tune up slides

index.qmd

@@ -94,7 +94,7 @@ title-slide-attributes:
 
 # STAC-powered applications
 
-## STAC Browser
+## STAC Browser {.hide-title}
 
 ::: {.r-stretch}
 <iframe 
@@ -133,13 +133,7 @@ title-slide-attributes:
 
 # STAC at scale: a journey
 
-## Challenges
-
-- data volume!
-- search speed for applications that consume data
-- ingestion pipelines
-
-##
+## Outline {.hide-title}
 
 ### LandsatLook
 - user-facing USGS application for visualizaing mosaics of Landsat scenes
@@ -152,6 +146,12 @@ title-slide-attributes:
 - massive catalog of public data
 - event-driven STAC metadata generation pipelines
 
+::: {.notes}
+- data volume!
+- search speed for applications that consume data
+- ingestion pipelines
+:::
+
 # LandsatLook
 
 - an early STAC-powered application
@@ -165,7 +165,7 @@ title-slide-attributes:
 - Spurred the development of MosaicJSON
 :::
 
-## {background-interactive=true background-iframe="https://landsatlook.usgs.gov/explore?date=2024-10-17%7C2024-11-15&sat=LANDSAT_9%7CLANDSAT_8"}
+## Explore LandsatLook {.hide-title background-interactive=true background-iframe="https://landsatlook.usgs.gov/explore?date=2024-10-17%7C2024-11-15&sat=LANDSAT_9%7CLANDSAT_8"}
 
 ::: footer
 <https://landsatlook.usgs.gov/explore>
@@ -191,12 +191,13 @@ title-slide-attributes:
 
 # Microsoft Planetary Computer
 
-##
+:::{.fragment}
 - STAC API with many public datasets
 - Continuous flow of new data
 - Interactive visualization platform powered by STAC metadata
+:::
 
-## {background-interactive=true background-iframe="https://planetarycomputer.microsoft.com/catalog"}
+## Explore Planetary Computer {.hide-title background-interactive=true background-iframe="https://planetarycomputer.microsoft.com/catalog"}
 
 ::: footer
 <https://planetarycomputer.microsoft.com/catalog>
@@ -206,7 +207,7 @@ title-slide-attributes:
 - Show how you can filter by date range, show different visualizations, etc.
 :::
 
-## Microsoft Planetary Computer
+## `pgstac` origin story {.hide-title}
 
 ::::{.columns}
 :::{.column width="50%"}
@@ -251,12 +252,13 @@ title-slide-attributes:
 
 # Amazon Sustainability Data Initiative (ASDI)
 
-## ASDI
+:::{.fragment}
 - sustainability data situated next to a compute platform
 - continuously updated
 - NODD: partnership with NOAA
+:::
 
-## {background-interactive=true background-iframe="https://radiantearth.github.io/stac-browser/#/external/dev.asdi-catalog.org/?.language=en"}
+## ASDI Catalog {.hide-title background-interactive=true background-iframe="https://radiantearth.github.io/stac-browser/#/external/dev.asdi-catalog.org/?.language=en"}
 
 ::: footer
 :::
@@ -265,7 +267,7 @@ title-slide-attributes:
 
 > The stactools-packages github organization is a home for datasets and tool packages using the SpatioTemporal Asset Catalog (STAC) specification.
 
-## {background-interactive=true background-iframe="https://stactools-packages.github.io/"}
+## Browse stacktools-packages {.hide-title background-interactive=true background-iframe="https://stactools-packages.github.io/"}
 
 ::: footer
 :::
@@ -275,7 +277,9 @@ title-slide-attributes:
 
 ![](https://github.com/developmentseed/stactools-pipelines/blob/main/docs/aws_asdi_cog.png?raw=true){.r-stretch}
 
-TODO: link to blog post
+::: {.aside}
+<https://developmentseed.org/blog/2023-10-20-asdi/>
+:::
 
 ::: footer
 [developmentseed/stactools-pipelines](https://github.com/developmentseed/stactools-pipelines)
@@ -289,17 +293,6 @@ TODO: link to blog post
 
 # Data conusumer experience
 
-## Cloud-native geospatial
-- work entirely on the cloud (jupyter hub)
-- work locally on files stored in the cloud
-
-::: {.notes}
-- several flavors of cloud-native geospatial, each with specific advantages
-- working locally is so convenient!
-- working on compute in the cloud is faster once you have an environment set up
-- 
-:::
-
 ## the STAC API advantage
 - don't need to learn file path schema for a new dataset
 - can rely on tools like [`pystac_client`](https://pystac-client.readthedocs.io/en/latest/) for API operations and [`odc_stac`](https://odc-stac.readthedocs.io/en/latest/) for file i/o
@@ -311,7 +304,7 @@ TODO: link to blog post
 - thanks to the STAC spec we only need a few libraries to interact with data from any collection!
 
 ```{python}
-#| code-line-numbers: "|1|3|4|"
+#| code-line-numbers: "|1|3|5|"
 import odc.stac
 import pyproj
 import pystac_client
@@ -321,7 +314,7 @@ from shapely.geometry import box
 from shapely.ops import transform
 ```
 
-##
+## Define AOI {.hide-title}
 
 - set up an AOI
 
@@ -344,7 +337,7 @@ bbox_4326 = transform(transformer_4326.transform, aoi).bounds
 print(bbox_4326)
 ```
 
-##
+## Collection search {.hide-title}
 - find DEM collections in a STAC API
 
 ```{python}
@@ -363,14 +356,12 @@ for collection_id, collection in search_results.items():
     print(f"{collection_id}: {collection.description}\n")
 ```
 
-## {.smaller}
+## Item search {.smaller}
 
 - query STAC items
 
 ```{python}
 #| output-location: column
-#| class-output: scrollable-output
-#| scrollable: true
 stac_items = client.search(
     collections="cop-dem-glo-30",
     bbox=bbox_4326,
@@ -379,7 +370,7 @@ stac_items = client.search(
 stac_items
 ```
 
-##
+## `odc.stac.load` {.hide-title}
 - load into an `xarray.Dataset` with `odc.stac.load`
 - don't read any data yet, though (just metadata!)
 
@@ -397,15 +388,16 @@ dem_ds = odc.stac.load(
 print(dem_ds)
 ```
 
-##
+## `xarray` API {.hide-title}
 
 - access to the `xarray` API for visualization and analysis
+- read data from cloud storage "just-in-time"
 ```{python}
 with rasterio.Env(aws_no_sign_request=True):
     dem_ds["data"].squeeze().plot.imshow()
 ```
 
-## 
+## New source, same tools 
 - read another dataset from a different source
 ```{python}
 client = pystac_client.Client.open(
@@ -424,15 +416,7 @@ stac_items = client.search(
 ).item_collection()
 ```
 
-## {.smaller}
-```{python}
-#| class-output: scrollable-output
-#| scrollable: true
-#| echo: false
-display(stac_items)
-```
-
-##
+## Load land cover {.hide-title}
 
 ```{python}
 #| output-location: fragment
@@ -449,7 +433,7 @@ landcover_ds = odc.stac.load(
 print(landcover_ds)
 ```
 
-##
+## Merge datasets {.hide-title}
 
 - combine the two Datasets into a single `xarray.Dataset`
 ```{python}
@@ -465,7 +449,7 @@ merged = xr.merge(
 print(merged)
 ```
 
-## {.smaller}
+## Analyze disparate datasets {.hide-title .smaller}
 
 - calculate mean elevation by land cover class
 
@@ -489,3 +473,15 @@ display(stats.drop_vars(["spatial_ref", "time"]).to_dataframe().reset_index().as
 ```
 :::
 ::::
+
+# Conclusion
+:::: columns
+::: column
+- STAC is working on a very large scale already
+- evolving fast (but not too fast)
+:::
+::::
+
+## Thank you!
+
+<[email protected]>

styles.css

@@ -129,3 +129,8 @@
   overflow-y: auto;
   overflow-x: auto;
 }
+
+/* make it possible to hide a slide title */
+.hide-title h2 {
+  display: none;
+}