Clean-up blog posts (#296)

* Update docs tags * Add Youtube Channel link in the button of the Doc Page * rm fab * Add link of youtube to every associated blog post * cleanups * comment out links
fusedio · Dec 23, 2024 · 0d19962 · 0d19962
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diff --git a/blog/2024-08-27-pachama/index.mdx b/blog/2024-08-27-pachama/index.mdx
@@ -9,6 +9,13 @@ hide_table_of_contents: false
 keywords: [pachama, sustainability]
 ---
 
+
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=wG4wWHL5rN0).
+
+:::
+
 import ReactPlayer from 'react-player'
 
 <ReactPlayer playsinline={true} className="video__player" playing={true} muted={true} controls height="100%" url="https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/pachama_land_suitability_app.mp4" width="100%" />

diff --git a/blog/2024-08-29-kevin/index.mdx b/blog/2024-08-29-kevin/index.mdx
@@ -8,6 +8,12 @@ hide_table_of_contents: false
 keywords: [computer vision, vegetation analysis, segmentation]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=2j8fYWYfO6U).
+
+:::
+
 
 import ReactPlayer from 'react-player'
 
@@ -115,4 +121,4 @@ import CODE from "@site/src/app-iframe/python/vegetation_segmentation.py"
   height="800px"
   useResizer={false}
 />
-</div>
+</div>
diff --git a/blog/2024-09-05-dl4eo/index.mdx b/blog/2024-09-05-dl4eo/index.mdx
@@ -9,6 +9,12 @@ hide_table_of_contents: false
 keywords: [object detection, deep learning, airplane, ai, computer vision]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=jAoBr_qD63I).
+
+:::
+
 In this article I show how to create an object detection layer on 50cm imagery in realtime. It explains how to create a Fused User Defined Function ([UDF](/core-concepts/write/)) to load satellite image tiles to call an inference model, then publish it as an interactive map app.
 
 import ReactPlayer from 'react-player'
@@ -123,4 +129,4 @@ import DL4EO_CODE from "@site/src/app-iframe/python/dl4eo.py";
   height = {700}
   useResizer={false}
 />
-</div>
+</div>
diff --git a/blog/2024-09-12-danieljahn/index.mdx b/blog/2024-09-12-danieljahn/index.mdx
@@ -9,6 +9,12 @@ hide_table_of_contents: false
 keywords: [blog]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=U7_4rmst3jc).
+
+:::
+
 Fused is a powerful and versatile tool that can do nearly anything with just Python. Its versatility is its strength, but it is also an obstacle. It's easy to walk about wondering: what, concretely, can Fused do for me?
 
 Here are six concrete ways you can use Fused today.

diff --git a/blog/2024-09-17-milindsoni/index.mdx b/blog/2024-09-17-milindsoni/index.mdx
@@ -9,6 +9,12 @@ hide_table_of_contents: false
 keywords: [Similarity]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=lmIFEgyUV8o).
+
+:::
+
 This article explores a User Defined Function ([UDF](/core-concepts/write/)) that utilizes global precipitation data to compare rainfall patterns between different locations worldwide and then creates an interactive app!
 
 
@@ -172,4 +178,4 @@ import RAIN_SIM from "@site/src/app-iframe/python/rainfall_similarity.py";
   height={700}
   useResizer={false}
 />
-</div>
+</div>
diff --git a/blog/2024-09-19-overture/index.mdx b/blog/2024-09-19-overture/index.mdx
@@ -9,6 +9,12 @@ hide_table_of_contents: false
 keywords: [overture]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=7x480zFrRr8).
+
+:::
+
 Overture is an open data project that publishes interoperable map datasets. It aims to foster an ecosystem of developers creating downstream map services around its data products. Fused emerged as a solution to enrich Overture datasets on the fly and serve them with [XYZ Tile endpoints](/core-concepts/filetile/#tile-endpoint).
 
 
@@ -112,4 +118,4 @@ import CODE from "@site/src/app-iframe/python/overture_nsi.py";
     "pydeck",
   ]}
 />
-</div>
+</div>
diff --git a/blog/2024-09-23-kristin/index.mdx b/blog/2024-09-23-kristin/index.mdx
@@ -8,6 +8,12 @@ hide_table_of_contents: false
 keywords: [crop,yield,agriculture]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=Qhh8eBOxDLw).
+
+:::
+
 Ever tried to make sense of the myriad file types in spatial data science and felt like you've wandered into a linguistic labyrinth? Trust me, you're not alone. As a data scientist who's spent more time wrangling datasets than I care to admit, I thought I'd take a casual stroll down memory lane with an old high school friend: regression models. Just a simple plot of actual vs. predicted, right? But when spatial data's involved, you can't just sit back and relax—you've got to keep one eye on the geometries.
 
 import ReactPlayer from 'react-player'
@@ -90,4 +96,4 @@ import BUFFER_CODE from "@site/src/app-iframe/python/kristin.py";
   ]}
   height={500}
 />
-</div>
+</div>
diff --git a/blog/2024-09-24-marie/index.mdx b/blog/2024-09-24-marie/index.mdx
@@ -8,6 +8,12 @@ hide_table_of_contents: false
 keywords: [marie,sentinel,landsat,hls]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=pP8q4uOgNAM).
+
+:::
+
 High-quality satellite imagery is essential to assess the carbon impact of nature-based forest conservation and restoration projects [1]. However, getting that high quality imagery is uniquely difficult in areas that need carbon financing the most: tropical forests. Tropical forests present a unique challenge for satellite imagery analysis due to persistent cloud cover, which often renders optical imagery unusable and creates data gaps.
 
 

diff --git a/blog/2024-09-25-pacific/index.mdx b/blog/2024-09-25-pacific/index.mdx
@@ -8,6 +8,12 @@ hide_table_of_contents: false
 keywords: [pacific spatial]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=4nquh30gODY).
+
+:::
+
 Over the last few decades, it has become increasingly evident that passenger vehicles are by far the most dangerous way to travel. As a result, it has become more and more important to find an efficient and effective method to predict traffic risk. However, predicting traffic accidents and where they are likely to occur is a very complex problem, with large amounts of data being needed for most meaningful predictions.
 
 import ReactPlayer from 'react-player'

diff --git a/blog/2024-10-14-fab/fab1.png b/blog/2024-10-14-fab/fab1.png
diff --git a/blog/2024-10-14-fab/fab2.png b/blog/2024-10-14-fab/fab2.png
diff --git a/blog/2024-10-14-fab/fab3.png b/blog/2024-10-14-fab/fab3.png
diff --git a/blog/2024-10-14-fab/index.mdx b/blog/2024-10-14-fab/index.mdx
diff --git a/blog/2024-10-22-kristin/index.mdx b/blog/2024-10-22-kristin/index.mdx
@@ -8,6 +8,14 @@ hide_table_of_contents: false
 keywords: [crop,yield,agriculture]
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=Cj4LaIQ_dxg).
+
+:::
+
+
+
 A raster, a vector, and an array walk into a bar…
 
 Ok I will spare you the corny jokes.

diff --git a/blog/2024-10-24-stefano/index.mdx b/blog/2024-10-24-stefano/index.mdx
@@ -5,6 +5,13 @@ authors: [stefano]
 image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/social_stefano.png
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=4JZZUTn9ru4).
+
+:::
+
+
 
 [GLS](https://gls-group.com/GROUP/landing_page) (General Logistics Systems) is an international parcel delivery service provider, primarily operating in Europe and North America. To stay ahead in the fast-paced logistics industry, GLS launched GLS Studio—an innovation lab aimed at optimising and modernising its depots and processes through cutting-edge technology.
 

diff --git a/blog/2024-10-29-durkin/index.mdx b/blog/2024-10-29-durkin/index.mdx
@@ -6,6 +6,14 @@ image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/gabe_social.pn
 ---
 
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=cyX75eS6s1M).
+
+:::
+
+
+
 The rate of prototyping the [Fused App Builder](/workbench/app-builder/) unlocks is unrivaled. Recently I used it to create a sophisticated prototype app to accelerate my ML workflow. You can follow along by using the ["Cube Factory" app](https://www.fused.io/workbench/apps#app/s/i/fa_5xqtbvqlJGXsmRLwL7zre2) I built.
 
 import ReactPlayer from 'react-player'

diff --git a/blog/2024-10-30-elizabeth/index.mdx b/blog/2024-10-30-elizabeth/index.mdx
@@ -5,6 +5,12 @@ authors: [elizabeth]
 image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/elizabeth_social.png
 ---
 
+:::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=56SCt9jsqTc).
+
+:::
+
 
 Neighborhoods within a city have consistent characteristics but often have ill-defined boundaries. Some neighborhoods are more similar than others even though they’re not nearby. Understanding these local boundaries and the demographics, dynamics and behaviors of different areas affects a wide range of business applications, including advertising, site selection, business analytics, and many more.
 
@@ -87,4 +93,4 @@ import PRECISELY_CODE from "@site/src/app-iframe/python/precisely.py";
   ]}
   height={500}
 />
-</div>
+</div>
diff --git a/blog/2024-11-04-kent/index.mdx b/blog/2024-11-04-kent/index.mdx
@@ -5,6 +5,12 @@ authors: [kent]
 image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/kent_social2.png
 ---
 
+{/* :::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=5V9W7jnkps4).
+
+::: */}
+
 
 I am a self taught developer and data enthusiast. I first came across the spatial data community when I saw a [Matt Forrest](https://www.linkedin.com/in/mbforr/) video on LinkedIn where he demonstrated how to visualize buildings from the [Vida Combined Building Footprints](https://beta.source.coop/repositories/vida/google-microsoft-open-buildings/description/) dataset with DuckDB. Immediately I thought, what if you could see all the buildings in a country, say, Egypt? I set out to do just that and made this map with DuckDB and Datashader.
 
@@ -17,6 +23,15 @@ _Buildings in Egypt._
 
 {/* truncate */}
 
+:::info
+
+Find Stephen's UDF code here:
+- [Five Minutes Away in Bushwick Brooklyn UDF](https://www.fused.io/workbench/catalog/Five_Minutes_Away_in_Bushwick_Brooklyn-95da9a11-176f-49dd-af80-63c7bd4065cf)
+
+:::
+
+
+
 
 
 ## Starting with Fused
@@ -97,7 +112,7 @@ import ImageKent4 from '/blog/2024-11-04-kent/kent4.png';
 
 _Overture Buildings joined with H3 Yellow Cab pickups in New York City._
 
-I made this particular map with Kepler.gl, with two clicks from the Workbench. I could have also exported the data to tools like [Felt](https://felt.com/) and [Mapbox](https://www.mapbox.com/). You can find the code I used to recreate this map here: https://github.com/kentstephen/duckdb_h3.
+I made this particular map with Kepler.gl, with two clicks from the Workbench. I could have also exported the data to tools like [Felt](https://felt.com/) and [Mapbox](https://www.mapbox.com/). You can find the code I used to recreate this map [here](https://github.com/kentstephen/duckdb_h3).
 
 ## App Builder
 

diff --git a/blog/2024-11-26-sameer/index.mdx b/blog/2024-11-26-sameer/index.mdx
@@ -17,7 +17,14 @@ import ReactPlayer from 'react-player'
 
 <ReactPlayer playsinline={true} className="video__player" playing={true} muted={true} controls height="100%" url="https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/sameer1.mp4" width="100%" />
 
-Check out Sameer's UDF on Workbench [here](https://www.fused.io/workbench/catalog/Coverage_model_ibis-a5d96749-bda3-41b6-a549-95ea4fada856).
+:::info
+
+Check out Sameer's UDF on Workbench here::
+- [Coverage Model IBIS UDF](https://www.fused.io/workbench/catalog/Coverage_model_ibis-a5d96749-bda3-41b6-a549-95ea4fada856).
+
+:::
+
+
 
 
 {/* truncate */}

diff --git a/blog/2024-12-03-guillermo/index.mdx b/blog/2024-12-03-guillermo/index.mdx
@@ -2,12 +2,19 @@
 slug: beyond-rgb-interactive-exploration-of-neons-hyperspectral-data
 title: "Beyond RGB: Interactive Exploration of NEON's Hyperspectral Data"
 authors: [guillermo]
-tags: [introduction]
+tags: [neon, hyperspectral, gee]
 image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/guillermo_social.png
 hide_table_of_contents: false
-keywords: [introduction]
+keywords: [neon, hyperspectral, gee]
 ---
 
+{/* :::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=8Dz3vmNbklY).
+
+::: */}
+
+
 
 As a research specialist focused on remote sensing applications in semi-arid rangelands, I'm constantly seeking tools that can enhance our ability to process and analyze large-scale geospatial data. The excitement of discovering new platforms that streamline complex workflows never gets old, especially when dealing with the massive datasets typical in remote sensing research.
 

diff --git a/blog/2024-12-05-claudio/index.mdx b/blog/2024-12-05-claudio/index.mdx
@@ -9,6 +9,14 @@ keywords: [osmnx, urban, mobility, network]
 ---
 
 
+{/* :::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=XokpNGYZPiI).
+
+::: */}
+
+
+
 On December 2023, I visited the [Institute for Metropolitan Planning](https://portal.imp.gob.pe/) (IMP) in Lima. The director had invited me to share some of my geospatial analysis projects from my master's studies and explore potential collaborations. Around that time, Lima's mayor had announced a bold infrastructure initiative: building 60 flyover bridges to ease traffic congestion in one of the most gridlocked cities in Latin America.
 
 When I asked how the city was simulating the impact of new network designs on urban mobility, the answer was: "We are not simulating anything, our budget is constrained, and there is no political will to solve this problem." I couldn't think of anything else after this meeting. I started thinking about how I could create an easy-to-use tool to simulate urban mobility using open-source data, tools with no subscriptions or licenses, and without data privacy concerns.

diff --git a/blog/2024-12-06-naty/index.mdx b/blog/2024-12-06-naty/index.mdx
@@ -29,7 +29,8 @@ import ReactPlayer from 'react-player'
 
 :::info
 
-Find Naty's UDF code [here](https://www.fused.io/workbench/catalog/Ibis_DuckDB_Overturemaps-ca8e306b-a05a-4d48-ab40-537edd2586dc).
+Find Naty's UDF code here:
+- [IBIS DuckDB Overture UDF](https://www.fused.io/workbench/catalog/Ibis_DuckDB_Overturemaps-ca8e306b-a05a-4d48-ab40-537edd2586dc)
 
 :::
 

diff --git a/blog/2024-12-10-qiusheng/index.mdx b/blog/2024-12-10-qiusheng/index.mdx
@@ -8,6 +8,12 @@ hide_table_of_contents: false
 keywords: [qiusheng, leafmap, foursquare, overture]
 ---
 
+{/* :::info
+
+🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=Xe2EjfrXd_k).
+
+::: */}
+
 Dr. Qiusheng Wu is an Associate Professor of Geography and Sustainability at the [University of Tennessee](https://faculty.utk.edu/Qiusheng.Wu) and a Founding Editorial Board Member at the [Cloud-Native Geospatial Forum (CNG)](https://cloudnativegeo.org/). As part of his commitment to making open-source geospatial analysis and visualization more accessible, he has developed several widely used open-source packages, including [geemap](https://geemap.org/), [leafmap](https://leafmap.org/), and [segment-geospatial](https://samgeo.gishub.org/).
 
 In [this Notebook](https://colab.research.google.com/drive/1JUupA0f5xJ_2QMqI6xrsEnLqIGXhB6le?usp=sharing) Qiusheng shows a few examples of how Cloud Native Geospatial datasets help you easily load data into a Jupyter Notebook environment using leafmap. His practical examples showcase how you can call the [Overture Maps UDF](https://www.fused.io/workbench/catalog/Overture_Maps_Example-64071fb8-2c96-4015-adb9-596c3bac6787) and [Foursquare Places UDF](https://www.fused.io/workbench/catalog/Foursquare_Open_Source_Places-5cd75ead-e319-4279-8efc-04276de145bc) to load data into a custom area of interest and render it in a leaflet map.
-Original file line number
+Diff line change
@@ Expand Up / @@ -8,6 +8,12 @@ hide_table_of_contents: false @@
     keywords: [marie,sentinel,landsat,hls]
     ---
+    :::info
+    🎥 Watch the Webinar recording associated with this blog post [here](https://www.youtube.com/watch?v=pP8q4uOgNAM).
+    :::
     High-quality satellite imagery is essential to assess the carbon impact of nature-based forest conservation and restoration projects [1]. However, getting that high quality imagery is uniquely difficult in areas that need carbon financing the most: tropical forests. Tropical forests present a unique challenge for satellite imagery analysis due to persistent cloud cover, which often renders optical imagery unusable and creates data gaps.
@@ Expand Down @@