I’ve found it to be surprisingly effective lately.
I’ve been renovating a spatial/topology validation system for an OpenLayers driven map editor for robot fleets. I set up a clean interface for spatial indexes and predicates as well as your basic geotoolbox functions.
I was most surprised by a moment where Opus detected that using a union approach to ensure that robot footprints always sat within valid polygons (possibly straddling multiple valid polygons) was slow in testing. So it decided to find the difference between footprint and each polygon within the AABB of it. And if the remaining polygon is zero size, it’s valid. If not, the remaining shape is what is invalid.
I was also surprised just how good it was at recognizing, “in the test data there’s a lot of this kind of feature so we should explore what’s more common: hits or misses?
Before robotics I spent a lot of time building spatial models for data processing and analysis. I find that I can usually just explain the spatial question in natural language and it will figure out the relevant inputs from the dataset and what the process can be. I’m not obsolete quite yet though because maybe 10-20% of the time it goes on a pretty fantastical journey through unnecessary intermediate data products.
I’ve been renovating a spatial/topology validation system for an OpenLayers driven map editor for robot fleets. I set up a clean interface for spatial indexes and predicates as well as your basic geotoolbox functions.
I was most surprised by a moment where Opus detected that using a union approach to ensure that robot footprints always sat within valid polygons (possibly straddling multiple valid polygons) was slow in testing. So it decided to find the difference between footprint and each polygon within the AABB of it. And if the remaining polygon is zero size, it’s valid. If not, the remaining shape is what is invalid.
I was also surprised just how good it was at recognizing, “in the test data there’s a lot of this kind of feature so we should explore what’s more common: hits or misses?
Before robotics I spent a lot of time building spatial models for data processing and analysis. I find that I can usually just explain the spatial question in natural language and it will figure out the relevant inputs from the dataset and what the process can be. I’m not obsolete quite yet though because maybe 10-20% of the time it goes on a pretty fantastical journey through unnecessary intermediate data products.