[piltboy]

TIFF is still very much alive in certain circles, see for example https://cogeo.org/ The format is basically a TIFF file with attached georeferencing information and with the data organized by geographical sector, enabling fast downloads of regional subsets.

[abram]

The Adobe DNG standard for raw camera images is based on TIFF as well. DNG is used in lots of places, including the raw capture support built into all modern iOS and Android smartphones.

I’ve been using both TIFF and DNG this very week in my work (https://filmlabapp.com), so I was happy to read this post and learn about Steve Carlsen aka Mr. TIFF, whose work we’re still building on 39 years later.

[mark-r]

Not only DNG, but I think most (maybe all?) raw camera files are based on TIFF.

[omoikane]

The top 3 (Canon, Sony, Nikon) are all based on TIFF, and that accounts for the majority of the market share. Search for "TIFF-based" here:

https://exiftool.org/#supported

[buildbot]

Not the .IA files from old Sinar digital backs! Those are based on DOOM PWADs (lol). But otherwise mostly yes this is true for nearly every other format as far as I am aware.

[geokon]

I'm not super knowledgeable about this stuff.. but out of curiosity, what advantage does it have over NetCDF?

I wrote a program processing GeoTIFF data. When I had started this project I chose GeoTIFF mostly b/c i wanted something simple. And I could load them in to Java's BufferImage class and manipulate them that way. But it seems all the pros exclusively use NetCDF and GeoTiffs are for noobs (working with atmospheric science data here)

GeoTIFF does extend "images" to cover more usecases, but a lot of stuff doesn't fit (like say a wind vector) and then you need some other container or metadata b/c you generally have many images. So I get the sense the complexity just ends up being moved elsewhere.

[piltboy]

Generally speaking I would classify TIFF and its variants as imaging formats (or for very simple numerical datasets), and NetCDF as more suited for raw data, in particular multi-dimensional data with time series, etc.

For forecast and climatological data I find NetCDF is vastly superior, but also much more complicated to work with due to the capabilities and how open the format is. Just have a look at the complexity of the CF Conventions to see what I mean: https://cfconventions.org/cf-conventions/release/v1.12.0/cf-...

For visualizing orthophotos and the like, I would choose GeoTIFF any day of the week, as they're easy to visualize across platforms using existing libraries. Using COGs you also get the functionality of a spatial index within each GeoTIFF file, meaning that you can stream subsets of GeoTIFF files without having to scan through the entire file for each request.

[geokon]

Yeah the complexity of NetCDF was the ick factor that made me use GeoTIFF. It's just not the level of complexity I wanted to deal with in-program. I didn't want to wade through the details to understand the format, and I didn't want to then lock users to some subset

Seemed easier to let users preprocess their NetCDFs into GeoTIFFs manually. I have a bunch of hacky scripts to massage NetCDFs from different sources in to compatible GeoTIFFs

> meaning that you can stream subsets of GeoTIFF files without having to scan through the entire file for each request.

Interesting. My performance bottleneck right now is the user selects a small regions and then the program has to read in GBs of global precipitation maps (from IMERG) and cut out tiny squares. In the extreme cases it can mean ~2 minutes of waiting for a result. This means the user can't casually select and try out different regions with quick feedback. If you have a beefy machine you can keep it all in RAM sometimes and it works better.. but it's not ideal (my 16GB machine can only handle simpler scenarios)

I'll take a closer look at in the future. At the moment I just use Java's default TIFF reader and ImageBuffer class. Maybe it'd be easy to convert to COG format and adapt in a COG reader