IMAGINiT Civil Solutions Blog

Demo or Proof of Concept: What's the Difference?

We demonstrate software. A lot. And it's fun. Showing someone the 'what could be' if they were using a different technology or even helping someone by offering up a tip or new feature is great. Demonstrations are intended to give a lot of information in a short period of time. To give a general idea of what can be done with a new technology or process.

Proof-of-concepts are different. They are intended to provide proof that a given system would work effectively. They are detailed and customized. 

When it comes to Reality Capture, we do a lot of both - demos and POCs. We often get asked just what is the difference between the two. So we took the time to write up a new whitepaper: Five Frequently Asked Questions about Proof-of-Concepts. 

If you still have questions after that, just let us know.

A UAV Where You Might Not Expect It

OK, so we’ve all been touched by the drone explosion.  They’re everywhere and they can be fascinating, but sometimes the technology gets a little spooky.  This UAV, called CRACUNS, developed at the Johns Hopkins University Applied Physics Laboratory in Laurel, Maryland takes spooky to a new level.

You can watch the Johns Hopkins video of CRACUNS here.

Just when you thought it was safe to go back to the beach. . .

Working With Some Big(ish) Data

Recently, I have been experimenting with a few tools to process and display larger sets of data.  I’m looking at this because, increasingly, there are much larger amounts of data available that may contain valuable information about an area of study.  Having some tools that can deal with this data easily and quickly would be a great help in spotting things I might want to look at more closely using some of my standard tools like AutoCAD Map 3D and Autodesk Infrastructure Map Server.

Python is a language that lends itself to this type of experiment.  It is easy to use, fast and has a wide range of capabilities.  In this post, I’m going to show some New York City taxi data, which is openly available, and makes an interesting dataset for testing.  I’ve contained all of this experiment in a Jupyter Notebook, This is a web application that allows you to create and share documents that contain live code in a number of languages, equations, visualizations and explanatory text. It is an ideal environment for cleaning and transforming data, simulation, statistical modeling, plotting, and a lot more.  These notebooks can be shared through email, and services like Dropbox, GitHub and the Jupyter Notebook Viewer.

So, let’s take a look.  Here are some excerpts from my notebook.

 

With a few lines of Python, the data is loaded and processed.  You can see the last five records of the file displayed.  Next, I’ll import some diagnostics and the graphics system, datashader.

 

Now, I’ll set some bounds and aggregate the data based on the location of the drop-off points.  Note that it took under a minute to process almost 11.5 million points (this is just on my laptop).  The points are plotted using the aggregation values between a low value (light blue) to a high value (dark blue)

 

As you would expect, the greatest density is in Manhattan and at the airports.  No surprises there.  But, this is only a basic plot of the drop-off locations.  I can take this a lot further by looking at parameters like time of day, number of passengers, trip distances, fares, and so on.  I can share these results easily with colleagues and interactively change the values of the parameters I’m using in the notebook.

I hope to explore this further in upcoming posts and tie it together with some additional applications.  Stay tuned!

AutoCAD LT 2016 and Civil 3D Object Enablers

AutoCAD LT 2016 will use the Civil 3D 2016 Object Enablers to allow you to see Civil 3D objects correctly when you open a Civil 3D drawing in AutoCAD LT 2016.  However, many of us have experienced problems with this, and have found that the proxy objects either did not represent correctly, or the enabler did not install as it should have.  This was due to a problem with the Civil 3D 2016 Object Enabler.  The newest version of the enabler (SP2) has solved this issue.

Here’s what you need to know.  Starting with a system with no other Autodesk products installed, I installed a 64 bit version of AutoCAD LT 2016 and then applied the SP1 (AutoCAD_LT_2016_SP1_64bit.exe) to it, so it was up to date. Next, I downloaded and installed the Civil 3D 2016 Object Enabler from here.

Be sure to use the correct enabler (make sure it is the SP2 version).

 

When you install an earlier version of the enabler on a system having only AutoCAD LT 2016 installed, the enabler will not detect a supported product and it will not install. If AutoCAD LT 2016 and another Autodesk product that is recognized by the installer are present, the object enabler will install and appear to be functioning, but it will not read drawings correctly and you will see proxy warnings. If you have an earlier version of the 2016 enabler installed, uninstall it and then install the SP2 version.

In my case, with only AutoCAD LT 2016 installed, the install screens look like this.

 

I tested a number of Civil 3D drawings with this AutoCAD LT 2016 configuration. All of the drawings responded correctly. Here’s a drawing created using Civil 3D 2016.

 

You can see AutoCAD LT 2016 recognizes the surface correctly.

 

Here are some COGO points from an earlier Civil 3D drawing version. Again, they are recognized correctly.

 

So, when using AutoCAD LT 2016, as long as you have the correct version of the Civil 3D 2016 Object Enabler installed, everything now will work as it should.

Contours or Triangles?

James Mussleman | Applications Expert

How do you create an existing grade surface when your surveyor isn’t utilizing Civil 3D? Recently I had two separate clients whose survey departments were not utilizing Civil 3D while the engineering department was. This results in an issue when the engineering department needs an existing grade surface in order to utilize Civil 3D to its full potential.

The best option would be to transition the survey department into using Civil 3D. Unfortunately, this was not an option in either case. In both the cases the clients were resorting to using the polylines that were generated from the surveyor in order to create this surface. On the surface (no pun intended) this appears to create an exact copy of the existing contours. Unfortunately looks can be deceiving. All they’ve done is create a surface that displays contours in the same place. This does not mean that all of the areas between the contours are exactly the same. If we turn the triangles of this surface on we can see how it has been constructed:

The TIN is constructed by connecting all of the vertices on the polyline to the one before and after. The issue is that this assumes it is a constant slope from one contour to the next. It does not handle depressions or mounds that lie between the contour interval. The more appropriate way to construct this survey would be from the surveyor’s triangles which are an exact representation of the TIN. I say the triangles and not the points because the triangles are the surveyor’s representation of how the points are connected. The benefit of using the triangles is that they have already been adjusted to accurately represent the existing grade. Also with this method we do not need to utilize the surveyor’s breaklines because they would already be built into the TIN. Here is an example of that same surface generated from TIN lines:

As stated previously, this will be an exact representation of the surveyor’s surface and not just the contours. The main benefit of this is that it reduces the potential error that can be introduced when using contours.

A quick note; if your surveyor can only give you the TIN in the form of lines Civil 3D can handle that as well. When creating the surface choose the option for Drawing Objects and set the object type to Lines. The last important step is to check on Maintain edges from objects.

The importance of that setting is that if we don’t check it, Civil 3D will reinterpret the triangles. By checking that option on it will keep the triangle edges right where they were.

I hope that this post will help you reconsider how you’re translating non Civil 3D survey data.

Mapping the World With Your Pictures

Sometimes, you come across a technology which is just fun, while being useful as well.  Here’s one I saw the other day.  It’s a platform called Mapillary, a community based photomapping service where anyone, anywhere in the world, can upload photos from a mobile app (iOS and Android) or a camera (think of your favorite drone).  Once a photo has been uploaded, the company’s computer vision analysis software extracts the geographic information, blurs sensitive parts of the images like license plates and faces, and detects traffic signs from the photo.  Here’s the cool thing.  The photos, based on their geographic information, are stitched together on the map along with other uploaded photos, creating a digital representation of each location in real time as seen by people who actually were there.  I’m not sure how differences in the seasons or times of the day are accounted for (maybe you can set a map view based on those parameters), or who determines what level of privacy should be applied, but it’s compelling, nevertheless.  Current estimates place the number of photo uploads globally at over 50 million, mapping more than 1.2 million kilometers in over 170 countries.

The idea of using photos to map is not new.  Google has been doing this for a while.  What makes Mapillary different is that the content is uploaded by people actually on the ground, each of whom shares an experience of the place.  The potential for mapping out of the way places and areas where there is little formal cartography makes Mapillary unique.  Maps evolve in real time and can be enriched quickly as events occur and people participate in or are affected by those events.

I can see Mapillary as a new way to navigate areas not commonly represented by standard commercial or government mapping efforts.  Information from these photos will show how areas change, where people move around in cities, how they travel from one place to another, and how they respond to natural and man-made events.  This is a valuable source of information to aid in understanding transportation needs, city and regional planning and infrastructure.

There is an extensive API to access the data and Mapillary partners with nonprofits to help them improve infrastructure in developing countries around the world.  The World Bank trains university students and local community members to use Mapillary in Dar Es Salaam, Tanzania, to create accurate maps of the most flood-prone areas of the city.  The Red Cross has been mapping Haiti so NGOs and individuals can use the data to better respond to crises affecting the area.

Geospatial Data Quality Guide

In a number of previous posts, I have talked about the evolution of the nature of geospatial data and how that data impacts both those who create and manage it and those who ultimately consume it.  New technologies for the capture, production and distribution of data have opened numerous new opportunities and presented significant challenges within our society. Along with the traditional industry and government data providers, there are now new organizations ranging from small companies to global data management services using a range of evolving technologies and data distribution methods for a mass market.  This is changing the landscape in regard to evaluating the quality of geospatial data and managing the risks of such data being used inappropriately.  The progression from the traditional Business-to-Business (B2B) paradigm toward a Business-to-Consumer (B2C) and Consumer-to-Consumer (C2C) paradigm is accelerating with the rapid advances in technology.  Within these new B2C and C2C contexts, this increasingly large group of geospatial data users quite often does not understand the uncertain nature of geospatial data and they may take digital data for granted, or rely on this data based on an incorrectly presumed level of precision and accuracy.  As you can imagine, this sometimes may lead to erroneous decisions, with possible dire social, political or economic consequences.

In any mass market, consumers are granted certain rights and providers of any goods or services have obligations to deal fairly within those markets and respect those rights.  Governing bodies at any number of levels develop regulations and court decisions based on the interpretations of those regulations hold that consumers must be informed about the quality of a product or service, as well as any risks, conditions or prohibited usage related to that product or service.  We’re all familiar with warranty documents and user manuals, as a broad example

The Geospatial Data Quality Guide explains, for every phase of a geospatial data product lifecycle, how to manage the data quality and what risks may be inherent within those phases.  Using the ISO 19157 Geospatial information - Data Quality international standard, it explains the geospatial data evaluation process as well as the general risk management within the framework of the ISO 31000 Risk Management - Principles and Guidelines international standard.  There are numerous examples regarding managing the risks of inappropriate geospatial data usage as well as recommendations relevant to geospatial data quality within the B2B, B2C and C2C contexts.

You can get a copy of the Geospatial Data Quality Guide here.