celoyd

#!/usr/bin/env python

"""Simple slitscan generator

$ slitscan.py source slice destination

Where:
- source is a video or a directory of images;
- slice is either "c" for column or "r" for row
  and an integer, e.g., r1 or c42; and

celoyd

from skimage import io
from pathlib import Path
from sys import argv

srtm_dir = Path(argv[1])

width = 3601  # pixel width of one of these srtm tiffs

starting_lon = -2
ending_lon = 143

celoyd

Notes on a big SAR swath

Introduction

I just had a big mug of coffee and now I have some black tea and here is a slapdash devlog about a “A lightly annotated SAR view from Los Angeles to Woss”.

Summary

This is basically a combination of several conversations I’ve had with friends about different aspects of the project.

celoyd

This is how to make animations like this (alternate), inspired by Paul Norman’s. This is a write-up of a one-off 45 minute project, so it’s rough around the edges and probably has a few typos; feel free to point them out. It’s mostly command-line work, using tools like GNU parallel and ImageMagick convert; it’s slow and wastes a lot of filesystem space compared to a more monolithic approach in (say) python, but it’s very flexible.

1. Get data

I use curl globs, for example:

mkdir xzs
cd xzs
curl -O 'https://planet.openstreetmap.org/tile_logs/tiles-2022-[01-12]-[01-31].txt.xz'

celoyd

"""
draw_osm_day.py osm_tile_traffic_file.txt output_image.tiff

Write a float32 TIFF representing tile traffic in the OSM aggregate log.

Charlie Loyd, 2023-09-14. Inspired by https://en.osm.town/@pnorman/111062780453196500.

I recommend downloading the .xz compressed files and using xzcat to feed
them to this script. (Doing xz decompression in-script was slow.) E.g.,

celoyd

Dear Ms Liang,

I am an Oakland resident and a past and future user of OAK.

I want a plan to make OAK a source of local pride and global leadership in responding to the climate crisis. That is not the plan on the table today. This plan only gestures at the climate crisis and aviation’s role in it, and invests nothing serious to help solve it. In fact, what’s proposed would make OAK an increasing part of the problem for years to come.

The plan is insufficient to our moment. I oppose it firmly enough to invest my personal time and resources to publicly work against it.

I hope you will reconsider and submit a better plan. I would expect it to start from the principle that any change to OAK must make it responsible for less CO2e emission. That is, the net climate impact of the airport, calculated globally and not just to the mixing level, and operating as designed, must be at worst steady.

celoyd

import numpy as np
from skimage import io
import time
import datetime
from ciso8601 import parse_datetime
from sys import argv

timestep = 60  # in seconds
lonsteps = 1  # pixels per degree, i.e., 1 means an image 360 wide, 2 means 720

celoyd

celoyd

Learning to despeckle sar with speckly targets

These are notes from a one-day project to test a hunch. The idea is to train a convolutional neural network to remove speckle from sar (synthetic aperture radar) using only one other observation – with its own speckles – as the target. This method does not come close to state of the art despeckling, and can be biased by the skewed distribution of noise in a way that makes it useless for quantitative research. However, I hadn’t noticed it in the literature and I think it’s kind of funny, so I’m writing it up.

Everything here is about Sentinel-1 L1 GRD-HD data, since it’s what I used, since it’s free.

Speckle

Sar observations contain speckle, a form of interference related to the sparkles in reflected laser light. By some definitions speckle is not noise, since it’s physically real outside the sensor and contains information, but we will treat it as noise. Speckle is (close enough to) independent between radar chirps, a.k.a. looks, and even its distributio

celoyd

Pansharpening notes, mid-2021

First posted in August 2021. This is basically a snapshot of my thinking about pansharpening at that time; I’m not making any substantial updates. Last typo and clarity fixes in February 2023.

Preface

This is a collection of notes on how I’ve been approaching convolutional neural networks for pansharpening. It’s an edited version of an e-mail to a friend who had asked about this tweet, so it’s informal and somewhat silly; it’s not as polished as, say, a blog post would be. It’s basically the advice I would give to an image processing hobbyist before they started working on pansharpening.

If you want a more serious introduction, start with the literature review in Learning deep multiresolution representations for pansharpening. Most of the academic work I would recommend is mentioned there.