The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography

The promise of digital mapping is to provide a shared and real-time view of the state of the underlying system. pg_eventserv is a free and open-source component that helps fulfill the promise of real-time event modeling and shared views in PostgreSQL. By connecting to PostgreSQL and listening on specified channels, pg_eventserv captures database notifications and forwards them to web clients, enabling real-time updates and synchronization of data displayed on maps or other web interfaces. pg_eventserv does one thing and one thing only: take events generated by the PostgreSQL NOTIFY command and passes the payload along to waiting WebSockets clients. pg_eventserv is free and easy to install and you can find it here: https://github.com/CrunchyData/pg_eventserv What this means is that any client can watch for notifications and update as changes in the database happen. Real-time data! Here is a link to a Youtube video demonstration of pg_eventserv in action! https://youtu.be/UakRtYmoWow  I will let Paul Ramsey the creator of pg_eventserv explain all this in more detail in this episode.  If you want to reach out to Paul the best place to do that is http://blog.cleverelephant.ca/  Or if you want to listen to previous episodes with Paul you might find these interesting Raster in the database? https://mapscaping.com/podcast/rasters-in-a-database/ Dynamic Vector Tiles Straight From The Database https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/ Spatial SQL- GIS Without The GIS https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   also ...  If you are interested in spatial databases at scale ... you might find this episode interesting https://mapscaping.com/podcast/distributing-geospatial-data/     Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping    

Jett Metcalf, a geospatial recruitment specialist, discusses job hunting, standing out from the crowd, and negotiating contracts. They compare working at Google and Descartes Labs, highlight the challenges and advantages of hiring for geospatial roles, emphasize the importance of soft skills in technical positions, and provide insights into compensation negotiation and freelancing opportunities in the industry.

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 Google Maps, Geomatics, Cartography Location Intelligence, Mapping

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/ https://foursquare.com/  

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.    https://pygeoapi.io   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      

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  

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! https://mapscaping.com/jobs/      

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 Google Maps, Geomatics, Cartography Location Intelligence, Mapping

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 https://datadan.io/   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!        

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 Jobs - Mapscaping.com   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping