There is a distinction in GIS between the people who use the tools with their knowledge and the people who develop those tools with a view on how the client will use them. I started out developing GIS products and environments for people and professionals to use. Then, I became more interested inwhy they used that data andhow they were using my tools in those ways.
Figuring out the viewpoint of the camera is a big part of augmenting reality. The camera has six degrees of freedom.The first three are straightforward — xyz coordinates. Or latitude, longitude, and elevation. Those give you a point in space.For a camera we also need to define the Euler angles — the yaw, the pitch, and the roll — especially if we care about what the camera is pointed at.This is the full six degrees of freedom state, also referred to as the pose.
SBAS stands for Satellite-Based Augmentation System to standard GPS or GNSS signals. It’s a service that improves the quality of positioning from GPS — from multiple meters down to sub-meter level.SBAS uses similar technologies to other high-precision correction services people might be familiar with. It leverages an entire network of continuously operating reference stations around the ground area.
OpenLayers is afully grown-up programming language. It runs both in the client and on the server, such as when you visit modern websites where you can edit documents or spreadsheets.
I got introduced to GIS and Earth observation (or remote sensing), which honestly, I hated. I took a few classes but couldn’t warm to it. Still, I had to pass the semester. As I was preparing for the exams, going through my notes, reading the required reading—slowly but surely, the usefulness of it all dawned on me
GRASS isnot limited to academia. It’s for anyone who needs simple to complex geospatial processing in whatever format or data model—raster, vector, 2D, 3D, and time series. Awide variety of businesses are using GRASS for data extraction, and provision of conclusions or analysis on top of extensive data.
You train a detector by teaching the machine learning model what you’re looking for by giving it examples. You give examples and see how it does. If it doesn’t do well, you give it more examples. You iterate until you get good enough results.
Commercial satellite providers produce somewhere between 100 and 200 terabytes of imagery a day ̶ a monstrous amount of information. Sentinel 2 has five years of daily refresh data. We have 40+ years of Landsat data. It’s a massive amount, particularly in the temporal dimension, where you can do longitudinal studies. Apache Spark and Raster Frames might just be the tools we need to handle this much data.
With the open data movement, there’s an ubiquity of data. We can let students pick their own data on topics that interest them. They find their own data for a geographic area they’re interested in, perhaps where they live or where they’d love to travel. They make connections to their own interests and lives. The more they’ll see the relevance of what they’re learning, the more they’re motivated.
The biggest value you get from satellite images is that you can see what’s on the ground. If there is too much water vapor in front of the satellite imaging sensors, it obscures the Earth’s surface, and you might lose all value of the platform.
WHAT IS OPEN SOURCE SOFTWARE? It’s software thatshares the actual instruction code, and the binary you run on your computer. There are different versions and variants of how that sharing happens. Essentially, you get access to the underlying recipe of the software. You can modify it and adapt it for your needs. This episode is all about building a business based on open source GIS software.
On one side, we have the promise of personalization of being seen and understood, provided we share our data about how we interact with the world. That data can be used to make things better. On the other side, we risk being exposed or being manipulated and treated like a product instead of the customer. In terms of data privacy, location plays a massive role. We, the geospatial community, play a role in this conversation.
Artists, mathematicians, and scientists think about their work as a structure that already exists. Their job is to chip away at the detritus. They clear the noise and the content that distracts. They dig up the bones of a fossil and work on revealing a structure that’s already there.
Take Michelangelo, who, by his own admission, was onlyfreeing David from the surrounding marble.
Mapmakers let the geographic content ̶ the data and the layers ̶ communicate.
If you're going freelance or self-employed in the earth observation sector, then youneed to have confidence in yourself and your ability to deal with the things that might get thrown at you. Its also important to remember that your confidence will increase with time.
What is SQL? - It's a structured query language. Putting it into terms of the history of languages, it's a language to query language. It's what computer scientists call a fourth-generation language.
What Elasticsearch can do for geospatial users,How it’s different from Postgres? Who should use it? Why it’s a special subset of NoSQL databases? All these answers and more i this episode of the MapScaping Podcast!
My passion is conservation. I was an archaeologist for eight years before I got into my second career, GIS. I would be out on sites surveying for archeology and come across mass developments where they'd bulldoze everything in the way of a pipeline. It was crushing me.
It's not really about the technologies we use, its about understanding the principles behind the technologies, and where and when to apply them. Spatial thinking is the key skill, we don't need to master every new GIS tools, just knowing that they exist might be enough.
For most people, when they think aboutthe relationship between GIS and COVID-19, the obvious solution is tracking data. At the highest level, if there is any spatial relationship between where we are in our societies, where we go, who we interact with, and spread the disease, our measurements, thenour policy tools must also have a geospatial component to capture that effect.It's not a question of whether spatial data and spatial analysis matter for COVID-19. The problem is understanding what tools exactly can be used for specific insights and decisions.
GPUs have orders of magnitude more processors. However, they're not as general-purpose as CPUs. They do discrete bits of work that are more or less the same weight. If you can figure out how to subdivide a problem into 20,000 pieces of equal weight, they will do it in less than a snap of a finger.
It's adiscrete system, breaking up the world into discrete cells. Every position in the world has a cell identifier associated with it. It's global. In H3, we subdivide each cell into seven smaller cells. If you try to draw that out, you’ll realize it doesn't fit right. We had to make some compromises ...
Google Cloud’senterprise data warehouse. It’s an immensely scalable and quick data storage and processing product that got a geospatial facelift a couple of years ago. Users interact with BigQuery with standard SQL verbs. It feels like a relational database except you can have relations and tables that are petabytes big ̶ but to the user, it’s still just a simple case of typing in SQL.
We now live in an age where almost everything is customizable and can be adapted to our individuality. In previous eras, maps were physical documents. If you went hiking and you took a one-to-one map with you, where would you even put it? It wouldn't be helpful. In the digital realm, we capture the information from smartphones, from IoT devices, intelligent cars driving around, satellites, and building sensors. We put these into place and have a perfectly clear picture of the world in its digital format—this is your digital one-to-one map.
The spatial internet of things is the ability to connect low-cost sensors over the internet and track devices, equipment, and people in real-time. It’s the next generation of the internet.
Segmenting data and audiences is really big at the moment, its about getting the right message to the right person at the right time. Geospatial is a big part of that, it's about putting your data in context.
Everything happens at a location and at a specific time. Why have GIS standards ignored the temporal aspect of data, why don't we have the same kind of functionality for spatial-temporal data that we have for spatial data? Anita Graser, the creator of the time manager plugin for QGIS answers these questions.
Property is surrounded by a legal space that the government defined and sectioned off to the owner with a street address for tax purposes. It’s a polygon, rectangle, or square space. A host of other information is connected to that parcel, such as when it was first subdivided, is there a structure on it, and does it have utility hookups? What are the permissions, zoning, and taxes owed on it?
Jupyter Notebooksare an interface for combining code, documentation, and data access. Previously, your data was held in one location. You coded somewhere else and output it to a different folder which you opened to see what your code did. A notebook is a single place where you combine code, workflow, documentation, and the results.
Remote sensing with a difference. That’s what Ellen Christopherson, CEO and founder ofclearGrid, is here to talk about. Their unique way of collecting radio frequency (RF) data makes them a valuable partner for utility and energy companies that do any type of meter readings and billings. Ellen’s background in aviation engineering gives her an edge to see how technology can deliver efficiency in this space.
Indoor positioning and navigation are still done on a case by case bases and generally only offer sub-meter accuracy. But what if you had millimeter accuracy? what problems could you solve or use cases could you dream up?
A digital twin is a digital replica or model of a living or non-living physical object. Geospatial and CAD data often provide the physical context for these objects but no digital twin would be complete without different real-time or near real-time data streams.
Anita Graser is a legendary open-source geospatial Python expert. With her extensive knowledge of the subject, she is here to convince us of why Python is a great language and how we can all get started learning it. Stick around to see the benefits and learn why Python may or may not be an option for your GIS project.
Two free and open-source apps that will help you collect GIS data in the field and synchronising that data to a centralized database. Online or offline these apps are integrated into the QGIS ecosystem and they just work!
GIStechnology is underutilized. There is a huge opportunity for professionals in the geospatial industry to be leaders and not just mapmakers. Cartography is an important part of what GIS professionals do but it does not have to be the only thining that they do.
Historically, CAD was used to draw buildings and facilities. These systems were never meant to store complex attribution we take for granted in the GIS world today. This geospatial podcast episode explores the differences between Computer aided-design and geographic information systems.
The Mars Rover Project. Autonomous robots monitoring substations. How is this all relevant to the geospatial community? Scott Nowicki is happy to clarify. He explains the technology that enables robots to integrate detailed maps, orientate, and move around their environments as they go on their daily business and build detailed change detection maps for substations and facilities management. But the question is, can they and do they truly add value to operations where human presence is difficult or unnecessary?
Learn how to apply for entry-level GIS jobs and geospatial internships. Find out what it takes to move from an entry-level GIS position to a mid-level / leadership role in GIS. You will also discover what recruitment looks like in the broader geospatial industry.
This week on the podcast Kent Marten from Tableau explains the cross over between business intelligence tools and traditional GIS software. We also explore the challenges around self serve mapping and answer the question "Is location still the next big thing"
Serving dynamic vector tiles might not be as difficult as you think. Paul Ramsey from Crunchy Data starts off by walking us through the early days of PostGIS, explaining what a vector tile is and the advantages over image tiles before explaining how you can easily and safely serve your own data as vector tiles straight from a PostgreSQL database.
A dive into the differences between remote sensing and earth observation, we walk through 4 major data collection platforms and the data that these platforms provide and where and how we might find it
QGIS is rapidly becoming the default opensource geospatial desktop tool for the GIS community. Today, Kurt Menke who has written serval books on QGIS shares his insights and experience with the QGIS and talks about so of the more exciting features that are built into QGIS
Serverless architectures provide an instantly scaleable application environment so that IT infrastructure may dynamically scale up and down to meet the demand of both machine and human users.
The changing world of eath observation and some of the challenges facing the industry at the moment. We also discuss remote sensing space with regards to the Gartner Hype Cycle and discuss the role of non-traditional players in the earth observation space. GIS / Geospatial podcast about remote sensing and earth observation.
John Bryant the founder of Mammothgeospatial introduces us to the power of an opensource SQL database called PostgreSQL. We talk about some advantages that this database has over flat files and why you might want to invest time and energy into learning more about relational databases and the Structured Query Language "SQL".
Geo-coding is not a solved problem but it is a very important step in locating and describing addresses with rooftop level accuracy is the first step in building risk profiles.
R is perhaps the most powerful computer environment for data analysis that is currently available. Tim Appelhans joins me on the show today to talk about his journey from learning R too developing new packages and extending the geospatial visualization capabilities of R
Drones are changing the frequency, resolution, and scale of geospatial data collection. SkyCatch is applying drone-based data collection to the mining industry which might just be the first step on the journey to an autonomous workplace.
An interview with one of the founders of Safe Software, Dale Lutz (an all-around nice guy and a thought leader in the geospatial world). Dale walks you thought the evolution of the problems that Safe is solving, and gives you insight into the fallacy of the one “file to rule them all” theory and talks about trends in the geospatial file formats and data exchange in general.
