If I have understood correctly, all raster images should be on top of each other?
For this, you can use the tool translate from gdal. To use it in QGIS, you can look for the tool "Translate (convert format)" and then add under "Additional command-line parameters" the parameter:
-a_ullr <upper left x> <upper left y> <lower ...
There is no need to download EPSG database from epsg.org because the Proj library comes with a copy of that database since version 6.0 and therefore every QGIS user has it available. The name of this SQLite database if "proj.db" and it is located in the PROJ_DATA directory that is for example in OSGeo4W installations C:\OSGeo4W64\share\proj.
You can generate an entire QGIS project showing all SRS code from QGIS internal srs.db using PyQGIS. It adds all layers from srs.db. I also directly style the layer to get transparent bbox.
I've taken a similar approach to @JGH but fully automated on all platforms. The additional layers can be considered garbage or useful depending of your use case. My code ...
Yes, QGIS holds this information in an SQLite table.
Go the menu layer / data source manager and select Browser then go to where QGIS is installed (like C:\Program Files\QGIS 3.16\) and dig down to apps\qgis-ltr\resources\ (or \qgis-dev\) and at last open the srs.db and add tbl_bounds
To view the bounds as geometries, we will need a virtual layer.
Go the ...
I can't speak to whether there's complete overlap between the two, but you can do this using EPSG's database. If you're not comfortable with SQL, I would just download the Access file (assuming you have that software), and load the table called 'Extent.' You'll find the four corners of the SRID's extent there, which you can then convert into a bounding box.
You can get the distance between two ee.Geometry instances:
var coords = [
var width = distanceBetween(coords, coords)
var height = distanceBetween(coords, coords)
If you're still interested in this problem, I posted a few web articles describing techniques for computing a lake or reservoir volume at:
The software I wrote to do this is all in Java, but the ideas should be applicable to other software environments.
Also, if you have your data in the ...
You can significantly reduce number of potential candidates by applying spatial join of end points to line segments:
Join them to segments:
arcpy.SpatialJoin_analysis("ends", "segments", "in_memory/sj", "JOIN_ONE_TO_MANY",.. , "INTERSECT", **"70 Meters"**)
and find frequency of highlighted field: