sixy6e

Documenting a potential issue when attempting to do TileDB versioning from GDAL and PDAL.

gdal_translate to an existng TileDB array but opening with a specific timestamp results in fail.

PDAL with a writers.gdal to an existing TileDB array, again with a specific timestamp results in a fail.

The general idea is to continually update the density grid as additional pointcloud datasets get
added to the centralised pointcloud.

The desired output can be generated by outputting independent grids, and pulling them

sixy6e

Sample code for tiledb data ingestion to be used as input into cellular density calcs.

sixy6e

This toy script was put together to assist in trawling some imagery and building a small catalog.

sixy6e

In the attached data table, you have a set of 12 differential InSAR observations ("obs" in units of millimetres) 
for a single pixel observed over a patch of ground undergoing a periodic surface movement signal.

The InSAR observations are formed from 11 SAR images collected by the Sentinel-1A satellite at regular 12 day intervals.
First and second epochs for each InSAR observation are given in the attached table ("epoch1" and "epoch2").

Write and submit some code (in the language of your choice) to re-construct the periodic ground surface movement signal
from the InSAR observations.

Answer the following questions:

sixy6e

A toy script to replicate the AWS costings summary that is done manually within Excel

sixy6e

A demo that uses Python to do a little query against a STAC catalogue.

sixy6e

Toy script for looking at average geometric accuracy of USGS Landsat Collection-1.

The geometric accuracy, whilst subjective, is retrieved from the metadata.

This example is only retrieving the CEP90 (Circular Error Probable) calculated by eugl, which itself is harvesting information
calculated by gverify (Tool developed by Pinkmatter).

sixy6e

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

sixy6e

A toy script to demonstrate generating statistics across array segments/blobs/labels.
The context for the example is 2D imagery.
It'll compare two methods:

1. reverse indices provided by <https://github.com/sixy6e/idl-functions> and wrapped by <https://github.com/sixy6e/image-processing> to for ease of use in an image processing context
2. loop through id's and do a bool lookup

sixy6e

Test that each Landsat *region_code* contains a singular CRS.  Previous Collections could contain multiple CRS's for a given
*region_code*. It would occur for regions that bordered UTM zones, and a CRS for a given acquisition was generally determined
centre of the acquisition.