Crafting a quality application for a job you really want takes time, so you do not want to spread yourself too thin. When constructing your CV, it is important to keep your audience in mind. Realistically, the first set of eyes will likely be a computer algorithm, scraping the submitted CVs for certain keywords.
A commodity is a standard, interchangeable product or good which may be used as the input for creation of another product or service. While imagery absolutely satisfies the second part of this definition, we will see that it is far from standardized, or interchangeable. In short, no,satellite imagery is not a commodity.
To put it simply, point clouds are a collection of XYZ points that represent some real world object of nearly any scale.They can be generated in a few ways. As geospatial scientists, we mostly work with LAS/LAZ data collected by aerial LiDAR (light detection and ranging) scanners at varying scales, from landscapes, down to project sites. We may also derive point clouds from highly detailed orthoimagery of an area, such as from the products of a drone flight.
As a data scientist, you don’t just go in and solve problems. You make recommendations to multi-faceted issues so that you get a fantastic model in the end. You’ll also be advocating a better use and understanding of the data while you do that.
If you think AI solves every problem, then you’re overestimating its utility and where we’re at. However, on the other side, if you’re a business of any size, operating at least some type of technology, and you think AI can solve none of your problems — you’re also mistaken.
The first couple of criticisms I received were hard to take and difficult to comprehend. Especially when they were not a critique of a particular fact, but more on personal opinions, such as “I don’t believe in the future you envision,” or “It’s not as rosy or complicated as you envision it.” When you hear such thoughts as an early-stage professional, you can’t help but feel imposter syndrome. You wonder if you’re out of your league? Are you talking about things you should not be talking about?
GDAL stands forGeospatial Data Abstraction Library. You will find people pronouncing it variably asgee-dal, orgoo-dle. I come back to goo-dle frequently because it trips off the tongue a little more easily than gee-dal.Goo-dle was the original pronunciation. The founder of the GDAL project pronounced it goo-dle for many years. When he stopped being the maintainer of the project, he took a job with Google and found it difficult to say he worked at Google and was the founder of the GDAL project.
Commercial imagery resolution is down to 30 centimeters.With modern interpolation techniques, you can produce images with a 15-centimeter resolution equivalent. You canidentify features in pastures that otherwise would not have been visible. Counting livestock is important on several levels — for animal disease control and for inventory.
The applications of the satellite systems are endless and we separate them into different market segments. For example,agriculture,location-based services (mainly phones),transport usages (navigation on roads, rail or maritime), andaviation (landing procedures heavily rely on EGNOS).Thegeomatics market is important; it’s a segment that’s usually the first adopter because they need the highest precision locationing.
Super-resolution originates from the computer vision domain.The quality of an image is defined by its resolution. Super-resolution gives you a better image by applying an algorithm — to get a higher resolved image. It’s like upsampling, just smarter.
Geospatial standards, like any standard, are what we agree on as a community. It’s a way to describe how we model geospatial data, exchange it, subset it, process it, visualize it, or reference it. We need standards because we share and integrate data, and we solve complex problems.
Google Earth Engine is acloud computing platform for scientificanalysis andvisualization of geospatial data sets. It isfree to use for research, education, and nonprofit. Google Earth Engine is essentially streaming data. You don’t need to go online to download the data — you just need a browser, and you can access the entire Google Earth Engine data catalog and a bunch of tools to do the analysis and visualization.
Coaches ask questions framed to help you clarify your own mind and come up with your own solutions and ideas. It’s powerful because, as the studies show, if you help anyone generate their own ideas and solutions, they’re more likely to implement them and succeed at them.
One of those light bulbs appeared over the top of my head. This is like a microcosm of what’s happening globally with the climate crisis and dramatic earth system changes. We have a limited time to record the Earth as it exists now for future generations. I realized we could use LIDAR to create a permanent digital record of what the Earth’s land surface looks like today to preserve it for future generations.
Instead of one long antenna that sends one big pulse at one time and then collects the pulses that come back, SAR have a much smaller antenna that sends lots of pulses in quick succession over time as the satellite goes through space.It “listens” to the pulses that come back to it when it moves through its orbit.That’s why the name synthetic is applied to the radar.
I liked the geospatial component of things. I enjoyed solving a problem and then seeing the result. It wasn’t just a Microsoft Excel model or some database table. It was something I could visualize in GIS software. If there had been a path to becoming a more in-depth GIS analyst at this company, I might have stayed on it.
What is a voxel?. It’s a 3D volumetric pixel, a cube. But voxels are nothing new. They’ve been used extensively in two key areas within computing. Computer games render worlds and use voxels instead of polygons. Minecraft is a good example — it’s a voxel rendered world. Gaming companies love voxels for their multi-resolution capability over polygons. Robotics uses voxels for image processing to reduce the size of LIDAR point clouds and to create small dynamic maps — or what we call VOG (Voxel Occupancy Grid) — for robots.
Geospatial experts need to have a wide variety of skills. They have to link up with other systems and understand those other systems, like Tableau. It’s not enough to know your desktop or application. How will they interface with the other systems and integrate into the greater enterprise system?
Satellites are getting better, and the number of constellations is increasing. Still, many people are not using GNSS terminology — they call it GPS, which is the American GNSS.There is Galileo (European), GLONASS (Russian), or BeiDou (Chinese). Back in 2000, there were 40 GNSS satellites. Fast forward to 2021, and we now have over 100.
Most of the data seafloor bathymetry data — it’s from inversion from gravity. They measure gravity, rather ingeniously, using satellite altimetry and looking at the slope of the sea water’s surface. Imagine a flat chunk of ocean with a perfectly flat sea bottom. If you stick a sea mountain on the bottom, gravity pulls the water a little closer to it, and there’ll be a little hill of water sitting over that sea mountain.
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.
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.
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.
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!