marklit

Satellogic's Open Satellite Feed

https://registry.opendata.aws/satellogic-earthview/

Footprints Heatmap

$ aws --no-sign-request \
      --output json \
      s3api \

marklit

"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039074787Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039088932Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039201597Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039215742Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039328461Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-newsat43-20240601T064039342606Z-micro-L0-v0.1.8_analytic.tiff"
"Baotou/0392e349-af81-4b9f-90ca-283070dc4dbb/SL-newsat43-20240601T064039074787Z-micro-L0/SL-ne

marklit

$ curl -A'curl' -L ipinfo.io

{
  "ip": "40.177.26.232",
  "hostname": "ec2-40-177-26-232.ca-west-1.compute.amazonaws.com",
  "city": "Calgary",
  "region": "Alberta",

marklit

Download

$ wget -O seerai-hifld-archive.xml \
    https://storage.googleapis.com/seerai-hifld-archive/

$ python3 -m venv ~/.xml
$ source ~/.xml/bin/activate
$ python3 -m pip install python-xml2dict
$ python3

marklit

https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n10_e005_n05.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n15_e005_n10.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n20_e005_n15.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n25_e005_n20.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n30_e005_n25.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n35_e005_n30.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e000_n40_e005_n35.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e005_n00_e010_s05.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e005_n05_e010_n00.geojson
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FLoD1%2Fafrica&files=e005_n10_e010_n05.geojson

marklit

https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n10_e005_n05.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n15_e005_n10.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n20_e005_n15.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n25_e005_n20.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n30_e005_n25.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n35_e005_n30.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e000_n40_e005_n35.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e005_n00_e010_s05.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e005_n05_e010_n00.zip
https://dataserv.ub.tum.de/s/m1782307/download?path=%2FHeight%2Fafrica&files=e005_n10_e010_n05.zip

marklit

Download https://gist.github.com/marklit/237bec7b8a82e1290e2f481c8adda611 to manifest.lod.txt.

for LINE in `cat manifest.lod.txt`; do
    GEOJSON=`echo "$LINE" | grep -o 'files=.*' | sed 's/files=//g'`
    ZIPFILE="$GEOJSON.zip"
    if [ ! -f $ZIPFILE ]; then
        echo $LINE
        wget -c -O $GEOJSON "$LINE"
        zip -9 "$ZIPFILE" "$GEOJSON"

marklit

$ python3 -m venv ~/.embeddings
$ source ~/.embeddings/bin/activate
$ pip install -U \
    sentence-transformers \
    pandas \
    umap-learn

marklit

//  spacex.capsules.parquet, 25
[
  {
    "__v": 0,
    "_id": "5e9e2c5bf35918ed873b2664",
    "land_landings": 0,
    "last_update": "Hanging in atrium at SpaceX HQ in Hawthorne ",
    "launches": "[NULL]",
    "reuse_count": 0,
    "serial": "C101",