3 ms·

If you do end up preprocessing the geotiff and if you already have the pipeline to give terrain elevation to user I guess you could also only encode the differe

by olup 3y ago

If you do end up preprocessing the geotiff and if you already have the pipeline to give terrain elevation to user I guess you could also only encode the difference between lidar and radar in your tiles, in order to have only trees data on top of your already served terrains data. The objects you are encoding and the precision you need could fit in as small as 4 bits, with lots of zeros that could be compressed away ? Just a brainstormy kind of comment.

tppiotrowski 3y ago
This is a good idea. There are two popular encodings of elevation data into RGB tiles. They are both not optimal in size because their value ranges need to accommodate bathymetric data (negative elevations for mapping the sea floor) height = -10000 + ((R * 256 * 256 + G * 256 + B) * 0.1) [mapbox/maptiler] height = (R * 256 + G + B / 256) - 32768 [mapzen terrarium] If you only care about elevations above sea level (0-8848 meters), you can pack the data into just two bytes maintaining a .13 meter precision (Mapbox precision is .1) height = (R * 256 + B) / (256 * 256) * 8848 [shademap] This is the encoding I'm going to use. I've already trialed it and it saves space (I'm not sure about processing time). The best encoding would be to encode the min elevation for an entire tile in the header and then just store the delta between the tile's min elevation and the elevation for a given pixel. It would be the most space efficient but would involve loading the whole tile data into memory to find the minimum elevation (which is less efficient then streaming and encoding one pixel at a time)
samstave 3y ago
UHM - I really need to know more about you/what you know - because this is a passion of mine as seeking the next thing I want to focus on - and the terminology that you have is exactly where I want my knowledge to be. Please point me at what I should study to be proficient in topological data science (which is what I want to study)
- mmyrte 3y ago
  Are you sure you mean topological data science? I know that there are topological methods for classifying high-dimensional data structures, but this discussion is mostly geographical/topographical. Yes, it does describe a surface, but there's a fundamental assumption that all objects are either on a plane or a sphere. edit: If you mean GIS (geographical information systems/science), there are plenty of undergraduate courses strewn over github. IMO, the R geospatial ecosystem is more mature than its Python counterpart, but both are very usable.
  - samstave 3y ago
    Thank you, I didnt have the vocabulary to acurately describe my interest - I appreciate it. Thanks. EDIT: I also made up "topological data science" - not sure thats a thing, but I want it to be.

by olup 3y ago

tppiotrowski 3y ago

This is a good idea. There are two popular encodings of elevation data into RGB tiles. They are both not optimal in size because their value ranges need to accommodate bathymetric data (negative elevations for mapping the sea floor) height = -10000 + ((R * 256 * 256 + G * 256 + B) * 0.1) [mapbox/maptiler] height = (R * 256 + G + B / 256) - 32768 [mapzen terrarium] If you only care about elevations above sea level (0-8848 meters), you can pack the data into just two bytes maintaining a .13 meter precision (Mapbox precision is .1) height = (R * 256 + B) / (256 * 256) * 8848 [shademap] This is the encoding I'm going to use. I've already trialed it and it saves space (I'm not sure about processing time). The best encoding would be to encode the min elevation for an entire tile in the header and then just store the delta between the tile's min elevation and the elevation for a given pixel. It would be the most space efficient but would involve loading the whole tile data into memory to find the minimum elevation (which is less efficient then streaming and encoding one pixel at a time)

samstave 3y ago

UHM - I really need to know more about you/what you know - because this is a passion of mine as seeking the next thing I want to focus on - and the terminology that you have is exactly where I want my knowledge to be. Please point me at what I should study to be proficient in topological data science (which is what I want to study)

mmyrte 3y ago
Are you sure you mean topological data science? I know that there are topological methods for classifying high-dimensional data structures, but this discussion is mostly geographical/topographical. Yes, it does describe a surface, but there's a fundamental assumption that all objects are either on a plane or a sphere. edit: If you mean GIS (geographical information systems/science), there are plenty of undergraduate courses strewn over github. IMO, the R geospatial ecosystem is more mature than its Python counterpart, but both are very usable.
- samstave 3y ago
  Thank you, I didnt have the vocabulary to acurately describe my interest - I appreciate it. Thanks. EDIT: I also made up "topological data science" - not sure thats a thing, but I want it to be.