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Rename files and fix remaining errors with headlines
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10 changed files with 629 additions and 766 deletions
7
_toc.yml
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_toc.yml
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@ -4,20 +4,21 @@
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title: "Remote Sensing for Journalism"
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- part: "Data Retrieval and Processing"
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chapters:
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- file: "sources/01 Data retrieval and processing.md"
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- file: "sources/01 Overview.md"
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- file: "sources/01a Download from Scihub.ipynb"
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- file: "sources/01b Plot downloaded products.ipynb"
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- file: "sources/01c Coverage analysis.ipynb"
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- file: "sources/1d Brandenburg mosaic.ipynb"
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- file: "sources/01 True-color pipeline.ipynb"
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- file: "sources/01e True-color pipeline.ipynb"
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- part: "Spectral Indices"
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chapters:
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- file: "sources/02a NDVI.ipynb"
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- file: "sources/02b Timeseries.ipynb"
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sections:
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- file: "sources/02c Corrupted Zip File.ipynb"
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- file: "sources/02c Multithreading.ipynb"
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- file: "sources/02d Cloud Masks.ipynb"
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- file: "sources/02 Spectral Indices.ipynb"
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- file: "sources/02e Spectral Indices.ipynb"
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- part: "Burn Ratio Calculations"
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chapters:
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- file: "sources/03a Forest Fire Product Downloads.ipynb"
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@ -404,6 +404,13 @@
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"products[products['title'].str.startswith('S2A')]['orbitnumber'].unique()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Compared to the area numbers of the S2B satellite:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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@ -434,11 +441,11 @@
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"What is the minimum time span in those four weeks to ensure a coverage of all of Brandenburg?\n",
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"To find out we iterate through the returned products, for each iteration $i$ unifying the associated product's geometry $P_i$ with all products we already iterated through:\n",
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"\n",
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"\\begin{align}\n",
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"P &= \\{P_1, P_2, \\cdots, P_n\\} \\\\\n",
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"U_1 &= \\{\\} \\\\\n",
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"U_{n+1} &= U_{n} \\cup P_n\n",
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"\\end{align}\n",
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"$$\n",
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"P = \\{P_1, P_2, \\cdots, P_n\\} \\\\\n",
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"U_1 = \\{\\} \\\\\n",
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"U_{n+1} = U_{n} \\cup P_n\n",
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"$$\n",
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"\n",
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"Given the shape of Brandenburg $B$ the condition for termination is when $B$ is contained entirely in the unified shape $U_n$:\n",
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"\n",
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"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "67ed5567a9f34f4a8b6099c7b965147c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(HTML(value='MD5 checksumming'), FloatProgress(value=0.0, max=1103411496.0), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "8488cd11a218407d8be7bdcd5c0039a7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=804654613.0), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Corrupted Zipfiles\n",
|
||||
"# Corrupted Zipfiles\n",
|
||||
"\n",
|
||||
"Out of the 40 files we were trying to download, some needed to be fetched from the [Long-Term Archive](https://scihub.copernicus.eu/userguide/#LTA_Long_Term_Archive_Access).\n",
|
||||
"After retrying the download several times, all files could be retrieved.\n",
|
||||
|
|
@ -173,7 +173,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Do the downloads fail repeatedly?\n",
|
||||
"## Do the downloads fail repeatedly?\n",
|
||||
"\n",
|
||||
"All files have been downloaded again to another folder, `input/tempelhofer_feld_test`."
|
||||
]
|
||||
|
|
|
|||
|
|
@ -247,7 +247,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Result\n",
|
||||
"## Result\n",
|
||||
"\n",
|
||||
"As we can see when processing a single element, multiprocessing comes with an overhead.\n",
|
||||
"When the list to be processed is sufficiently large, we get a reduction in processing time of roughly 30%-50%, depending on list size.\n",
|
||||
|
|
|
|||
|
|
@ -1,5 +1,12 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Spectral Index Pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
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Loading…
Add table
Add a link
Reference in a new issue