Image processing of aerial imagery for energy infrastructure analysis using joint image identification

1. A computer-implemented method for processing images to identify Energy Infrastructure (EI) features, the method to be carried out by at least one processor executing computer instructions, the method comprising:

receiving an image comprising at least one captured image of a portion of terrain;

determining an identification of a first EI feature of a first EI feature type at a first location within the portion of terrain, responsive to applying an EI feature recognition model to the image to identify the first EI feature at a first confidence level according to image content of the image;

retrieving from a supplemental information source, an identification of a second EI feature of a second EI feature type and a second location in the portion of terrain; and

updating the identification of the first EI feature to a second confidence level based at least on the first confidence level, the identification of the second EI feature, and information on a relationship between the first EI feature and the second EI feature.

2. The computer-implemented method of claim 1 , wherein the identification of the second EI feature includes a third confidence level.

3. The computer-implemented method of claim 2 , wherein updating the identification of the first EI feature to the second confidence level is further based on the third confidence level.

4. The computer-implemented method of claim 1 , wherein updating the identification of the first EI feature to the second confidence level is further based on the first EI feature type and the second EI feature type.

5. The computer-implemented method of claim 1 , wherein updating the identification of the first EI feature to the second confidence level is further based on the first location and the second location, or a distance therebetween.

6. The computer-implemented method of claim 1 , wherein the first EI feature type and the second EI feature type are members of a set of EI feature types including one or more of:

an EI development site,

a frac-water pit, frac pond or frac water impoundment,

a well pad,

a drilling rig,

pipeline infrastructure,

a service road,

a clearing,

a vehicle or truck,

a tank battery,

a proppant store,

a drilling reserve pit,

a frac spread,

a sand mine,

a producing well,

a flare system,

solar panel mounts,

solar panels,

an electrical substation,

a security fence,

a building,

a cable system,

a wind energy collector,

meteorological monitoring equipment,

construction equipment,

hydroelectric reservoirs or forebays,

hydroelectric intake structures,

penstocks,

surge chambers,

a hydroelectric power house, and

a hydroelectric tailrace.

7. The computer-implemented method of claim 1 , wherein the EI feature recognition model is a first EI feature recognition model, the method further comprising applying a second EI feature recognition model to the image to determine the identification of the second EI feature according to image content of the image.

8. The computer-implemented method of claim 7 , further comprising generating the first EI feature recognition model by training a neural network to recognize EI features of the first EI feature type and generating the second EI feature recognition model by training a second neural network to recognize EI features of the second EI feature type.

9. The computer-implemented method of claim 1 , wherein the supplemental information source includes information on at least one of:

oilfield permits,

easements,

building permits,

energy infrastructure development proposals, consents or reports,

maps,

deeds,

prospective land transactions,

land use proposals or consents,

mineral rights,

contracts,

news and media,

seismology information,

field data,

weather data,

legal information,

GPS and location information,

sensor data, and

hydrological information.

10. The computer-implemented method of claim 9 , wherein the supplemental information source is a public records information source or an EI feature database.

11. The computer-implemented method of claim 1 , further comprising updating a database of EI features with a data record including:

the first location, and

the first EI feature type.

12. The computer-implemented method of claim 11 , wherein updating the database of EI features is conditional on the second confidence level meeting or exceeding a predetermined confidence threshold.

13. The computer-implemented method of claim 11 , wherein the data record includes the second confidence level.

14. The computer-implemented method of claim 11 , further comprising identifying a status attribute of the first EI feature, and wherein the data record includes the status attribute.

15. The computer-implemented method of claim 14 , wherein the status attribute includes one or more of:

a detection date or time,

a location,

a type,

an area, length, width, height, size or volume,

a number,

a number of sub-parts,

a surface rights owner,

a mineral rights owner,

a fluid level or volume,

a fluid or material type,

a fluid or material color,

a fluid or material quality,

a fluid or material attribute,

a surface type,

a level of activity, inactivity or operation,

a brightness, intensity or spectral content,

a connectivity,

a power delivery or capability,

a bore size, and

a flow rate.

16. The computer-implemented method of claim 1 , wherein the information on the relationship between the first EI feature and the second EI feature includes one or more of:

a functional or operational dependence between the first EI feature and the second EI feature,

a functional or operational dependence between the first EI feature type and the second EI feature type,

a surface-rights ownership of the first location and the second location,

a mineral rights ownership of the first location and the second location,

a statistical distribution of a distance between EI features of the first EI feature type and the second EI feature type,

a conditional probability associated with the first EI feature and the second EI feature,

a conditional probability associated with the first EI feature, the second EI feature, and a distance therebetween,

an association of the first location or the second location to an identifier or location of an existing or potential EI site; and

an association of the first EI feature or the second EI feature to an identifier or location of an existing EI site or potential EI site.

17. The computer-implemented method of claim 1 , wherein the image includes an image time-sequence, the image time-sequence including a plurality of captured images of the portion of terrain, each captured image of the plurality taken at one of a respective plurality of image capture times, and wherein applying the EI feature recognition model includes recognizing a stage of EI feature development based on the first EI feature in the image.

18. A system for processing images to identify Energy Infrastructure (EI) features, the system comprising:

a non-transitory computer readable memory unit;

at least one processor configured to execute computer instructions to:

receive an image comprising at least one captured image of a portion of terrain;

determine an identification of a first EI feature of a first EI feature type at a first location within the portion of terrain, responsive to applying an EI feature recognition model to the image to identify the first EI feature at a first confidence level according to image content of the image;

retrieve from a supplemental information source, an identification of a second EI feature of a second EI feature type and a second location in the portion of terrain; and

update the identification of the first EI feature to a second confidence level based at least on the first confidence level, the identification of the second EI feature, and information on a relationship between the first EI feature and the second EI feature.