Cameron Bodine

Thanks for your interest and very sorry for the delay. PM does not currently have a workflow for converting rectified imagery back into a waterfall type of image. This would...

Closing due to inactivity.

**TEST** Tried rectification with COG (left) and heading (right). Since pings are rectified from their waterfall version (essentially [rubber sheeting](https://support.esri.com/en-us/gis-dictionary/rubber-sheeting)), rectification with heading has minimal effect. The rectification needs to...

Success!  But very slow... I have ideas.

Constructing sparse arrays may be the fastest. https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html

Implemented sparse arrays. Much faster. However, the `NearestNDInterpolator` is very slow. I did manage to get a mask using convex hull which works decent for masking interpolation output. Additionally, the...

Check out gdal.grid: gdal.Grid(output_raster, point_shp, zfield="elevation", outputSRS='EPSG:4326', # Replace with your desired CRS format="GTiff", algorithm="invdist:power=2", width=100, height=100) # Adjust dimensions as needed https://gis.stackexchange.com/questions/254330/python-gdal-grid-correct-use

Masking interpolated grid can be done by: - Fitting smoothed trackline - Fitting smoothed range extent - Create polygon from smoothed track and range lines

For memory issues, will need to write each chunks x, y coords to a file for each chunk. - [x] The function that calculates each pings coords in parallel needs...

Fixed the excessive memory usage issue with c1f9785d0e32039d28925680b6981a4407126969. It is not necessary to write any intermediate data files. Tried `gdal.Grid` but the process was very slow. An implementation example is...