Digital Terrain Mapping via LIDAR

From the purely scientific (ozone-column mapping, imaging hydrometeors in clouds) to commercial (on-board detection of clear air turbulence, CAT), my exposure to LIDAR applications has been primarily atmospheric.

Of course, other applications of LIDAR technology exist, and one of these is Digital Terrain Mapping (DTM).

Terra Remote Sensing Inc. is a leader in LIDAR-based DTM. Particularly impressive is their ability to perform surface DTM in areas of dense vegetation. As I learned at a very recent meeting of the Ontario Association of Remote Sensing (OARS), Terra has already found a number of very practical applications for LIDAR-based DTM.

Some additional applications that come to mind are:

  • DTM of urban canopies for atmospheric experiments – Terra has already mapped buildings for various purposes. The same approach could be used to better ground (sorry 😉 atmospheric experiments. For example, the boundary-layer modeling that was conducted for Joint Urban 2003 (JU03) employed a digitization of Oklahoma City. A LIDAR-based DTM would’ve made this an even-more realistic effort.
  • Monitoring the progress of Global Change in the Arctic – In addition to LIDAR-based DTM, Terra is also having some success characterizing surfaces based on LIDAR intensity measurements. Because open water and a glacier would be expected to have different DTM and intensity characteristics, Terra should also be able to monitor Global Change as nunataks are progressively transformed into traditional islands (land isolated and surrounded by open water). With the Arctic as a bellwether for Global Change, it’s not surprising that the nunatak-to-island transformation is getting attention.

Although my additional examples are (once again) atmospheric in nature, as Terra is demonstrating, there are numerous applications for LIDAR-based technologies.

Genetic Aesthetics: Generative Software Meets Genetic Algorithms

I’m still reading Cloninger’s book, and just read a section on Generative Software (GS) – software used by contemporary designers to “… automate an increasingly large portion of the creative process.” As implied by the name, GS can produce a tremendous amount of output. It’s then up to the designer to be creatively stimulated as they sift through the GS output.

As I was reading Cloninger’s description, I couldn’t help but make my own connections with Genetic Algorithms (GAs). I’ve seen GAs applied in the physical sciences. For example, GAs can be used to generate models to fit data. The scientist provides an ancestor (a starting model), and then variations are derived through genetic processes such as mutation. Only the models with appropriate levels of fitness survive subsequent generations. Ultimately, what results is the best (i.e., most fit) model that explains the data according to the GA process.

In an analogous way, this is also what happens with the output from GS. Of course, in the GS case, it is the designer her/himself who determines what survives according to their own criteria.

The GS-GA connection is even stronger than my own association may cause you to believe.

In interviewing Joshua Davis for his book, Cloninger states:

At one point, you talked about creating software that would parse through the output of your generative software and select the iterations you were most likely to choose.

Davis responds:

That’s something [programmer] Branden Hall and I worked on called Genetic Aesthetic. It uses a neural network and genetic algorithms to create a “hot or not” situation. It says, “Rate this composition I generated on a scale from 1 to 10.” If I give it a 1, it says, “This isn’t beautiful. I should look at what kind of numbers were generated in this iteration and record those as unfavorable.” You have to train the software. Because the process is based on variables and numbers, over a very short period of time it’s able to learn what numbers are unsatisfactory and what numbers are satisfactory to that individual human critic. It changes per individual.

That certainly makes the GS-GA connection explicit and poetic, Genetic Aesthetic – I like that!

I’ve never worked with GAs. However, I did lead a project at KelResearch where our objective was to classify hydrometeors (i.e., raindrops, snowflakes, etc.). The hydrometeors were observed in situ by a sensor deployed on the wing of an airplane. Data was collected as the plane flew through winter storms. (Many of these campaigns were spearheaded by Prof. R. E. Stewart.) What we attempted to do was automate the classification of the hydrometeors on the basis of their shape. More specifically, we attempted to estimate the fractal dimension of each observed hydrometeor in the hopes of providing at automated classification scheme. Although this was feasible in principle, the resolution offered by the sensor made this impractical. Nonetheless, it was a interesting opportunity for me to personally explore the natural Genetic Aesthetics afforded by Canadian winter storms!

Annotating at the AGU

Perhaps two years ago, it was a challenge to find appropriate sessions at the American Geophysical Union Fall Meeting for submissions that addressed the intersection between geophysics and knowledge representation.

A year ago, there were quite a few to choose from.

This year, I was almost overwhelmed by choice.

I ended up selecting the “Earth and Space Science Cyberinfrastructure: Application and Theory of Knowledge Representation” session in the “Earth and Space Science Informatics” section. The work I intend to present, co-authored with Jerusha Lederman and Keith Aldridge also of York University, is described via an abstract elsewhere. I’ll need to prepare well as I’m presenting in good company and have only 15 minutes!

The makings for a productive and stimulating meeting are clearly present.

And for a Canadian in December, it’s pretty difficult not to enjoy the Bay Area!

Annotation Presentation to Remote Sensing Association

Next Wednesday, I’ll be making a presentation to the Ontario Association for Remote Sensing (OARS). The details are available online. As you can see from the abstract,

Incorporating Feature-Based Annotations into Automatically Generated Knowledge Representations

Earth Science Markup Language (ESML) is efficient and effective in representing scientific data in an XML-based formalism. However, features of the data being represented are not accounted for in ESML. Such features might derive from events (e.g., a gap in data collection due to instrument servicing), identifications (e.g., a scientifically interesting object in an image), or some other source. In order to account for features in an ESML context, consideration is given from the perspective of annotation, i.e., the addition of information to existing documents without changing the originals. Although it is possible to extend ESML to incorporate feature-based annotations internally (e.g., by extending the XML schema for ESML), there are a number of complicating factors that are identified. Rather than pursuing the ESML-extension approach, attention focuses on an external representation for feature-based annotations via XML Pointer Language (XPointer). In previous work, it has been shown that it is possible to extract relationships from ESML-based representations, and capture the results in the Resource Description Format (RDF). Thus attention focuses here on exploring and reporting on this same requirement for XPointer-based annotations of ESML representations. Earth Science examples allow for illustration of this approach for introducing annotations into automatically generated knowledge representations.

my intention is to emphsize some of my recent research into annotation. Most of this work is being done in collaboration with Jerusha Lederman and Keith Aldridge of York University.

The above abstract very closely resembles a submission that was recently accepted for the Fall Meeting of the American Geophysical Union (AGU). I’ll be blogging more on that soon.