System and method for large scale crowdsourcing of map data cleanup and correction

1. A system for large-scale crowd sourcing of map data cleanup and correction, comprising:

an application server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to:

select an orthomosaic image of a portion of the Earth's surface corresponding to a particular geographic location;

automatically select a plurality of smaller images each drawn from a portion of the orthomosaic image, at least a portion of the plurality of images being selected such that, when combined, they cover a particular area of interest;

transmit, over a network, at least a portion of the plurality of smaller images to each of at least two user devices;

receive, over the network, tagging data from at least one of the user devices, the tagging data corresponding to a plurality of objects and locations identified by the user of the device;

provide at least a plurality of tags to a crowdsourced search and locate server, the tags being based at least in part on received tagging data from at least a user device;

a crowdsourced search and locate server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to:

receive tagging data corresponding to a plurality of objects and locations from an application server;

compute agreement and disagreement values for at least a portion of the tagging data;

perform at least an expectation-maximization analysis process based at least in part on the computed values; and

a map data server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to store and provide map data.

2. The system of claim 1 , wherein the image portions contain at least a three-dimensional viewable images of said area, aiding crowdsourcing participants to better recognize difficult objects.

3. The system of claim 1 , further comprising a client interface application comprising a plurality of programming instructions stored in a memory operating on a network-attached computer and adapted to display a plurality of interactive elements to a user, receive input from the user, and provide the results of the input to the application server.

4. The system of claim 3 , wherein the crowdsourced search and locate server provides analysis results to the client interface application for display to a user.

5. A method for conducting crowdsourced search and locate operations, comprising the steps of:

receiving, at an application server, a plurality of communication connections from a plurality of user devices via a communication network;

navigating a to a particular geographic location based at least in part on input received from a first user device;

selecting an orthomosaic image of a portion of the Earth's surface corresponding to the particular geographic location;

automatically selecting a plurality of smaller images each drawn from a portion of the orthomosaic image, at least a portion of the plurality of images being selected such that, when combined, they cover a particular area of interest;

transmitting, over a network, at least a portion of the plurality of smaller images to each of at least two user devices;

receiving, over a network, tagging data from the first user device, the tagging data corresponding to a plurality of objects and locations identified by the user of the device;

reducing geolocation error for a plurality of features on the earth by analyzing tagging data pertaining to each respective feature; and

updating at least a portion of the orthomosaic image by labeling a plurality of features on the earth, the features being based at least in part on at least a portion of the tagging data.

6. A method for estimating location and quality of a set of geolocation data, comprising the steps of:

receiving, at a crowdsourced search and locate server, tagging data corresponding to a plurality of objects and locations;

computing agreement and disagreement values for at least a portion of the tagging data;

computing maximum likelihood values for at least a portion of the tagging data, the likelihood values being based at least in part on the computed agreements and disagreement values;

merging a plurality of vectors based at least in part on the computed likelihood values; and

producing final tag and vector values based at least in part on the results of analysis performed in previous steps.

7. The method of claim 6 , further comprising the steps of:

iteratively performing analysis over a plurality of tags and values; and

when a threshold is reached, stopping iteration and producing final values.

8. The method of claim 7 , wherein the threshold is based at least in part on the quantity of data remaining for analysis.

9. The method of claim 7 , wherein the threshold is based at least in part on a value configured by a user.