I have a couple of AI/ML projects related to mapping things, often conservation related, with remote sensing data. Some details and packages will vary, but the process below describes how I generally approach these types of problems. Some of these tools I have only touched briefly, but I like them, and this is more an outline of how I would like to approach a new project than a retrospective look at my previous work.
We use AWS, so it makes sense to use datasets and services that are already hosted on AWS. The data discovery and loading part of this process would look somewhat different if we were using Azure and Planetary Computer, and very different if we were using GCP and Earth Engine.
All of my analysis will be done using python on an AWS VM in the same region as my data on S3, Probably using VSCode on Sagemaker or [JupyterLab](https://docs.aws.amazo
$ parted /dev/sdX mklabel gpt/dev/sdXY)
$ parted /dev/sdX mkpart esp fat32 1MiB 512MiB$ parted /dev/sdX set 1 esp on$ parted /dev/sdX set 1 boot on$ mkfs.fat -F 32 -n UEFI /dev/sdXY
HCT is a color model developed by [Google][material-hct]. It aims to solve a problem related to generating color palettes with good contrast. While HCT may seem like a revolutionary color model, the idea behind it is quite simple, take the perceptually uniform color model CAM16 and combine it with the CIE Lab's lightness.
This note describes how to connect two networks/devices/VMs over public network using Wireguard with Layer 2 support (ARP, IPv6 link-local, etc).
This can also be achieved using SSH and its "tap" tunnel, however, it does not provide the same level of latency and bandwidth as full-blown VPN such as Wireguard.
In addition, this note describes how to tunnel Wireguard over TCP connection. This may be of use if you encounter firewall in-between so, for instance, you can use TCP port 443 only.