A farming machine moves through a field and performs one or more farming actions (e.g., treating one or more plants) in the field. Portions of the field may include moisture, such as puddles or mud patches. A control system associated with the farming machine may include a traversability model and/or a moisture model to help the farming machine operate in the field with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the farming machine attempting to traverse an untraversable portion of the field, and the control system may employ the moisture model to reduce the likelihood of the farming machine performing an action that will damage a portion of the field.

Various aspects of a system and a method for determining a position of one or more multi-dimensional objects are disclosed herein. In accordance with an embodiment, the system may include a memory and a processor. The processor may be configured to obtain, from a plurality of satellite images, shadow data of a first multi-dimensional object from one or more multi-dimensional objects on a visible surface. The processor may be configured to obtain, from a server, base elevation data and height data of the first multi-dimensional object. The processor may be further configured to generate a Digital Elevation Model (DEM) of the plurality of multi-dimensional objects. The processor may be further configured to determine a position of a second multi-dimensional object of the plurality of multi-dimensional objects on the visible surface, based on the generated DEM.

Various aspects of a system and a method for determining a position of one or more multi-dimensional objects are disclosed herein. In accordance with an embodiment, the system may include a memory and a processor. The processor may be configured to obtain, from a plurality of satellite images, shadow data of a first multi-dimensional object from one or more multi-dimensional objects on a visible surface. The processor may be configured to obtain, from a server, base elevation data and height data of the first multi-dimensional object. The processor may be further configured to generate a Digital Elevation Model (DEM) of the plurality of multi-dimensional objects. The processor may be further configured to determine a position of a second multi-dimensional object of the plurality of multi-dimensional objects on the visible surface, based on the generated DEM.

Methods and systems estimate crop coefficients of a crop. At least one image sensor system captures a plurality of multispectral images of the crop and image data is derived from the multispectral images. At least one vegetation index of the crop is determined based on image data in at least a first spectral band. The reflectance of the crop monotonically increases and reaches a reflectance of at least 20% for at least one wavelength in the first spectral band. A crop coefficient of the crop is estimated based on the determined at least one vegetation index.

Embodiments herein provide a method for generating a real-time radio coverage map in a wireless network by a network apparatus. The method includes: receiving real-time geospatial information from one or more geographical sources in the wireless network; determining handover information of at least one user equipment (UE) in the wireless network from a plurality of base stations based on the real-time geospatial information; and generating the real-time radio coverage map based on the handover information of at least one UE and the real-time geospatial information.

Embodiments herein provide a method for generating a real-time radio coverage map in a wireless network by a network apparatus. The method includes: receiving real-time geospatial information from one or more geographical sources in the wireless network; determining handover information of at least one user equipment (UE) in the wireless network from a plurality of base stations based on the real-time geospatial information; and generating the real-time radio coverage map based on the handover information of at least one UE and the real-time geospatial information.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

A method of producing a model to detect changes in forest cover is disclosed. The method includes obtaining forest-cover classification data of a land area. The land area includes one or more subregions having unchanged forest-cover classifications between a first time and a second time. The method further includes obtaining image data of the subregions at multiple times. For at least one forest-cover classification, the method includes applying a statistical analysis to the image data to determine one or more threshold values representing measurement variations. The method further includes comparing subsequently obtained image data to the one or more threshold values and classifying the one or more subregions as changed or unchanged based on the comparison of subsequently obtained image data to the one or more threshold values.

A method of producing a model to detect changes in forest cover is disclosed. The method includes obtaining forest-cover classification data of a land area. The land area includes one or more subregions having unchanged forest-cover classifications between a first time and a second time. The method further includes obtaining image data of the subregions at multiple times. For at least one forest-cover classification, the method includes applying a statistical analysis to the image data to determine one or more threshold values representing measurement variations. The method further includes comparing subsequently obtained image data to the one or more threshold values and classifying the one or more subregions as changed or unchanged based on the comparison of subsequently obtained image data to the one or more threshold values.

A method is provided. An unmanned aerial vehicle (UAV) is operated. A position of the UAV is determined while in flight, and a nonce is generated. A Merkel root is generated based at least in part on a timestamp and the position of the UAV. A current block is calculated based at least in part on a previous block, the Merkel root, and the nonce, and the current block, the timestamp, the nonce, the prior block, and the position of the UAV are transmitted.