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👽 Handle ms-buildings 20230425 update in Object Detection tutorial #106

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merged 2 commits into from
May 31, 2023
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👽 Handle ms-buildings 20230425 update in Object Detection tutorial #106

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c62753f
:passport_control: Use sign_inplace modifier in pystac_client API call
weiji14 May 31, 2023
ea881c0
:alien: Filter ms-buildings to those intersecting bbox area of interest
weiji14 May 31, 2023
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32 changes: 22 additions & 10 deletions docs/object-detection-boxes.md
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Expand Up @@ -129,24 +129,35 @@ dataarray

Now to pull in some building footprints 🛖. Let's make a STAC API query to get
a [GeoParquet](https://github.com/opengeospatial/geoparquet) file (a
cloud-native columnar 🀤 geospatial vector file format) over our study area.
cloud-native columnar 🀤 geospatial vector file format) that intersects our
study area.

```{code-cell}
catalog = pystac_client.Client.open(
url="https://planetarycomputer.microsoft.com/api/stac/v1"
url="https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
items = catalog.search(
collections=["ms-buildings"], query={"msbuildings:region": {"eq": "Brunei"}}
collections=["ms-buildings"],
query={"msbuildings:region": {"eq": "Brunei"}},
intersects=shapely.geometry.box(minx=114.94, miny=4.88, maxx=114.95, maxy=4.89),
)
item = next(items.get_items())
item
```

Next, we'll sign 🔏 the URL to the STAC Item Asset, and load ⤵️ the GeoParquet
file using {py:func}`geopandas.read_parquet`.
```{note}
Accessing the building footprint STAC Assets from Planetary Computer will
require signing 🔏 the URL. This can be done with a `modifier` function in the
{py:meth}`pystac_client.Client.open` call. See also 'Automatically modifying
results' under {doc}`PySTAC-Client Usage <pystac_client:usage>`).
```

Next, we'll load ⤵️ the GeoParquet file using
{py:func}`geopandas.read_parquet`.

```{code-cell}
asset = planetary_computer.sign(item.assets["data"])
asset = item.assets["data"]

geodataframe = gpd.read_parquet(
path=asset.href, storage_options=asset.extra_fields["table:storage_options"]
Expand All @@ -155,10 +166,11 @@ geodataframe
```

This {py:class}`geopandas.GeoDataFrame` contains building outlines across
Brunei 🇧🇳. Let's do a spatial subset ✂️ to just the Kampong Ayer study area
using {py:attr}`geopandas.GeoDataFrame.cx`, and reproject it using
{py:meth}`geopandas.GeoDataFrame.to_crs` to match the coordinate reference
system of the optical image.
Brunei 🇧🇳 that intersects and extends beyond our study area. Let's do a spatial
subset ✂️ to just the Kampong Ayer study area using
{py:attr}`geopandas.GeoDataFrame.cx`, and reproject the polygon coordinates
using {py:meth}`geopandas.GeoDataFrame.to_crs` to match the coordinate
reference system of the optical image.

```{code-cell}
_gdf_kpgayer = geodataframe.cx[114.94:114.95, 4.88:4.89]
Expand Down