Systems and methods for extracting and vectorizing features of satellite imagery

1. A computer-implemented method, comprising:

generating, via a machine learner, a pixel map, wherein the generation comprises outputting a plurality of pixels associated with one or more intermediate values that do not satisfy a criterion for indicating whether each of the plurality of pixels belongs to an object of a same type as a first object;

adjusting a value of each pixel associated with one of the one or more intermediate values such that the each pixel is determined to belong to the object of the same type as the first object or determined not to belong to the object of the same type as the first object;

converting the pixel map into a plurality of first vectors, where the conversion includes a raster phase where the pixel map is converted into skeleton vectors by:

a morphological-cleanup via performing erosion to remove noise and artifacts at a pixel level; and

skeletonization via thinning the pixel map to a thickness of one pixel; and

storing, in a file type, the first vectors such that a software application is operable to edit the first object represented by at least one of the first vectors.

2. The method of claim 1 , further comprising:

performing vectorization, which comprises converting the thinned pixel map to vector form by:

traversing a skeleton map to find one or more neighboring pixels, which represent one or more second objects of a same type as the first object, within a predetermined pixel distance;

extracting a vector in a direction of each of the one or more found pixels; and

combining the one or more extracted vectors.

3. The method of claim 2 , further comprising:

performing smoothing, which comprises removing a crookedness of the combination.

4. The method of claim 1 , wherein the conversion further comprises a vector phase, which includes improving quality and shape of second vectors of the pixel map and removing artifacts by:

performing connectivity-graphing, which includes adding the second vectors to a connectivity graph; and

performing gap-jumping, which includes detecting gaps at dead-ends in the connectivity graph.

5. The method of claim 1 , further comprising:

performing cluster-collapsing by:

clustering a plurality of nodes that have distances from each other that each satisfies a criterion; and

joining objects that are not connected to each other and that have distances from each other that each satisfies another criterion.

6. The method of claim 1 , further comprising:

performing spur-removal, which includes:

detecting one or more spurs at a dead-end of a line; and

removing the one or more spurs, which are shorter than a threshold length.

7. The method of claim 4 , further comprising;

performing intersection-repair, which includes:

repairing one or more four-way intersections by collapsing detected instances of two three-way intersections; and

repairing one or more three-way intersections, including when one object intersects but does not pass through another object of the same type, by utilizing the connectivity graph to find distorted intersections and by ignoring an area near the distorted intersection such that:

two lines are colinear; and

the one or more repaired intersections is placed where a colinear pair and third objects meet.

8. The method of claim 7 , further comprising:

performing vertex reduction, which includes processing the lines to reduce vertex count.

9. The method of claim 1 , further comprising:

determining whether each pixel of the map belongs to an object of a same type as the first object.

10. The method of claim 1 , further comprising:

transforming the first vectors from a pixel-space to a coordinate system,

wherein the transformed vectors are stored.

11. The method of claim 9 , wherein the same type is a road or building.