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Carina Butterworth is a Geomatics Engineering Instructor at SAIT Polytechnic in Calgary, Canada. She is also finishing her Ph.D. in biomedical engineering at the Schulich School of Engineering, University of Calgary.

Carina uses our Engine application with both her students at SAIT, as well as high school students in the Calgary area.

When she visits a high school to introduce graduating students to the science of geomatics (and to encourage them to enroll in her program at SAIT), she brings along a variety of tools for them to go hands-on with. One of the tools is Engine, which they use to look at example geospatial projects. Carina says that Engine is simple enough and intuitive enough for even a high school student to use!

At the end of her talk, she uses a Leica BLK360 to capture the classroom and the students. Carina then uses the Engine Export feature to create a package of the point cloud and imagery, which she sends to the teacher and students as a souvenir. Using only their browser, they can easily interact with the scan data.

Carina also uses Engine as part of a SAIT student assignment that she designed to have students move through an entire project workflow. They go from planning, to data capture, to data processing, to sharing the final results with the “customer”. In this case, the customer is a SAIT lab supervisor and the lab is the project site.

Students don personal protective equipment, take a BLK360 sensor through the laboratory capturing data as if it was an actual job. They then process the results and review the finished project in Engine. When they are happy with the results, they use the Engine Export feature to provide their customer with the results. The lab supervisor is able to review the results using only the browser on their computer, and they can even use the file to add the lab scan to their department’s website!

“Engine has been instrumental in helping me showcase geomatics engineering to high school students and showing them how cool our technologies are with simplicity. It has also simplified teaching my 3D modeling class where the students find Engine to be intuitive, so I can focus on teaching the fundamentals of laser scanning, not the software.”

For more information about SAIT and the Geomatics Engineering Technology program there, please visit the department’s website at SAIT.ca.

For information on how you can get your own copy of Engine, please email us at info@solv3d.com

Twenty years ago, my paternal grandfather passed away. While we were all dealing with the grief of the situation and planning his funeral, my mother handed me a stack of what seemed like a million old family photographs.

She, being a genealogy buff, instructed me to “Find some good photographs of your grandfather” and “Digitize every photo using this fancy scanner”.  She also told me to label every person who appeared in each photo, along with where it was taken and what was going on in the photo. Some of the photos dated back to when I was a five-year-old kid running around with my cousins on a hot summer’s day. Others were only a few years old, taken when we were visiting Grandma and Grandpa and eating homemade chicken noodles and mashed potatoes that my grandma made from scratch.

At the time, going through these photos was a very emotional and tedious task. Off and on over the course of the week, I sat there slowly putting a photo in the scanner one-by-one, hitting the scan button, and writing in a journal all the information I could extract out of the photo. I know I was probably making a few mistakes along the way, but I didn’t care, I just wanted this long process to end.

Today, every single photograph taken is natively stored in a digital form in the camera and in many cases, stored online immediately after it is taken. Throughout the Internet, we all have access to millions of photographs of people, our friends, pets, food, buildings, flowers, and landscapes. These are all logged automatically using artificial intelligence. I can open my photos app on my smart phone, select a photo and it automatically shows me all the photos I have that have the same person in it. I can simply search for “car” and find all the pictures of my old red sports car that I used to drive to university every day. I can digitally erase the photobomber out of the picture that I don’t want in that otherwise perfect shot I took. It’s quite amazing that technology exists so readily, right in our hands. You don’t need a Ph.D. in computer vision to be able to do this sort of work, you just need the right application to help you out.

Here at Solv3d, our customers collect thousands upon thousands of miles (or kilometers) of LiDAR, orthomosaics, and panoramic images every year. They are mapping the world and creating a digitized version of it. This digitalization requires them to identify areas (or objects) of interest, such as street signs.

For example, a municipality may want to quickly identify the location of all their stop signs and yield signs, and then cross reference this against a list of dates the signs were installed. They may need to use this information for maintenance; a maintenance manager could use this information to estimate costs to upgrade all these signs throughout the entire city boundary.

We set out to provide a solution that will allow our customers to cost effectively identify objects of interest in their datasets in a timely manner. We knew that whatever we came up with had to produce results consistent between datasets. We also set ourselves a goal to develop a solution that was both secure (the data and the model had to remain local to the user) and flexible (the user should be able to identify whatever is important to them, not what we thought was important). Our solution is a deep learning-based tool that our customers can use to produce their own model and then use it to identify objects of interest in their data.

The process is simple and very flexible. Users load a recent set of panoramic images into Engine that they then use to train a model. They do this by using Engine to draw polygons around 10 to 20 examples of the object they are interested in, such as a stop sign or a yield sign [Figure 1]. 

They then press a button to have Engine “teach” the model about the example. As Engine is teaching the model, they can monitor the accuracy of the model as it works through its learning process [Figure 2].

When Engine has finished training the model, users then use it to apply the model to their data. Engine will show the results on the screen.

Finally, Engine can export the results, including an estimate of the locations of each object, in multiple formats such as CSV. This allows users to share the results with others, or use the results in other applications such as SOLV3D Encompass or Esri ArcGIS. Figure 4 shows results inside of Encompass.

Once you have trained the model, you can use that model any time on other data to identify the same object of interest. The model is very cost effective to use on subsequent datasets, and much faster when compared to a manual process. 

Because Engine is a local application, you have complete control over your data and your model.  Both the model and your data stay with you. The model is yours and is not shared with anyone else, which means you can use your data strategically by training it to identify unique items that your competitors may not have data for.

Because the model is computer based, the results are consistent, and Engine will provide you with a statistical estimation of its effectiveness at identifying the objects – something difficult (if not impossible) to do with a manual process.

Do I wish I had this technology 20 years ago when I was cataloging all those photographs for my mother?

I sure do!

What I find even more amazing is that this technology is only a stepping stone towards what is yet to come with automatic analysis of geospatial datasets. In the very near future, using the combination of imagery and point cloud datasets, we plan to deliver more value by aggregation of results of different datasets to improve the accuracy of detection and to automate workflows even further.

We developed an intensive, hands-on workshop to introduce you to deep learning and how it can be applied to geospatial data like imagery.  To learn more, please attend our workshop.

Additionally, if you would like more information on how Engine can help identify items you need to find in your data, contact us at sales@solv3d.com.

With internet services like Facebook, Twitter, and TikTok, you would think that sharing information would be very easy these days. But when it comes to sharing large amounts of geospatial data, sharing can still be a challenge. In this blog post, I will look at two concepts for sharing: local-first and cloud-based. Both concepts have been around since the start of the computer era, and the pendulum swings back and forth as to which is in favour at any given time.

Cloud-Based Sharing

The pendulum is currently in the cloud camp. Most people turn to cloud-based applications to share data with stakeholders, such as team members and clients. One such application is our SOLV3D encompass™ (Encompass), which I will use to illustrate this type of sharing.

The application runs on a central server or in a cloud service such as AWS (Amazon Web Services). Users can log-in from remote locations anywhere in the world. They can store data in the same location as the application or store it on another server or cloud service.

A typical workflow for such an application is:

1. The company using the application makes one user the administrator (admin) to organize and manage the application for all users. 
2. The admin first creates a “project” that defines the scope of the data they will share with stakeholders. Projects can be defined several ways, for example, their project could be a small construction project with different time periods (epochs); it could be a large construction project with many times and locations; or it could be the inside of a building covering several floors and times.
3. The admin uploads data to a cloud location. If that location is not the same as the sharing application itself, then they can link the data location to the application.
4. The admin then invites other users to access the application and project. The admin may grant different access levels to each user. For example, they may grant a user full access to the data (e.g., the ability to export sections of the data) or they may grant more limited access (e.g., only the ability to view data).
5. When the admin has loaded the data into the application, the application automatically gives all invited users secure access to it.

The pros and cons of cloud-based sharing are:
Pro:

• The data synchronization is real-time – all users see the same dataset as soon as the admin has loaded it into the project. The exception is restricted access users; the admin needs to let them know of data updates by email.
• Users can access project data from anywhere with an internet connection, and using almost any platform: mobile, tablet, laptop, or desktop.
• Secure access stakeholders can use the application to jointly work on a project, such as leaving notes, comments, or tags for other stakeholders. Unsecure access stakeholders cannot.
• Data sharing is usually more secure, as the admin can assign different levels of access to users.
• The admin (or another user) can embed a project into a webpage, such as their company website, like in this example.
• Cloud-based sharing applications can be inexpensive if you need them only for a couple of months, as most providers offer them in a software-as-as-service (SaaS) format.
• Cloud-based applications efficiently store large amounts of data, as there is only one, central copy, typically known as the “one source of truth”.

Con:

• Cloud-based sharing applications tend to be more expensive over the long term (e.g., two years or more) if the provider only offers them in a SaaS format. An exception is our Encompass application, which we offer both as short-term SaaS and as a perpetual licence.
• Data security could be an issue, as you may not have control over where the data or the application reside. Encompass is again an exception, as you control the location of both the data and the application.
• Users need an internet connection to access the data. This can be a huge problem if users are working at remote sites.
• You need to make one person the administrator of the system, which reduces flexibility of your team, as only one person can upload data for the team to use.

Local-First Sharing

A local-first application runs on your laptop or desktop. One such application is our SOLV3D engine™ (Engine) program, which I will use to illustrate this type of sharing. To share, you export a “share package”, which you send to your stakeholders by e-mail.

The workflow is quite simple:

1. The user who wants to share geospatial information loads the data into a project and then exports a share package using Engine. There is no admin in this case, all users who have Engine can do their own work without needing a central person to configure and run a system.
2. The user then sends an e-mail to other stakeholders (they do not have to be Engine users) with the file attached or as a link to a share drive. Many e-mail programs, like Gmail, will automatically upload large files to share drives and put the required link into the email like the example below:

3. The stakeholder receives the e-mail and downloads the share file and can now see the shared data using the included viewer (they do not need Engine or any other program).

The pros and cons of a local-first sharing are:
Pro:

• The cost can be lower, as you can start with one program for one user. Also, there are no centralized services to manage or pay for.
• All stakeholders do not have to be a user on the system to receive a shared project.
• Stakeholders can access project data from anywhere without an internet connection. Once they have the data on their computer, they can access it any time.
• The user sharing the project controls where the data and the application reside.
• The sharing user can encrypt the share file if they need to.
• You do not need to make one person an administrator over the system, as there is no system to administer.
• The share package can have a dataset that stakeholders can use in other applications, such as publishing to a company website, like in this example.
• Managing restrictions is much simpler – the user either sends the share package to a stakeholder or they do not!
• Local-first applications usually have other useful features, such as data processing and analysis functions.

Con:

• Once the sharing user sends the data out to a stakeholder, they have no control over who the stakeholder might send the data on to.
• The data synchronization is static – if there is new data or changes to the data, the sharing user needs to send new share packages to all stakeholders and request them to download before viewing.
• Local first applications are less efficient at storing large volumes of data, as users store their own copies of the data.

Summary

Both cloud-based and local-first sharing are practical ways to share large amounts of your geospatial data. The main deciding factors are cost and timeliness of data. Cloud based sharing is faster and handles large amounts of data better, but local-first is simpler and less expensive.

If you have a need to share your geospatial data with others, be sure to check out our upcoming webinar on sharing where we show how both Encompass and Engine share data, and we will provide you with 4 weeks access to both applications for you to decide which best suits your needs. Sign-up using the following link.

Recently we have been experimenting with the creation of a top-down floorplan view from point cloud data. In our latest version of SOLV3D engine™ (Engine), we have released a tool called “Generate Floorplan”. Through the examination of point density across a sparse matrix, this tool maps the results into a transparent georeferenced tiff file ready for consumption in SOLV3D encompass™ (Encompass), or other software like Esri ArcGIS. This is extremely useful in remote or construction sites where the user would like a highly accurate and quick to produce floor plan.

In this example, which uses terrestrial scanner data, a user can clearly see the layout of the indoor facility. The tanks, wall edges, catwalk and other features are clearly visible.

Pushing this a bit further, by changing different parameters, the user can fine tune the results to make some things clearer to see.

Moving to an outdoor mobile mapping project, we can see that the building edges are greatly enhanced. By removing the ground classification and lowering the percentile, nearby features such as trees and poles become more visible.

Use cases for this tool could include:

• Viewing topography changes.
• Checking positional correctness of building walls or foundations as well as any changes when compared to design.
• Visibly see extent of LiDAR data on a computer that may not have the tools in place to view.

Future enhancements may include:

• Extraction of 2D linework of captured building edges or breaklines.
• Comparing the generated image to UAV/orthophotos for disaster analysis.
• Pair the generated image with Engine’s object identification algorithms to automatically identify trees or other unique objects picked up within the dataset.

The latest release of Engine (3.0.5) also includes some enhanced blurring of vehicles and people within images, a change detection function showing spatial differences between two-point clouds, as well as several cleanup utilities for converting/scaling different attributes of extra information within point cloud data.

To use this feature, or any other of our 70+ algorithms, please contact us to access a free 7-day trial of Engine.

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Whether apparent or not, almost every data set and application today have a geographic component built in. It could be as simple as an X,Y location of your computer mouse within a web-application, the monitoring of assets and their locations, construction progress over time including how components fit together, or perhaps a more advanced self-driving car that needs to make sure it isn’t running into that parent pushing their baby across the street. All of these examples include some sort of geospatial information, a standard that identifies the data having an implicit or explicit association with a location relative to Earth.

Over the past several years, the amount of spatial data available has exploded. Before the internet, thirty years ago, no one really had any detailed sense of where things were located except surveyors and geographers. It was extremely costly, and in many cases impossible, to track your assets based on location. Geographic dataset coverage was a challenge which was solved when internet services started becoming available in remote areas and not just in major centers. Within the last 10-15 years, we have seen systems start talking to each other, allowing users to bring increased information together, including location. Device coverage was also an issue which we are now solving as systems are getting smaller and less costly, allowing more and more “things” to record and provide all sorts of data. The common term for this development and architecture is known as the Internet of Things (IoT). Along with this, there are now even some companies working on building microchips that can essentially capture 3D points (like LiDAR) – small enough to fit within a mobile phone! There remains the question of how should a company manage and share all of this information from all of these devices to the rest of their team/stakeholders?

Our team at SOLV3D, deals with extremely large and complex geospatial datasets.
Most of the time, our clients’ data lives on computer servers, or hard drives somewhere and is utilized by one person or a small team of people (usually within the same organization), preventing this typically expensive data from being shared and used effectively. We are looking to change this and provide a method for sharing the information effectively over the internet.

Regardless of the sharing method of choice, many of the organizations we work with aren’t in the day-to-day business of developing software or managing big IT software projects. This means they simply do not understand the architecture behind hosting and sharing data of this magnitude.

After speaking with dozens of individuals at companies throughout North America
and Europe, it seems there some consistent thoughts in regards to the needs of users:

• They typically do not need to access all of the project data sets on a daily basis.
• Rather, they just need to access sub-sets or single tile/polygon of an area. They do not want to wait in order to see the latest data in an area. They want to be able to see it right away.
• They want to compare versions of data for the same area to see how the area has changed over time.

To meet these needs, the data needs to be:

• On the internet, accessible from a web browser and no special software needed.
• Organized, or merged, in a way that a user can use a map and easily find out their data coverage.
• Organized in a chronological fashion, and accessible with associated metadata that lets a user know what they are looking at, when the data was recorded, and allows the user to easily compare datasets of various vintages.
• Easily accessible, to allow analysis on the data such as measurements or comparisons. Downloadable for use in other off-line programs.
• Provided in the format of a known common file format and properly georeferenced.
• Accessible by as many users as needed, and not restricted to those with technical expertise.
• While the hardware and skill sets of data collectors have evolved rapidly over the past number of years, to the point where they can collect data with millimetre accuracy and properly geo-reference it even if it is several kilometres under the ground, they often revert back to archaic practices when they attempt to deliver and disseminate this data. Typically, their data ends up on a USB hard-drive and mailed across the country to their client! Let’s not get into the problems that can arise when a hard-drive is mailed across the country, but trust me, you’re definitely taking a risk doing so.

We also see most data collectors employing different workflows for each hardware type they employ. This results in datasets that are all over the place in terms of consistency, especially when it comes to combining this data with other datasets. All of that hardwon data accuracy is for nothing if the data is lost in transit, or if the data is not consistent from dataset to dataset.

By sharing data over the internet, data collectors can dramatically reduce the risk of losing their data when they send it to their clients. This method also usually means that their clients can access their data immediately, and not several days later.

Sharing geospatial data over the Internet does come with its set of challenges however:

• Too much data can slow things down. Data typically needs to be formatted, indexed or split up to allow for quick access.
• Data needs to be in a common format. To be accessible by many people, the data needs to be organized and presented in a format that is easily consumable – i.e. not everyone has a high-end computer running some CAD software.
• This all boils down to the user experience on how to visualize and interact with the data.
• The data needs to be accessible. Systems should be in place to allow people to collaborate/comment/work with other team members without too much hassle
• The data need to be secure, protected and only accessible to the people who have permissions to access it. The data needs to be available for users when it is needed. You can’t have any downtime, or have the data slow to access.

As an organization, you may choose to take on these challenges yourself, however, this typically involves forming an entire professional development team. Instead, I recommend looking at companies or products that specialize in dealing with and managing these types and magnitude of data. By doing so, as they expand their product offerings to solve use-cases for their users, they can also take advantage of these features.

Companies that specialize in creating professional software, have the team and expertise to help you effectively manage your data. They are the ones that can help you deal with different types of data, along with making it easier for you and your clients to collaborate.

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