A podcast for geospatial people. Weekly episodes that focus on the tech, trends, tools, and stories from the geospatial world. Interviews with the people that are shaping the future of GIS, geospatial as well as practitioners working in the geo industry. This is a podcast for the GIS and geospatial community subscribe or visit https://mapscaping.com to learn more
Episodes
Thursday Jun 22, 2023
Thursday Jun 22, 2023
You guessed it this episode is all about recruitment!
Who is this episode for? Well if you have a career in geospatial … it's for you!
Getting the job you want, changing your career path, or deciding whether to work at a start-up or a more established company. Once you have made these decisions, how do you stand out from the crowd? What is it the recruiters are looking for? And if you do get offered a contract should you negotiate and what is negotiable?
Jett Metcalf has worked as a geospatial recruitment specialist for a few well-known companies like Google, Descartes Labs and is currently the Head of Talent at Regrow Ag. In this episode, he is going to help us understand some of the answers to these questions.
Reach out to Jett on LinkedIn or Twitter
If you enjoy this episode and are interested in more career-focused episodes go to https://mapscaping.com/podcasts/ and filter by “Geospatial Career"
See you next week!
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Google Maps, Geomatics, Cartography
Location Intelligence, Mapping
Wednesday Jun 14, 2023
Wednesday Jun 14, 2023
How do we get data from a satellite down to Earth? How do we task a satellite?
Today the answer is likely to be via radios and a system of downlink sites or ground stations. As the satellites pass overhead or within “line of sight” data can be sent via radio from the satellite to the receiver on the ground.
If you don’t want to wait until the satellite can see the ground station, you can send your data to a geostationary satellite that can always see a ground station and let it send the data back to Earth.
Radios are tried and tested, they have been used for this purpose since the inception of satellite communication and radio waves can pass through Earth's atmosphere without significant loss!
But … the frequency spectrum for radio waves is strictly regulated, which can limit available channels for communication, and the bandwidth of radio frequencies is limited, which can reduce the volume of data transmission.
What about lasers?
You can send more data faster with a laser, you don’t need to worry about interfering with someone else part of the radio spectrum, and ground stations can be much smaller even human-portable!
But … lasers struggle with clouds and the technology is still relatively new
So what is the best way to communicate with satellites? Radio or Laser? The answer is … it depends ;)
Jordan Wachs, Director of Business Development for SpaceRake.net does a great job adding context to this discussion but perhaps the bigger question here is what will we do when satellites become internet devices, part of the Internet of Things?
What if they were always on always connected in the same way your phone is always on, always connected? What will this enable?
This episode was sponsored by Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing
People who liked this episode also liked …
How to keep your satellite pointing at earth
https://mapscaping.com/podcast/how-to-keep-your-satellite-pointing-at-earth/
Hyperspectral v’s Multispectral
https://mapscaping.com/podcast/hyperspectral-vs-multispectral/
Sentinel Hub
https://mapscaping.com/podcast/sentinel-hub/
Swing by our website sometime https://mapscaping.com/
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Wednesday Jun 07, 2023
Wednesday Jun 07, 2023
There is a general understanding that it is becoming increasingly difficult to extract meaning from all the data we are collecting without using AI.
But what is AI, and how did we end up in a situation where it is identifying wolves from dogs based on the presence of snow in the background of images?
What does this mean for spatial analysis using tabular data?
What is explainability?
This is not a "how-to" do spatial analysis using an AI episode, it is an overview of AI in spatial analysis episode with Vin Sharma, VP of Engineering at FourSquare
https://www.linkedin.com/in/ciphr/
Wednesday May 31, 2023
Wednesday May 31, 2023
PYGEOAPI is a Python server implementation of the OGC API suite of standards ... which might be really useful if you are thinking about upgrading from the first-generation OGC standards to the second-generation OGC standards
... or if need to implement a custom data source or custom functionality to your web services.
If you are using MapServer, Geoserver, Mapproxy, QGIS server, or Deegree you might find this episode interesting!
Relevant previous episodes
Cloud-native Geospatial
https://mapscaping.com/podcast/cloud-native-geospatial/
Geoserver
https://mapscaping.com/podcast/geoserver/
Geonode
https://mapscaping.com/podcast/geonode-open-source-geospatial-content-management-system/
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Google Maps, Geomatics, Cartography
Location Intelligence, Mapping
Wednesday May 24, 2023
Wednesday May 24, 2023
So why would anyone want to put alot of data into a browser? Well, for a lot of the same reasons that edge computing and distributed computing have become so popular.
You get the data a lot closer to the user and you don’t have to pay for the compute ;)
… this sounds great but as I found out during this conversation it's not as easy as it might seem!
There are a lot of trade-offs that need to be evaluated when moving data and analytics to the client.
Nick Rabinowitz Senior Staff Software Engineer at Foursquare has a ton of experience with this so he volunteered his time to help us understand more about it.
https://location.foursquare.com/
https://studio.foursquare.com/home
If you are not familiar with the Arrow data format it might be worth checking out
Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead
Related podcast episodes that you might find interesting include
H3 grid system
https://mapscaping.com/podcast/h3-geospatial-indexing-system/
The H3 geospatial indexing system is a discrete global grid system consisting of a multi-precision hexagonal tiling of the sphere with hierarchical indexes. H3 is a really interesting approach to tiling data that was developed by UBER and has been open-sourced.
Hex Tiles
https://mapscaping.com/podcast/hex-tiles/
If you have not heard of the H3 grid system before listen to that episode first before listening to this one it will add a lot of useful context!
Spatial Knowledge Graphs
https://mapscaping.com/podcast/spatial-knowledge-graphs/
Foursquare is moving away from spatial joins and focusing on building a knowledge graph. If you are not familiar with graphs this might be a good place to start, also its interesting to hear the reasons for the move from spatial joins to another data structure.
Distribution Geospatial Data
https://mapscaping.com/podcast/distributing-geospatial-data/
This is interesting if you want to understand more about distributed databases and some of the strategies for doing this. It sounds complicated but this episode is a really good introduction!
Cloud Native Geospatial
https://mapscaping.com/podcast/cloud-native-geospatial/
This episode give a solid overview of what cloud-native means and some of the current geospatial cloud native formats out there today
I am constantly thinking about how I can make this podcast better for you so if you have any ideas or suggestions please let me know!
Also, I am thinking of recording a behind-the-scenes episode, is that something you might be interested in? if so what questions do you have?
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Google Maps, Geomatics, Cartography
Location Intelligence, Mapping
Wednesday May 17, 2023
Wednesday May 17, 2023
Sounds like a great idea right?
In this episode, Paul Ramsey explains why you shouldn't ... unless you want to ... and how you can ... if you have to.
You can find Paul's blog here: http://blog.cleverelephant.ca/about
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Google Maps, Geomatics, Cartography
Location Intelligence, Mapping
Previous episodes with Paul
Spatial SQL
https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/
GDAL
https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/
Dynamic Vector Tiles
https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/
Blog posts by Paul about Rasters in the Database
https://www.crunchydata.com/blog/postgres-raster-query-basics
https://www.crunchydata.com/blog/waiting-for-postgis-3.2-secure-cloud-raster-access
Check Out Our Geospatial Job Board!
Friday May 12, 2023
Friday May 12, 2023
A knowledge graph is a network of relationships between real work entities and in this episode, you will learn how and why knowledge graphs might be a better choice than spatial joins!
Further listening!
The H3 Indexing System
https://mapscaping.com/podcast/h3-geospatial-indexing-system/
Hex Tiles
https://mapscaping.com/podcast/hex-tiles/
Points of Interest data
https://mapscaping.com/podcast/all-of-the-places-in-the-world/
Dark Data
https://mapscaping.com/podcast/unstructured-data-is-dark-data/
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Wednesday May 10, 2023
Wednesday May 10, 2023
I am sure you have heard of ChatGPT by now so the hope of this episode is to give you some more context about what is it built on and how it works.
To do that I invited Daniel Whitneck back on the podcast
You can connect with Daniel here
and listen to his previous episode here:
https://mapscaping.com/podcast/an-introduction-to-artificial-intelligence/
This is perhaps the quote for the episode that I have spent the most time thinking about
"We always thought AI would be logical and lack creativity - but it is almost the exact opposite"
This reframes the idea of being wrong to being creative which I think you could argue really depends on the context!
If you have not already played around with ChatGPT it's well worth spending the time to experiment with it ... while its still free ;)
https://chat.openai.com/auth/login
Further listening
If you have not already listened to this episode about computer vision and GeoAI you might find it interesting. Listen out for the discussion around plausible / realistic data and real measurements - I think this gives more context to the use cases for generative AI
https://mapscaping.com/podcast/computer-vision-and-geoai/
You might also enjoy this episode about fake satellite imagery
https://mapscaping.com/podcast/fake-satellite-imagery/
BTW I have started a job board for geospatial people
feel free to check it out!
Wednesday Apr 26, 2023
Wednesday Apr 26, 2023
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.
You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.
This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.
Listen out for these key points during the conversation!
- Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)
- In computer vision we can learn from the data, in earth observation we know the physics
- To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don’t need to be an expert in all three but you need to be interested in all three
- Vectors in the machine learning world don’t necessarily have anything to do with points lines and polygons ;)
Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/
Related Podcast Episodes
Super Resolution
https://mapscaping.com/podcast/super-resolution-smarter-upsampling/
Fake Satellite Imagery
https://mapscaping.com/podcast/fake-satellite-imagery/
Sentinal Hub
https://mapscaping.com/podcast/sentinel-hub/
Google Earth Engine
https://mapscaping.com/podcast/introducing-google-earth-engine/
Microsofts Planetary Computer
https://mapscaping.com/podcast/the-planetary-computer/
BTW MapScaping has started a Job Board!
it's in the early stages but it's live
Some more episodes you might enjoy
ESRI, GIS careers, Geospatial Data Science
QGIS, Geospatial Python, ArcGIS Pro
Google Maps, Geomatics, Cartography
Location Intelligence, Mapping
Wednesday Apr 19, 2023
Wednesday Apr 19, 2023
Data is what data does - more about that later on ;)
This episode focuses on designing for privacy, how do we create value from location data without sacrificing personal privacy?
Well, you might start by adhering to the Enhanced Standards For Precise Location Information which means that information about sensitive places like churches, hospitals, military bases, and LGBTQ+ spaces isn't misused or sold. Plus, they protect our exact location from being shared with law enforcement or bounty hunters!
Yes, that's right bounty hunters!
You might also think about adding noise to the data, maybe you want to blur the time stamp and look at everything in aggregate?
It turns out the location data is not just classified as personal data but is actually classified as sensitive personal data in privacy law so if you are going to work with it you really need to understand the risks involved.
One of the really interesting ideas mentioned by Elizabeth Hein VP of Compliance & Data Protection was the idea of regulating Use, Harm, and Risk instead of sensitive data
Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data
If you want to learn more about POI data and why points of interest data are so hard
check out the episode called All of the Places in the World: https://mapscaping.com/podcast/all-of-the-places-in-the-world/
On a side note, I am working on a side project, it's a job board for geospatial people, and you can find it here https://mapscaping.com/jobs/ it's still in the development phase but feel free to check it out!