Dialog– What qualifies as a benchmark? Part 1

Normally, my blog is a bit of a monologue.  It’s not a bad thing, but can be a little lonely.  Every now and then I get a great (and often doubtful) comments, which enhances things considerably.

What follows is some dialog about the LiDAR shootout series, largely between Etienne and Pierre, posted with their permission:


“Etienne, Stephen,

“I really appreciate the benchmark work you are doing to compare different technologies to solve your lidar data processing problem.

“However, if you want your tests to have as much credibility as possible and mostly if you wishes to publish the results on the web I would recommend that you compare horses with horses, dogs with dogs, general vector technologies with general vector technologies, raster technologies with raster technologies, specialized lidar technologies with specialized lidar technologies. Otherwise the dog will always lose against the horses. You have to be clear on that and well explain the differences and the pro’s and con’s between all the tech you will be using in your benchmark. Are you doing the test in ArcGIS using ESRI lidar technology (or general vector one)? Same question for R and PostGIS Raster (in the last case it is clear that the answer is no). In your graph you are comparing very different kind of animals without giving much chances to dogs.

“For every technologies you will also have to test different approaches in order to find the fastest one for this technology in order to compare it with the fastest one in the other technologies. Comparing a slow approach in one tech with the fastest in the other would be a bit unfair. This is what we did for PostGIS Raster. Did you do the same with R and ArcGIS?

“Basically I think it is just unfair to compare ArcGIS, R and PostGIS Raster with LAStools for working with Lidar data. Mostly if you are not using ArcGIS and R packages for dealing with lidar. Teach us you did not use them if you did not. We will all learn something.”


“As you said, the point is to compare different strategies to get the job done. That’s it. It’s not a question of being fair or not, we report facts. Maybe the bottom line is that pgraster shouldn’t be used to extract lidar height. However it can be used for other things. Well, we all learned something.

“It also emphasize the fact that there is no point_cloud type in postgis. I guess this could improve a lot the postgis results as it seems handling point cloud might be really fast. I learned that.

“Each solution has its drawbacks, for ArcGIS I think we’ll all agree about the cost of the licence and furthermore the cost of the lidar plugin. For lastools, it require a licensing for commercial use (which is really low btw compared to ArcGIS + Spatial analyst + LP360). PG raster require a postgres installation and some SQL knowledge, etc. R require to learn a new language, etc.

“Now I must acknowledge, the tests we did were on only 500k points. That could be considered a lot, and not. When dealing with lidar, one could easily expect to get more than a thousand times this quantity. But it was the limit imposed by the LP360 version I used and also a reasonable size for benchmarking. Note that pg raster was really stable in the timing (whatever the size).

“Finally, please note that Martin’s approach is new and he just released the tool a week ago. It’s to the better of my knowledge, the first one the do it.

“Pierre, explain your point better, I’m not sure I get it (or I disagree, as you can see).”

“My point is just that you have to be clear in your post about the fact that LAStool is a technology developed explicitly for dealing with lidar data. PostGIS raster is not and I guess you are not using ESRI lidar technology and I have no idea how you do it with R. The point is to compare potatoes with potatoes.

“If I would be from the ESRI side I would quickly post a comment in the post saying “Did you read this article?” http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Estimating_forest_canopy_density_and_height/00q8000000v3000000/

“A don’t know how you did it in ArcGIS but it seems that they are storing lidar data as multipoints.

“A good benchmark should precise all this.

Etienne (Pierre > quoted):

> If I would be from the ESRI side I would quickly post a comment in the post saying “Did you read this article?”

> http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Estimating_forest_canopy_density_and_height/00q8000000v3000000/
“I would reply: apart from the fact that it uses lidar data, it has nothing to do with the object of the present article 🙂 But I’d be happy to discover a better way than what I did (for all the tools btw).

“Maybe you should (re-)read the post, it’s written. For R as well. Maybe Steve, you should precise (this is my fault) that for ArcGIS, I’ve used LP360 (evaluation) to load lidar data, which was limited to 500k points. But it wasn’t involved in the computation as I used (as written in the post) «ExtractValuesToPoints».”


> So it’s a good benchmark ?

“I just think it would be more fair to create three test categories:

“1) Lidar technologies (apparently your ArcGIS test should be in this category)

“2) Vector/raster technologies (why not use non lidar techs if lidar techs are so much better?)

“3) Nearest neighbor technique using vector only technology

“I won’t comment further…


“I personally would not encourage these categories as there is some confusion (at least in my mind) on the definition of what is a «LiDAR thechnology». However I think it would be a good idea to nuance the conclusions, which I think will be done in further posts (or not). It’s also pretty clear from the RMSE measures what is the main drawback of each solution (with speed).

“I’m still open for discussion…

Stephen (me):

“Hi Pierre,
     “I agree with you in principle, but the ArcGIS aside, there are some very real opportunities for optimization on point in raster analyses with tiling, indexing, etc.  In other words, I don’t see any practical reasons why PostGIS Raster couldn’t be (nearly) as fast as an nn technique, so long as the tile size is optimized for density of the analysis in question.  The only thing that makes it a dog is its age as a technology.

(moments later after discovering the lengthy dialog…)

“It looks like I missed all the discussion here…

“The reason such benchmarks are interesting is (in part) that they are not fair.  PostGIS isn’t optimized for LiDAR analyses.  PostGIS Raster isn’t optimized for LiDAR analyses.  PostGIS Raster is very young technology which really isn’t fair.  The PostGIS development folks know this, and were interested in seeing my initial benchmarks with nn analyses for LiDAR height (last year) because they are aware of the optimizations that are not yet there for handling point clouds.  Vector and raster analyses (as we’ve run them here) should not in principle or practice substantially different in speed, so long as the query analyzer maximizes the efficiency of the techniques.  Add in multipoint optimizations and other voodoo, and well, that’s a different story.  Another aspect to this is that comparing lastools to the rest is unfair not just because it’s optimized for lidar but because it’s working at a much lower level (file level) than the abstractions a database provides.  The trade-off, off course, is that a database can provide transaction support, standardized language, automated query analyzers, etc. etc. that don’t apply for file level processing.

“What I see here, categories aside, is a need for the other 2-3 parts of this 3-4 part series– the breakdown of which techniques are used, how they’re implemented, and the rationale behind them.  Don’t worry, we’ll get there.  That’s the danger of starting with the punchline, but we’ll build the nuance in.


So, there we are.  Looks like I’m committed to building this out with a little more nuance.  🙂  Stay tuned.

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