THE MAPSCAPING PODCAST

Imagery collection is a stage of the GIS process that has been taken for granted for some time. Commonly, consumers are limited to coarse government-provided satellite imagery, or must commission a UAV/UAS collection of the area they are interested in to obtain high quality data products that suit their business needs.

Deep learning and machine learning are both types of artificial intelligence (AI). Ultimately, the goal of AI is to help humans solve problems more efficiently. In order to do this we need to train the algorithm and to do that we need training data ( labeled data )

Building a startup from the ground up is no doubt an intimidating process, so it can be an excellent jumpstart to have specialized resources at hand when beginning such an adventure. There are organizations out there that exist to help startups in their early stages.

There are quite a few challenges in the pursuit of monitoring and mapping flooding. For ICEYE, these challenges begin with determining where collection needs to be to begin with. The nature of catastrophic events is that they happen quickly, and this reduces the timeline available to activate resources.

To put it simply, the blue dot is you. To put it less simply, the blue dot is your device’s predicted location based on the results of some unseen algorithms intertwined with the results of a set of fused location services.

The GISP is the certification program to become a Geographic Information Systems Professional. The program itself began to form in the early 2000s and is administered by the GIS Certification Institute (GISCI). The GISP can be considered as a demonstration of commitment to behaviors that enrich the geospatial profession, and an indicator that an individual aims for improvement and insertion into the greater GIS community.

Are you unhappy in your current GIS job, or feel stagnant in your development? Are you thinking that maybe you are not exploring your full professional potential in the geospatial field? If you are looking for a way to manage these feelings, while taking on very little startup risk, a geospatial side hustle may be the thing for you. If you are completely satisfied with your day-to-day work, that’s great! You will still benefit from keeping aware of market demand, and where the future industry opportunities may lie. 

OpenTopography is a free online public geospatial data portal, clearinghouse, or spatial data infrastructure for topographic data. Their mission statement is to “Democratize online access to high-resolution (meter to sub-meter scale), Earth science-oriented, topography data acquired with lidar and other technologies.” and to promote interaction, discovery, and knowledge in the Earth science community

GeoNode is an open-source geospatial platform, catalog, and content management system (CMS). The general functionality can be most likened to ArcGIS Online. Users can create filtered searches for their choice of geospatial data, by using keywords, and even filtering locations by selecting a bounding box on a map.

Drones generate a lot of data. So much data, that the software intended to process it needs for it to be thinned before it can be used. It can be tricky to dumb the data down, intelligently. You may be needing to reduce 300 points in a 1x1m square, down to a single point. Generally, these complicated decisions will be streamlined through use of algorithms that can help smooth the overall data.

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.

Social media and the internet have made it easier than ever to get your name out there, but not all publicity is good publicity. The key is not just consistently creating content, it is in creating quality content that attracts a consistent community.

 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.

GIS and the environmental and conservation fields have had a love affair since the beginning. Maps provide the perfect medium for communicating changes in the landscape to stakeholders, making GIS a solid investment for a conservancy looking to maximize their impact. 

OpenDroneMap is an open-source application with the goal of making it more accessible to process, view, and manipulate aerially collected imagery cohesively. 

GIS is a wonderfully flexible field in that it encompasses applications in many other fields. For those that wish to get their foot in the door, it is invaluable to begin preparing early.

MapStore is a front-end, open-source, client-side web GIS application. It can be described as both a product, and a framework.  As a product, it can be used out of the box to perform analysis, and create visualizations, geoportals, dashboards, GeoStories, and web applications sourcing data from your preferred geospatial server.

Ditching the predictability and security of full-time work is a truly intimidating step for professionals of any industry. A venture into the unknown. For some though, the risk is worth the independence and flexibility they come to enjoy in their work, and home lives. While the idea of not having a boss, or a set 9-5 schedule is appealing to nearly everyone, this does not mean that the remaining responsibilities of self-employment are for everyone.

The introduction of the web browser as a geospatial tool has become less of a novelty over the years, and for good reason. High-speed internet coverage has increased and spread across the globe, allowing more people than ever to access quality geospatial information and processing with minimal taxing of machine resources. 

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.

It’s an open source product developed in Java, using enterprise Java architecture, that allows you to take your geospatial data (shapefiles, special DBMS like post GIS) and publish it to the web. You get good-looking maps or other types of services where you can directly access your data.

Interferometrics, or InSAR, is taking stacks of radar data and looking at it over a certain timescale and seeing how the slight changes in elevation occur. It’s used for subsidence analysis and for understanding when there’s a risk — karstification or possible landslide. It’s looking at where there are minor changes in the movement of the Earth’s surface.

Some people might say that GIS is not a profession. I’d sayit’s like a toolkit; we use and apply it to different industries — a geographer might use it to do a map or analysis, for example. Once you have a toolkit and the skills, you candevelop them to be adaptable.The opportunities in the future are endless. You can do anything you wanted to.

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.

Are we making data available to those unexpected contributors? And are we making it available at a resolution sharp enough or high enough for the Dr. John Snows to have found that well?

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.

OpenStreetMap is so much more than a map and a database — it’s a global community of mappers. A while ago, OSM passed a significant milestone — the 100 millionth changeset was uploaded to it.This milestone represents a collective contribution of nearly 1 billion features globally in the past 16 plus years by a diverse community of over 1.5 million mappers.

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.

OpenLayers is a fully 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.