  FIG.  1   ) may include instructions for capturing (or may be controlled/programmed to capture) images of the scene  202  from a perspective that is perpendicular to the ground (i.e., straight down), as depicted in  FIG.  2 B . The capture device may include overlap between the scenes  222 -A through  222 -D, such that one or more points (i.e., tie points) are captured in multiple images. 
     Exemplary Generative Adversarial Network 
       FIG.  3    depicts an exemplary GAN  300 . The GAN  300  includes a test input  302  and a generator  304 . The generator  304  may be an artificial neural network (ANN) that generates a generative sample  306 . Generating the generative sample  306  may include the generator  304  modifying a ground truth image by, for example, creating a geometric hole in the ground truth image. The GAN  300  may create a hole randomly, in terms of size/area, position and/or geometry of the hole. The GAN  300  further may include ground truth images  308  and a ground truth sample  310 . 
     The GAN  300  further may include a discriminator  312 . The discriminator  312  may be an ANN that accepts as input the generative sample  306  and the ground truth sample  310 . As the GAN  300  is operated, the discriminator  312  compares the generative sample  306  and the ground truth sample  310  to generate a generator loss  314  and a discriminator loss  316 . The generator loss  314  and/or discriminator loss  316  may be implemented using a loss function such as minimax. The GAN  300  may be trained to discriminate among RGB-colorspace features of images, and/or in other dimensions (e.g., with respect to a property of an image, such as elevation). 
     The GAN  300  may use a generator/discriminator pattern, wherein two neural networks (one generator and one discriminator) are concurrently trained to produce an image. The generator  304  generates an image that the discriminator  312  analyzes. The discriminator  312  attempts to determine whether the image generated by the generator  304  corresponds to a real image or a fabricated (i.e., counterfeit or fake) image. The generator  304  and discriminator  312  may comprise a feedback loop. In some embodiments, the generator  304  includes a convolutional neural network (CNN). 
     The ground truth used for training the GAN  300  may include—drone images and training images having simulated gaps. No labeling may be performed to train the GAN, and training may be fully automated. For example, the training module  168  may include randomly simulating holes in a point map corresponding to an area (e.g., a 70-acre section of land). The holes may be random in terms of shape and size, and may be uniform and/or non-uniform. Once the gaps or holes are added, the machine learning module  168  may use those portions of the point map that were removed from the holes/gaps as training data for the GAN  500 . Advantageously, by using shapes of different shape/size, the GAN learns to fill in gaps of any shape (e.g., an organic, non-symmetrical shape such as shadows cast beneath tree). By doing so, the GAN is able to be used to analyze point maps having any arbitrary holes/gaps, including those 3D point maps that were not used to train the GAN. 
     The discriminator  312  may classify one or more inputs (e.g., a 3D point cloud or a portion thereof) into a category of real or fabricated. The discriminator  312  may be trained using real images, such as images of terrain. The discriminator may be trained using fabricated images, such as images of terrain wherein some portion of the image has been deleted or removed. Fabricated images produced by the generator ANN  304  may be used to train the discriminator  312 . 
     The discriminator  312  may use backpropagation to update a set of discriminator weights. For example, an operator (e.g., the ML training module  168  of  FIG.  1   ) may provide a real image to the discriminator  312 . The discriminator  312  may classify the real image as fabricated. When the discriminator  312  incorrectly classifies an image (e.g., classifies a real image as fabricated, or a fabricated image as real), the ML training module  168  may update the discriminator loss  316  using backpropagation. When the classification of the discriminator  312  is correct, the weights may remain unchanged. 
     The generator  304  may be trained (e.g., by the ML training module  168 ) to generate image samples (e.g., the generative sample  306 ). The discriminator analyzes the generative sample  306  and produces a real/fabricated output, indicating whether the discriminator finds the generative sample  306  as corresponding to a real or fabricated item. The discriminator classification includes the discriminator loss  316 . The discriminator loss  316  is backpropagated through the GAN  300 , and the weights of the ANN of the generator  304  are repeatedly updated, improving the ability of the generator to produce samples that appear real to the discriminator. 
     The generator  304  and the discriminator  312  may not be trained simultaneously. For example, in some embodiments, the generator  304  may be trained for n epochs, followed by the discriminator  312  being trained for m epochs, wherein n and m are any positive integers. The training of n and m epochs may be repeated until the GAN  300  makes stable predictions as to the authenticity of each input. 
     In some embodiments, the GAN  300  may be used for semantic inpainting tasks, wherein portions of an image are missing (e.g., either removed from or absent from the image). The GAN  300  may fill in the information missing from the image in a way that causes the resulting filled in image to appear natural to a human viewer. 
     Exemplary Photogrammetry Environment 
       FIG.  4    depicts an exemplary photogrammetry environment  400 . The photogrammetry environment  400  may include a 3D model  402 , constructed by a series of planar images  404  being captured by a capture device (e.g., a camera of the drone  102  of  FIG.  1   ). Each of the planar images  404  may correspond, for example, to the planar images  206  of  FIG.  2 A . The 3D model  402  may correspond to the point cloud of the 3D scene  202  of  FIG.  2 A .  FIG.  4    depicts corresponding feature points of each planar image  206 , which may correspond to the tie points  208  of  FIG.  2 A . 
       FIG.  5    depicts an exemplary photogrammetry environment  500 . The photogrammetry environment  500  includes a capture device  502 . For example, the capture device may be onboard the drone  102  of  FIG.  1   . The capture device  502  may include a focal length and focal plane as determined by a lens  504 . The lens  504  may be located a distance  508  above terrain (e.g., the ground, sea level, etc.). The distance above terrain  508  may be measured as a distance  512  measured relative to sea level  510 . The scale of the image captured using the photogrammetry environment  500  may be calculated by computing the ratio of the focal length of the lens  504  of the camera  502  to the height above terrain  508 . 
     Exemplary Generative Adversarial Network Region Filling—Explicit Gaps 
       FIGS.  6 A- 61    depict exemplary images for training the generator and discriminator portions of a GAN (e.g., the GAN  300  of  FIG.  3   ) to perform various region filling tasks for 3D point clouds that include explicit (i.e., added by programmed instructions), and for operating the trained GAN to generate region-filled 3D point clouds. 
       FIG.  6 A  depicts 3D ground truth image data  600  corresponding to a road. The ground truth image data  600  depicts RGB-colorspace ground truth image data  602  and elevation ground truth image data  604 . The ground truth image data  600  may be used to train the GAN  300 . For example, the RGB-colorspace ground truth image data  602  may correspond to the ground truth sample  310 . In another embodiment, the elevation ground truth image data  604  may correspond to the ground truth sample image  310 . The ground truth image data  600  may be stored in and/or retrieved from an electronic database, such as the database  108  of  FIG.  1   . A component of the environment  100  (e.g., the ML training module  168 ) may retrieve/receive the ground truth image data  600  during training. 
       FIG.  6 B  depicts 3D generative image data  610 . The generative image data  610  includes an RGB-colorspace generative sample image  612  and an elevation generative sample image  614 . The RGB-colorspace generative sample image  612  and/or the elevation generative sample image  614  may correspond to the generative sample  306  of  FIG.  3   , in some embodiments. That is, the generative image data  610  may be produced by the ANN of the generator  304  and may be used as generative samples for training the discriminator  312  by, for example, the ML training module  168  of  FIG.  1   . 
     The RGB-colorspace generative sample image  612  and the elevation generative sample image  614  may include one or more respective holes, or gaps. The gaps may correspond to 3D cutouts added at random locations. Any 3D sub-images (e.g., cutouts) may be used (e.g., by the ML training module  168  of  FIG.  1   ) to train the discriminator portion of the GAN. Specifically, the ML training module may remove one or more 3D portions from a 3D image at random, thereby causing one or more gaps corresponding to each of the one or more removed portions. The removed portions may be used to train the discriminator as ground truth data. In this way, advantageously, the GAN may be trained in an automated way, while removing any requirement of labeling data. 
       FIG.  6 C  depicts an exemplary 3D output  620  of a GAN trained using the ground truth image data  600  of  FIG.  6 A  and/or the one or more removed 3D portions (e.g., the portions removed to create the gaps in the RGB-colorspace generative sample image  612  and/or the elevation generative sample image  614 ). Specifically, a GAN RGB output  622  represents the probabilistic, GAN-based region filling of the holes in the 3D generative RGB sample image  612  to match the surrounding region. A GAN elevation output  624  represents the probabilistic, GAN-based region filling of holes in the generative elevation sample  614 . It should be appreciated that the output  620  of  FIG.  6 C  appears similar to the ground truth image data  600  of  FIG.  6 A , but the portions of the output  620  that correspond to the holes of  FIG.  6 B  are imputed by the GAN (i.e., they are fabricated to match the surroundings and do not represent pixels that were, in fact, included in an image of a real physical object). 
       FIG.  6 D  depicts ground truth image data  630  corresponding to two sidewalks. The ground truth image data  630  depicts RGB-colorspace ground truth image data  632  and elevation ground truth image data  634 . In some embodiments, the ground truth image data  630  may be used to train the GAN  300 . For example, the RGB-colorspace ground truth image data  632  may correspond to the ground truth sample  310 . In one embodiment, the elevation ground truth image data  634  may correspond to the ground truth sample image  310 . The ground truth image data  630  may be stored in/retrieved from an electronic database, such as the database  108  of  FIG.  1   . A component of the environment  100  (e.g., the ML training module  168 ) may retrieve/receive the ground truth image data  630  during training. 
       FIG.  6 E  depicts a generative image data  640 . The generative image data  640  includes an RGB-colorspace generative sample image  642  and an elevation generative sample image  644 . The RGB-colorspace generative sample image  642  and/or the elevation generative sample image  644  may correspond to the generative sample  306  of  FIG.  3   , in some embodiments. That is, the generative image data  640  may be produced by the ANN of the generator  304  and may be used as generative samples for training the GAN  300  by, for example, the ML training module  168  of  FIG.  1   . Specifically, the rectangles of the RGB-colorspace generative sample image  642  and the elevation generative sample image  644  representing random hole and/or gap locations may correspond to removed portions of the RGB-colorspace generative sample image  642  and the elevation generative sample image  644  removed. The present techniques may include training the GAN using the removed portions. 
       FIG.  6 F  depicts an exemplary output  650  of a GAN trained using the ground truth image data  630  of  FIG.  6 D  and/or the generative image data  640  of  6 E, and/or the removed portions. Specifically, a GAN RGB output  652  represents the probabilistic, GAN-based region filling of the one or more holes in the generative RGB sample image  642  to match the surrounding region. A GAN elevation output  654  represents the probabilistic, GAN-based region filling of holes in the generative elevation sample  644 . It should be appreciated that the output  650  of  FIG.  6 F  appears similar to the ground truth image data  630  of  FIG.  6 D , but the portions of the output  650  that correspond to the holes of  FIG.  6 E  are imputed (i.e., they are fabricated to match the surroundings and do not represent pixels that were, in fact, included in an image of a real physical object). It should further be appreciated that in  FIG.  6 F , the GAN is seen to correctly and realistically fill both elevation and spatial/terrain regions wherein the holes in the generative image data  640  cover multiple divergent paths (e.g., two sidewalks). 
     Exemplary Generative Adversarial Network Region Filling—Implicit Gaps 
     As noted above, the present techniques are applicable to probabilistic filling randomly-generated (i.e., explicit) gaps/holes. The present techniques are also applicable, in some embodiments, to the probabilistic filling of gaps (i.e., blank regions) created during mapping due to the limitations of an imaging devices. For example, the present techniques may fill holes/gaps that appear in a 3D point cloud due to obstructions in the path of the imaging device used (e.g., a tree branch occluding the ground beneath). Specifically, the present techniques may be used to fill regions of images that include holes due to imaging artifacts (i.e., that include implicit gaps). 
       FIG.  6 G  depicts 3D ground truth image data  660  including a hole that may correspond to a tree. The hole, or gap, may have been included in the ground truth image data  660  as a result of an imaging artifact (i.e., an implicit gap). The ground truth image data  660  includes an RGB-colorspace ground truth image data  662  and elevation ground truth image data  664 . 
       FIG.  6 H  depicts a 3D generative image data  670 . The 3D generative image data  670  includes an RGB-colorspace generative sample image  672  and an elevation generative sample image  674 . The RGB-colorspace generative sample image  672  and/or the elevation generative sample image  674  may correspond to the generative sample  306  of  FIG.  3   , in some embodiments. The gaps in the RGB-colorspace generative sample image  672  and the elevation generative sample image  674  may be randomly generated. The portions of the 3D generative image data  670  removed randomly (i.e., the portions corresponding to the gaps) may be used to train the GAN, as discussed above. 
       FIG.  6 I  depicts an exemplary 3D output  680  of a GAN trained using the ground truth image data  660  of  FIG.  6 G  and/or the generative image data  670  of  FIG.  6 H , and/or the removed portions. Specifically, a GAN RGB output  682  may correspond to the RGB-colorspace ground truth image data  662 , wherein the gaps have been filled in probabilistically by the GAN to match the surrounding area in color space, texture, etc. An elevation output  684  may correspond to the elevation ground truth image data  664 . 
     Exemplary Floodplain Modeling 
     The present techniques are applicable to floodplain modeling using a 3D point cloud. Elevation is an important aspect for modeling a 3D point cloud due to the flow of water primarily determining flood damage. In the case of floodplain mapping, or projecting the flow of water, a 3D point cloud (e.g., the church scene of  FIG.  2 A ) that may objects such as cars, trees, lampposts, benches, etc. If the flow of water is simulated in such a point cloud, water may appear to flow around objects in a manner that is unrealistic. Thus, removing such objects may advantageously provide a better, more accurate modeling outcome. 
     The location of buildings is similarly important. The present techniques may be used to fill gaps in a 3D point cloud, to improve the ability of modeling to improve quality of floodplain analysis. The present techniques may be used to fill gaps corresponding to the removal of superfluous 3D data (e.g., trees) not relevant to creating a high quality elevation map. Removing such 3D data may create gaps/holes, as described herein. 
     The insufficiency of interpolation techniques are particularly acute in the case of floodplain modeling. In a floodplain model, interpolating across a water channel, for example, may create an artificial barrier blocking flow of water where none exists in reality. Using the GAN-based approaches of the present techniques, on the other hand, fill in information accurately, allowing realistic water flow models to be developed. In yet further embodiments, the present techniques are applicable to additional use cases, such as video game design. 
     Exemplary Computer-Implemented Methods 
       FIG.  7    depicts an exemplary computer-implemented method  700  for training a generative adversarial network, according to one embodiment and scenario. 
     The method  700  may include obtaining one or more training three-dimensional point clouds (block  702 ). The training 3D point clouds may be generated from 2D imagery via a photogrammetric process such as structure-from-motion. In some embodiments, the training point clouds may be captured by a drone such as the drone  102  of  FIG.  1   . In some embodiments, a single point cloud of a large area (e.g., a farm) may be subdivided into many smaller point clouds. 
     The method  700  may include extracting one or more three-dimensional regions from each training three-dimensional point cloud, wherein extracting the one or more three-dimensional regions from each training three-dimensional point cloud includes creating one or more gaps in each three-dimensional point cloud corresponding to each of the one or more extracted three-dimensional regions (block  704 ). For example, the ML training module  168  may analyze a point cloud and extract a region corresponding to each hole, or gap, in the point cloud. A single 3D point cloud may have many (e.g., 100 or more) gaps. The extracted regions including holes may be used as input to the GAN for training the GAN to classify a 3D image. 
     The method  700  may include training the generative adversarial network by analyzing the extracted three-dimensional regions and each three-dimensional point cloud including the respective one or more gaps, wherein the analyzing includes generating a loss value, and updating one or more weights of the generative adversarial network by backpropagating the loss value throughout the generative adversarial network (block  706 ). A generative loss may be backpropagated, and/or a discriminator loss. 
     The method  700  may include storing the updated weights of the generative adversarial network on the computer readable storage medium as parameters for initializing the generative adversarial network (block  708 ). For example, the ML training module  168  may store the updated weights once the GAN has converged (i.e., once a loss value of the GAN has met a predefined criteria). 
     In some embodiments, the method  700  may further include obtaining a three-dimensional point cloud having one or more gaps. The 3D point cloud having one or more gaps may correspond to a scene. The GAN may analyze the 3D point cloud and impute values into any gaps, such that the output of the method  700  is a point cloud having no gaps. In this way, method  700  may use the trained GAN to produce gapless point clouds. To use the trained GAN, for example, the ML operation module  170  of  FIG.  1    may initialize the GAN using weights obtained during the training phase. 
     Imputation of the gaps may include imputing RGB data, elevation data, and/or other types of data (e.g., metadata). The method  700  may impute data in the gaps on a pixel-by-pixel level, in some embodiments. The method  700  may store the three-dimensional point cloud including the imputed data in a memory. In some embodiments, the method  700  may include displaying and/or transmitting the imputed 3D point cloud. The gaps may be natural or implicit gaps (e.g., gaps created as an artifact of imaging) and/or explicit gaps added by a manual process and/or an automated/programmatic process. 
     The method  700  may include updating the one or more weights of the generative adversarial network by backpropagating the loss value throughout the generative adversarial network, which may include backpropagating discriminator loss to a discriminator artificial neural network. Updating the one or more weights of the generative adversarial network by backpropagating the loss value throughout the generative adversarial network may include backpropagating discriminator loss to a discriminator artificial neural network and a generator artificial neural network. The discriminator and generator portions of the GAN may be trained together, separately, in serial, and/or in parallel. Those of skill in the art will appreciate that discriminator weights may not be changed during training of the generator artificial neural network, and/or that generator weights may not be changed during training of the discriminator artificial neural network. Further, the discriminator and generator may be trained for a limited number of epochs in an alternating pattern. 
     Exemplary Use Cases 
     In some embodiments, the present techniques may include the application of multiple/different deep learning techniques. For example, in one embodiment, a scene may be semantically segmented using a first ML model as discussed above, and a second ML model (e.g., a CNN) may be used to determine a roof geometry. A third ML model may be used to analyze the result of the semantic segmentation and the CNN output. The semantic segmentation information generated using the above-described techniques may be included in the 3D point cloud, and is useful for further analysis/operations in many domains. 
     Vehicles &amp; Transportation 
     For example, additional use cases that may be implemented using the above-described techniques include transportation-related implementations such as autonomous vehicle mapping wherein, for example, an autonomous vehicle generates mapping information and aerial imagery is collected. For example, the aerial imagery may correspond to a rural areas wherein airspace is less tightly restricted. In another example, LIDAR is used in an urban/metro area. 
     Some embodiments may improve existing autonomous vehicle navigation and/or orientation systems. For example, the present techniques may modify an existing localization technique to provide more precise navigation by combining data from multiple systems (e.g., GPS data, LIDAR data including known landmarks, a camera system that centers on lanes, drone data, etc.). The point cloud data from each may be combined and spatially segmented using the present techniques to produce high resolution maps for the autonomous vehicle. As the autonomous vehicle operates, a module in the autonomous vehicle may analyze the spatial data in the high resolution maps (e.g., a class of an object, such as a pothole) to determine a navigation decision (e.g., a turning angle) or to provide information to a vehicle operator. 
     In one embodiment, a plurality of point clouds from multiple sources (e.g., the autonomous vehicle and the LIDAR data) may be merged together into a highly-accurate point cloud (e.g., 3D data accurate to 2.5 cm geospatially), advantageously minimizing the spatial error known to affect current-generation GPS-based systems. The combined point cloud may be used to provide high definition 3D maps that include elevation information for different purposes. For example, the combined 3D point cloud may be used in an autonomous vehicle implementation to detect potholes, analyze elevation differences for curbs, etc. 
     In another embodiment, non-color data (e.g., LIDAR) is combined with colorspace information in a point cloud. A generative adversarial network (GAN) may be used to add color information to non-color point clouds. 
     The present techniques may be used to implement functionality specific to the operation heavy equipment (e.g., a bulldozer, a tractor-trailer truck/semi, etc.). For example, semantic segmentation may be used to segment a map. While the heavy equipment is used, the slope and/or elevation information may be used by an engine control module to adjust engine speed/RPM in response to an incline. In one embodiment, one or more drones may scout a future route/future traffic to identify congestion. A route planning module may reroute the heavy equipment based upon the congestion. The drones may be used to determine future elevation. The engine control module may analyze the planning module to determine engine control decisions. 
     For example, when the engine control module identifies an aggressive slope ahead, the engine control module may increase engine torque to improve performance of the heavy equipment. When the route planning module identifies a steep decline, the engine control module may be caused to activate regenerative braking. The drones may be docked to the heavy equipment or dispatched via a waypoint along a route (e.g., at a roadside facility/installation) or a landmark (e.g., a rest area, a truck stop, etc.). The drones may facilitate the movement of multiple heavy equipment vehicles, such as assisting the entering/exiting of autonomous vehicles at a location. The drones may include self-service drones that are able to self-recharge. 
     In one embodiment, the present techniques facilitate risk assessment of an urban travel system. For example, the semantic segmentation information may be used to analyze the elevation and connection of roads/paths in a city. A significantly riskier road may be identified. A usage-based travel route may be identified wherein the risk associating with a particular road/path is used to plan travel. For example, a user may be notified (e.g., via a client computing device) that travel via a first road is longer, less expensive and/or less risky. The user may be notified that a second road is shorter, quicker, and/or more expensive. A route may be planned based upon a customer preference/objective (e.g., based upon the customer&#39;s preference with respect to a quickest path, a path including scenery, an eScooter route, etc.). A route may be planned based upon anticipated wear/tear on a vehicle due to changes in elevation or another aspect. 
     The present techniques may be used in an agricultural setting, such as in analyzing grass ways necessary for watersheds within a field. The present techniques may be used during the installation of irrigation, and/or to reduce erosion. Multi-spectral imaging may be used to determine crop health, whether fertilizers/chemicals are needed, etc. Elevation information may be used to determine whether to fill holes, to add earth, to plant an area, etc. The above-described drone technologies may be used to automate/navigate tractors such as when plowing to pick up or lower a plow based upon the elevation of a field. A drone may launch from a tractor and photograph a future route. Structure-from-motion may be used to uncover potential problems in the path of the tractor. The present techniques may be used to place field tiles and for crop rotation and planting decisions. The present techniques may be used to prevent chemical/manure runoff. 
     Facilities Operation &amp; Management 
     The present techniques may be used for facilities management. For example, the present techniques may be used in golf course modeling. A semantic segmentation 3D point cloud may allow golf course management to analyze precise terrain models. A virtual reality simulator may load a 3D point cloud to view a model/replica of the golf course for production purposes. The 3D point cloud including semantic segmentation information may be analyzed to detect erosion in a sand trap, green or fairway. For example, the 3D point cloud may be analyzed to determine how much sand is needed to replenish a sand trap, and/or to determine the acreage of a green, fairway, etc. The 3D point cloud including semantic segmentation information can be analyzed to determine irrigation and fertilizer usage, such as where irrigation heads are located in relation to one another. 
     The semantically segmented 3D point cloud may be used during play to determine the elevation/pitch/range of green in relation to where one is standing, and to determine play decisions/recommendations (e.g., club selection). The semantically segmented 3D point cloud enables an accurate base map with elevation to inform assistive play applications, such as when teaching golf or when simulation a course in electronic gaming. A course may be duplicated or replicated based upon the semantic map, in some cases, using automated fabrication methods. The present techniques may be used to calculate par and to automate the delivery of products/services during play (e.g., a beverage, a snack, etc.). The semantically segmented 3D point clouds generated by the present techniques may be used in the provision of driverless or autonomous golf carts, and to prevent/reduce wear and tear on golf carts. 
     Aspects of play (e.g., the location of players and carts) may be added to the 3D point cloud. The 3D point cloud may be used to navigate the cart, for example by preventing driving on the green. In some embodiments, a system may use both the 3D point cloud and information from another source (e.g., a smart golf ball). A golf ball may be retrieved, e.g., from a hazard, via a drone. The semantically segmented 3D may be used to automatically identify course components (e.g., a fairway, green, rough vegetation, etc.). The present techniques may color-code the semantically segmented information within the point cloud to show course shape, curve, elevation, etc. 
     The present techniques may be used to implement functionality specific to other play, such as ski resorts, beaches, etc. For example, the semantically segmented 3D point cloud may be used to determine run difficulty, for hazard identification, to determine snow depth, and/or for maintenance (e.g., to identify runoff/erosion year over year). The 3D point cloud may be used to analyze locations for new ski lifts. 
     Modeling Peril 
     The present techniques may be used to model peril, such as a natural disaster. As discussed above, the present techniques enable high quality floodplain modeling. The present techniques may be used to implement wildfire risk management by, for example, detecting fuel loads in areas that correspond to higher likelihood of a wildfire affecting a structure and/or habitation. For example, the semantically segmented 3D model may be analyzed such that a fire break may be installed in a strategic location. A mapping module may automatically determine regions in the 3D point cloud where vegetation is located close to a road, a utility company property, an insured home, an insured business, etc. 
     The “tree” label may be used to identify areas having more trees of a predetermined type (e.g., having a higher burn rate, a higher foliage density, etc.). Predictive modeling may be used to determine fire risk based upon whether trees are labeled as having leaves or needles. It should be appreciated that the ML training and/or operation discussed above may be adjusted to account for any necessary spatial/semantic features, in addition to/alternate to the examples discussed. The present techniques may analyze the size and/or features of a structure to determine the risk level represented by the structure. The proximity of multiple structures and/or objects may be analyzed to determine risk. 
     The present techniques may be used to model risk in hurricane modeling, tornado modeling, and for modeling other convective storms. For example, the density of trees and other natural dampeners may be analyzed. Wind simulation may be performed using the semantic segmentation information contained in the 3D point cloud. 
     The present techniques may be used to implement earthquake modeling. For example, some soil types are more susceptible to the propagation of seismic waves. The resolution of existing soil maps is poor. In one embodiment, one or more drones collect hyperspectral imaging to determine and/or measure soil composition. The methods and systems may semantically segment the imaging to label soil as bedrock, clay, sand, etc. As elevation information is helpful in floodplain modeling, the soil properties may determine risk to a structure (e.g., sandy soil may undergo liquefaction in an earthquake). The height and/or number of stories of a building may be modeled using the present techniques, as may construction and/or material types (e.g., framed, masonry, etc.). 
     The present techniques may be used to model a potential path of lava flow for evacuation modeling. For example, the change in elevation may be analyzed to determine the cooling of lava. Topography of the 3D point cloud may be analyzed to determine lava flow, and whether a building is at risk. Mudslides, avalanches, rock slides and other disaster scenarios may be analyzed using similar techniques. Emergency response (e.g., police and fire) may be staged based upon semantically segmented information in the 3D point cloud, by identifying safe and unsafe areas. 
     Underwriting, Claim Handling &amp; Retail 
     The present techniques may be used for underwriting, claim handling and retail purposes. In the context of underwriting, the above-described peril modeling may be used in underwriting risk assessment. A home insurance quote may be determined based upon analyzing a semantic segmentation 3D point cloud. For example, aspects of a dwelling may be counted and/or analyzed (e.g., a number of windows, a size of a garage, a number of stories, a roof composition, an estimated square footage, etc.). Additional characteristics may include a roof type, a chimney, a wall-mounted air-conditioning unit, etc. A drone may photograph storm damage to assess a damaged home, and to automatically handle a claim using detailed existing data and real-time data from a 3D point cloud. The present techniques may be implemented in the provision of crop insurance. 
     The present techniques may augment the 3D point cloud with historical customer data. When the model is missing information (e.g., has gaps) a GAN may combine 3D point cloud information with historical descriptions to fill the gaps. For example, when a structure is described in a textual description as having gray vinyl siding, the GAN may probabilistically fill the gaps using an imputed gray vinyl pattern. 
     The semantically segmented information may be used in fraud detection and for accident reconstruction. For example, a drone may capture imagery corresponding to a post-accident scene, and the scene may be semantically segmented. Certain information (e.g., broken glass, skid marks) may be used to detect the presence of an accident and/or as physical evidence (e.g., to determine speed). 
     The present techniques may facilitate access by retail customers. For example, an insurer may allow a user to purchase semantically segmented 3D point cloud data by encircling an area on a map. The insurer may provide the customer with the option to purchase one or more semantic 3D point cloud corresponding to the encircled area (e.g., a map of buildings, a map of vegetation, a map of the ground, etc.). The customer may submit one or more labels and a filtered 3D point cloud may be automatically generated and transmitted to the customer via a backend server. In some embodiments, the user may select different layers and/or models such as 3D models, orthomosaic models, etc. In one embodiment, the customer provides a 3D point cloud not including semantic segmentation information, and the insurer analyzes the customer&#39;s 3D point cloud using, e.g., the server  108  to generate a semantically segmented 3D point cloud. The semantically segmented point cloud may be delivered to the customer. 
     In some embodiments, an end user (e.g., a homeowner) flies a drone over the end user&#39;s property, and uploads images to the insurer, which provides the customer with a semantically segmented 3D point cloud, including an exterior inventory of home or other information. The insurer may provide a specific flight pattern for collecting optimal photos. With the end user&#39;s permission, the insurer may pass the segmented 3D point cloud to an underwriting and/or claims department. The claims/underwriting department may analyze the 3D point cloud in conjunction with existing data for different purposes (e.g., to detect structural changes). The present techniques may be used during the insurance application process. For example, a module operated by the insurer in the client device (e.g., an application executing in a consumer smart phone) may execute a drone flight path wherein the drone takes photographs of the applicant&#39;s home. 
     The present techniques may be used to generate physical 3D models that may include semantic segmentation information (e.g., surfaces of a different type may be printed using different colors of material using a 3D printer). 
     Smart phone applications may include the semantically segmented 3D point cloud information, and such information may be provided to developers via an API. Haptic feedback and/or voice feedback may be used to provide mapping capabilities. The height and/or slope of a workout may be analyzed, and custom workouts may be generated using the 3D point cloud information. In some embodiments, the semantically segmented 3D point clouds may be used to develop video games. 
     Construction and Urban Planning 
     The present techniques may be used in construction volumetrics. For example, in a major construction effort, the site must be cleared before building may begin, often at significant cost. Conventionally, estimates for cost are based upon crude measurements of land. The present techniques advantageously improve existing volumetrics measurement approaches by providing more precise ground elevation information. The elevation information may be used to calculate volumes of soil more accurately and to determine more refined cost estimates. The present techniques may be used in mining. The 3D map may be used to measure/analyze a building that is under construction. 
     The present techniques may be used during construction of an area (e.g., before building a residential subdivision). The segmented 3D point cloud may be used to survey land to determine locations for sewer management pumps, which may work more efficiently at particular relative elevations. The present techniques may be used to avoid building on saturated ground, for example, when installing concrete or asphalt. The present techniques may be used in utility management (e.g., for sewer, gas, power, and water). The present techniques may be used to model the location of street signs. 
     As noted above, the present techniques may be used to generate semantically segmented 3D point clouds that are very accurate. Such point clouds may be used to determine the attributes of buildings (e.g., blueprints, elevation of windows, how far windows are away from each other, the shadow of buildings, landscaping, architectural features, etc.). Such point clouds have many uses, including for historic preservation/modeling/reconstruction of historic or otherwise significant sites. Such 3D models may be combined with a customer&#39;s written description to rebuild a damaged home, filling in any gaps in the 3D point cloud with the customer&#39;s recollections. 
     ADDITIONAL CONSIDERATIONS 
     The following considerations also apply to the foregoing discussion. Throughout this specification, plural instances may implement operations or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s). The systems and methods described herein are directed to an improvement to computer functionality, and improve the functioning of conventional computers. 
     Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information. 
     As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). As used herein, the term “set” may include a collection of one or more elements. 
     In addition, use of “a” or “an” is employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise. 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for implementing the concepts disclosed herein, through the principles disclosed herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.