Add logo, add intro to chapter one, add note about hardware requirements

Dockerfile

@@ -11,7 +11,8 @@ ENV JUPYTER_ENABLE_LAB=yes
 RUN conda install -c conda-forge geopandas==0.8.1 && \
   pip install \
     descartes==1.1.0 sentinelsat==0.14 rasterio==1.2.0 folium==0.11.0 \
-    jupyterlab-spellchecker \
+    jupyterlab-spellchecker nbdime \
-    jupyter-book 0.10.0 && \ 	
+    jupyter-book==0.10.0 && \ 	
   fix-permissions $CONDA_DIR && \
-  fix-permissions /home/$NB_USER
+  fix-permissions /home/$NB_USER && \
+  nbdime extensions --enable

README.md

@@ -1,4 +1,4 @@
-# Remote sensing for journalism
+# Remote Sensing for Journalism
 
 This repository contains a series of notebooks describing interaction with the Copernicus Open Acces Hub in order to obtain and manipulate earth observation data.
 The aim is to document common tasks that might make the data from the Copernicus Sentinel missions attractive for usage in data journalism reporting.
@@ -11,7 +11,7 @@ Copernicus Open Access Hub is the platform which is openly distributing the Terr
 A (free) Scihub account is needed in order to follow this documentation interactively.
 The registration form can be found at https://scihub.copernicus.eu/dhus/. 
 
-## Obtaining and running the code
+## Obtaining and Running the Code
 
 The source code lives at https://github.com/heyarne/remote-sensing-for-journalism.
 
@@ -39,6 +39,12 @@ This starts up a `JupyterLab` environment which allows you to interactively exec
 The Docker image is based on the [jupyter/scipy-notebook](https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook).
 Follow the link for more information on installed packages or other configuration details.
 
+## Hardware Requirements
+
+Note that working with this kind of data is resource intensive.
+These notebooks download or create roughly 50GB of data, most of which is occupied by compressed GeoTIFF files.
+They have been executed and tested on a virtual server with 4 CPU cores at 2.6 GHz each and 32 GB of RAM.
+
 ## Building the Jupyter Book
 
 The `jupyter-book` dependency is included in the `Dockerfile`.

_config.yml

@@ -8,7 +8,6 @@
 title: Remote Sensing for Journalism  # The title of the book. Will be placed in the left navbar.
 author: Arne Schlüter  # The author of the book
 copyright: "CC-BY-SA 4.0 2021"  # Copyright year to be placed in the footer
+logo: great-forty-foot-telescope-wikipedia.png  # A path to the book logo
 execute:
   execute_notebooks: "off"
-
-# logo                        : logo.png  # A path to the book logo

_static/custom.css

@@ -0,0 +1,4 @@
+div.navbar-brand-box a.navbar-brand img {
+  max-height: unset !important;
+  max-width: 80%
+}

_toc.yml

@@ -3,7 +3,8 @@
 - file: README
 - part: "Data retrieval and processing"
   chapters:
-  - file: "remote-sensing-research/01a Download from Scihub.ipynb"
+  - file: "sources/01 Data retrieval and processing.md"
-  - file: "remote-sensing-research/01b Plot downloaded products.ipynb"
+  - file: "sources/01a Download from Scihub.ipynb"
-  - file: "remote-sensing-research/01c Coverage analysis.ipynb"
+  - file: "sources/01b Plot downloaded products.ipynb"
-  - file: "remote-sensing-research/01 True-color pipeline.ipynb"
+  - file: "sources/01c Coverage analysis.ipynb"
+  - file: "sources/01 True-color pipeline.ipynb"

sources/01 Data retrieval and processing.md

@@ -0,0 +1,7 @@
+# Data Retrieval and Processing
+
+The first chapter contains notebooks that document the data retrieval process from the Copernicus Open Access Hub.
+
+It explains how to use [`sentinelsat`](https://github.com/sentinelsat/sentinelsat) library to interact with the API: How to specify what kind of data we are interested in with the aide of open map data from [OpenStreetMap](https://www.openstreetmap.org/) and how to download it. It then goes on to show how to use [`rasterio`](https://rasterio.readthedocs.io/) and [`matplotlib`](https://matplotlib.org/stable/index.html) to work with the data.
+
+The Sentinel-2 satellite captures light in visible and invisible parts of the spectrum. This can be used to derive different kinds of useful information about ground-level phenomena. This chapter will detail how to use it to create true-color images for different moments in time.

sources/01 True-color pipeline.ipynb

@@ -1,21 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rasterio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

sources/01b Plot downloaded products.ipynb

@@ -64,7 +64,7 @@
     {
      "data": {
       "text/plain": [
-       "PosixPath('input/raster/true_color_image/S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324.zip')"
+       "PosixPath('input/true_color_image/S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324.zip')"
       ]
      },
      "execution_count": 2,
@@ -577,6 +577,13 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.6"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,