Methods and systems for cloud-based management of images captured by aerial vehicles

1. A method comprising:

capturing image data via a vertical take-off and landing (VTOL) aerial vehicle;

receiving the captured image data;

transmitting the captured image data to a server having a processor and addressable memory via a network-connected computing device;

storing the captured image data on the server;

generating captured image metadata based on the stored captured image data;

providing access to the captured image data and captured image metadata via an image management component;

displaying, by the image management component, the captured image data;

filtering, by the image management component, the captured image data based on the generated captured image metadata;

correlating, by the image management component, a first captured image data with a second captured image data; and

displaying, by the image management component, both the first captured image data and the second captured image data, wherein a visual dividing line separates the first captured image data from the second captured image data, the visual dividing line being moveable by a user.

2. The method of claim 1 , wherein the VTOL aerial vehicle comprises a plurality of sensors.

3. The method of claim 2 , wherein the plurality of sensors comprise an RGB sensor.

4. The method of claim 2 , wherein the plurality of sensors comprise a LIDAR sensor.

5. The method of claim 2 , wherein the plurality of sensors comprise one or more multi-spectral cameras.

6. The method of claim 1 , further comprising:

co-registering, by the image management component, the captured image data with at least one pre-loaded image to a high level of precision.

7. The method of claim 1 , further comprising:

filtering, by the image management component, the displayed captured image data based on a plurality of spectrums selected by the user.

8. A method comprising:

defining a ground region for capturing one or more images;

receiving image data of the defined ground region from an aerial vehicle and one or more satellite images, wherein the aerial vehicle is a vertical take-off and landing (VTOL) aerial vehicle;

associating each received image data from the one or more satellites with a respective latitude and longitude;

co-locating each received image data from the aerial vehicle with the received image data from the one or more satellites;

processing the image data;

storing the processed image data;

providing access to the processed image data via an image management component;

correlating, by the image management component, a first captured image data with a second captured image data; and

displaying, by the image management component, both the first captured image data and the second captured image data, wherein a visual dividing line separates the first captured image data from the second captured image data, the visual dividing line being moveable by a user.

9. The method of claim 8 , wherein the VTOL aerial vehicle is a VTOL unmanned aerial vehicle (UAV).

10. The method of claim 8 , wherein the received image data comprises multi-spectral images of the pre-defined ground region.

11. The method of claim 10 , wherein the multi-spectral images comprise at least one of: red, green, blue, infra-red, and ultra-violet spectrums.

12. The method of claim 8 , further comprising, prior to storing the image data:

verifying an integrity of the received image data, wherein verifying the integrity of the received image data comprises scanning the received image data for viruses.

13. The method of claim 8 , further comprising:

adding at least one of: a season date range for the defined ground region, one or more crop types for the defined ground region, one or more tags to the stored image data, and one or more notes to the stored image data via a dashboard component.

14. The method of claim 8 , further comprising:

determining a canopy coverage data of the defined ground region based on the stored image data, wherein the determined canopy coverage is based on a percent of the defined ground region that is covered by vegetation;

determining an anomaly level of the defined ground region based on the stored image data, wherein the determined anomaly level is based on a percent of the defined ground region that may have anomalies; and

sending a message when the determined anomaly level exceeds a set amount.

15. The method of claim 8 , further comprising:

generating a normalized difference vegetation index (NDVI) profile of the defined ground region based on the stored image data.