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

100 billion Points Every Day 100 billion is a very large number, let's say that I gave you a spreadsheet with 100 billion rows in it, each row consisted of five columns Latitude, Longitude, Device ID, A Timestamp, and a column telling the name of the data provider What would you do with that?  How would you clean it? Make sense of it? Extract value from it? What would people use it for? And how would you do this in a way that could be systematized? FourSquare does this every day with the help of something they call a movement engine. To help understand more about how they do this I have invited Gabriel Durkin the director of data science on the podcast. This is the last in a series of episodes I have worked on together with FourSquare and I have to say it's been really enjoyable working with them.  If you are interested in hearing some of the previous episodes just check out the links below!   From Pixels To Patterns AI In Spatial Analysis https://mapscaping.com/podcast/from-pixels-to-patterns-ai-in-spatial-analysis/ Big Data In The Browser https://mapscaping.com/podcast/big-data-in-the-browser/ Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Designing For Location Privacy https://mapscaping.com/podcast/designing-for-location-privacy/ All Of The Places In The World https://mapscaping.com/podcast/all-of-the-places-in-the-world/ Geospatial Jobs   There are a few new jobs on our Job Board! The most interesting one is the role of Social Media Manager at Felt - United States (Remote)  ( If you want to apply for this one, it might be a good idea to listen to this episode first ;)  https://mapscaping.com/podcast/felt-upload-anything/ ) See more at https://mapscaping.com/jobs/   As a bonus for reading all the way to the end :)    If you are looking for free terrain data for anywhere in the world you might find this useful  https://github.com/openterrain/openterrain/wiki/Terrain-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

Learn about the importance of labeled datasets in computer vision and the concept of synthetic data generation. Explore the career journey of Chris Andrews from Rendered AI, specializing in synthetic computer vision data. Discover the use of physics-based synthetic data generation and the trend of reality capture and digital twins. Dive into the challenges of training AI systems and the future landscape of synthetic data in computer vision.

An interview with a senior data scientist from Hub Ocean, a platform for unlocking and uniting ocean data. They discuss the challenges of implementing data hubs and the concept of fair data. The importance of ocean data, app development for data analysis, and the availability and accessibility of ocean data are also explored. The podcast highlights the need for data sharing and collaboration in the ocean data space.

Learn about FELT, a unique mapping tool that allows users to upload any type of data without specifying the format. Explore the features of Felt, such as the 'upload anything' button and real-time spatial collaboration. Discover the world of GPS art and how Felt enables users to create traced routes. Gain insights into the company's hiring and monetization plans, and their vision of encouraging map creation.

Rapid is a free open-source web-based editor for an OpenStreetMap. In the past the focus was on conflating AI-generated datasets with OpenStreetMap data but the future for this editor is conflating authoritative datasets with OpenStreetMap.  Humans are in the loop, people reviewing data authoritative datasets and adding them to OpenStreetMap with a few clicks!  So you might be wondering, what is Authoritative data? And perhaps it doesn’t even matter what authoritative means maybe the most important thing is it correct. If you are interested in OpenStreetMap you might enjoy this episode  https://mapscaping.com/podcast/openstreetmap-is-a-community-of-communities/ which is a great introduction to OpenStreetMap as a project but also explains some of the commercial interest in updating the map which adds a lot of context to Rapid and its development and future.   Humanitarian OpenStreetMap Team If you have not heard of Humanitarian OpenStreetMap Team this is well worth checking out!  https://www.hotosm.org/   Segment Anything  (SAM) can segment objects by simply clicking or interactively selecting points to include or exclude from the object. This makes it a user-friendly tool for image segmentation. https://segment-anything.com/   Mapillary Mapillary is a platform that provides street-level imagery and map data from all over the world. The platform is powered by collaboration and computer vision, which helps in generating and maintaining up-to-date, detailed maps. https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/   GeoSpatial Jobs! Drone Deploy’s Earthworks team is looking for an experienced Back End Engineer Full time / Remote NV5 is looking for a Senior GIS Analyst https://mapscaping.com/jobs/ Want to help? I could really use some support!   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

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