Is there an efficient algorithm to generate a 2D concave hull?
AlgorithmGeometry2dGisConcave HullAlgorithm Problem Overview
Having a set of (2D) points from a GIS file (a city map), I need to generate the polygon that defines the 'contour' for that map (its boundary). Its input parameters would be the points set and a 'maximum edge length'. It would then output the corresponding (probably non-convex) polygon.
The best solution I found so far was to generate the Delaunay triangles and then remove the external edges that are longer than the maximum edge length. After all the external edges are shorter than that, I simply remove the internal edges and get the polygon I want. The problem is, this is very time-consuming and I'm wondering if there's a better way.
Algorithm Solutions
Solution 1 - Algorithm
One of the former students in our lab used some applicable techniques for his PhD thesis. I believe one of them is called "alpha shapes" and is referenced in the following paper:
<http://www.cis.rit.edu/people/faculty/kerekes/pdfs/AIPR_2007_Gurram.pdf>
That paper gives some further references you can follow.
Solution 2 - Algorithm
This paper discusses the Efficient generation of simple polygons for characterizing the shape of a set of points in the plane and provides the algorithm. There's also a Java applet utilizing the same algorithm here.
Solution 3 - Algorithm
The guys here claim to have developed a k nearest neighbors approach to determining the concave hull of a set of points which behaves "almost linearly on the number of points". Sadly their paper seems to be very well guarded and you'll have to ask them for it.
Here's a good set of references that includes the above and might lead you to find a better approach.
Solution 4 - Algorithm
The answer may still be interesting for somebody else: One may apply a variation of the marching square algorithm, applied (1) within the concave hull, and (2) then on (e.g. 3) different scales that my depend on the average density of points. The scales need to be int multiples of each other, such you build a grid you can use for efficient sampling. This allows to quickly find empty samples=squares, samples that are completely within a "cluster/cloud" of points, and those, which are in between. The latter category then can be used to determine easily the poly-line that represents a part of the concave hull.
Everything is linear in this approach, no triangulation is needed, it does not use alpha shapes and it is different from the commercial/patented offering as described here ( http://www.concavehull.com/ )
Solution 5 - Algorithm
A quick approximate solution (also useful for convex hulls) is to find the north and south bounds for each small element east-west.
Based on how much detail you want, create a fixed sized array of upper/lower bounds. For each point calculate which E-W column it is in and then update the upper/lower bounds for that column. After you processed all the points you can interpolate the upper/lower points for those columns that missed.
It's also worth doing a quick check beforehand for very long thin shapes and deciding wether to bin NS or Ew.
Solution 6 - Algorithm
Good question! I haven't tried this out at all, but my first shot would be this iterative method:
- Create a set N ("not contained"), and add all points in your set to N.
- Pick 3 points from N at random to form an initial polygon P. Remove them from N.
- Use some point-in-polygon algorithm and look at points in N. For each point in N, if it is now contained by P, remove it from N. As soon as you find a point in N that is still not contained in P, continue to step 4. If N becomes empty, you're done.
- Call the point you found A. Find the line in P closest to A, and add A in the middle of it.
- Go back to step 3
I think it would work as long as it performs well enough — a good heuristic for your initial 3 points might help.
Good luck!
Solution 7 - Algorithm
A simple solution is to walk around the edge of the polygon. Given a current edge om the boundary connecting points P0 and P1, the next point on the boundary P2 will be the point with the smallest possible A, where
H01 = bearing from P0 to P1
H12 = bearing from P1 to P2
A = fmod( H12-H01+360, 360 )
|P2-P1| <= MaxEdgeLength
Then you set
P0 <- P1
P1 <- P2
and repeat until you get back where you started.
This is still O(N^2) so you'll want to sort your pointlist a little. You can limit the set of points you need to consider at each iteration if you sort points on, say, their bearing from the city's centroid.
Solution 8 - Algorithm
You can do it in QGIS with this plug in; https://github.com/detlevn/QGIS-ConcaveHull-Plugin
Depending on how you need it to interact with your data, probably worth checking out how it was done here.
Solution 9 - Algorithm
As a wildly adopted reference, PostGIS starts with a convexhull and then caves it in, you can see it here.
Solution 10 - Algorithm
The Bing Maps V8 interactive SDK has a concave hull option within the advanced shape operations.
Within ArcGIS 10.5.1, the 3D Analyst extension has a Minimum Bounding Volume tool with the geometry types of concave hull, sphere, envelope, or convex hull. It can be used at any license level.
There is a concave hull algorithm here: https://github.com/mapbox/concaveman