About Amerisurv| Contact    
Magazine | Newsletter    
Flickr Photos | Advertise    
HomeNewsNewsletterAmerisurv DirectoryJobsStoreAuthorsHistoryArchivesBlogVideosEvents
Register to receive the Amerisurv Newsletter | Also See Our LiDAR News Newsletter | RSS Feed  

Sponsored By

Software Reviews
Continuing Series
An RTN expert provides everything you need to know about network-corrected real-time GNSS observations.
Click Here to begin the series,
or view the Article PDF's Here
76-PageFlip Compilation
of the entire series
Test Yourself

Got Answers?
Test your knowledge with NCEES-level questions.
  Start HERE
Meet the Authors
Check out our fine lineup of writers. Each an expert in his or her field.
Wow Factor
Sponsored By

Product Reviews
Partner Sites







Spatial Media LLC properties




  The American Surveyor     

Pix4D Announces Machine-Learning Point Cloud Classification Print E-mail
Written by Pix4D   
Monday, 25 September 2017

25 September 2017 - Machine learning meets photogrammetry

Machine-learning point cloud classification

With Pix4Dmapper 4.0 you get machine-learning tools for photogrammetry applications in your hands. It allows you to classify 3D point clouds into categories like buildings, roads or vegetation.
And this is just the beginning of Pix4Ds latest journey.
We believe in the ability of photogrammetry and machine learning techniques to revolutionize todays workflows and to enable many new ones.  In the end, it will allow the conversion of raw image input to 3D reality models with attributed semantic information.
That means, instead of having operators inspecting and measuring 3D reality models manually, they will directly receive automatically-generated answers to questions like:
How many trees are within the project area and at what locations? What is their height and species?
What is the total road surface area in your area of interest?
What is the amount and distribution of roofs that are suitable for solar cell coverage?
How many cars are at your parking lot and at what locations?
Answering these very specific questions will make workflows feasible that allow photogrammetric processing being connected directly to GIS databases to update their vectorized information based on any new drone data collected.
There is still work to be done. Machine learning techniques are as good as the training data that are used to build the classification models. We opted to give our users the tools to control and refine the classification. As a baby learns how to see and interpret its environment gradually as it grows, our machine learning techniques evolve with the training data and the results will model more object categories and become more reliable.
As of today, professionals will use the new machine learning based point classification mainly to automatically generate digital terrain models (DTMs). In the near future, point classification also forms the basis to extract buildings and model them as a semantic composition of geometry elements as roof, facade, windows, doors and balconies for example.
Our growing R&D teams in Lausanne, Berlin and San Francisco are dedicated to this challenge.

Christoph Strecha, CEO and Founder Pix4D

Trained algorithms based on geometry
Our first step in this direction is our novel machine-learning based point cloud classification.
We have trained algorithms based on geometry and pixel values to understand object classes.
We are able to collect user inputs to train new algorithms which can adapt to many topics, for instance in aggregates separating stockpiles from the bare terrain and measure volumes automatically with unprecedented accuracy, or digitizing automatically new road and urban areas.

Extract bare-earth terrain by excluding above-ground objects
A lot of hydrological or geological analysis need to be done with bare terrain models. In Pix4Dmapper, you can use the point classification function to separate all the above-ground objects and improve the classification using the point editing tools.

Ignore vegetations on top of a stockpile for more accurate volume measurement
To get an accurate volume measurement, it is crucial to remove vegetation or human-made objects from the point cloud. With the point classification, it would be more time-saving to achieve more reliable volume calculations.

Prevent electricity outage by vegetation growth control
Vegetation is one of the leading causes to power line outage. It is extremely important for the energy company to keep track on the vegetation growth to be able to trim it before it causes damages. With the point classification, the extracted infrastructures can be grouped and manually digitized for further analysis.

Pix4Dmapper 4.0: available in preview today!
Contains supervised machine-learning technology
Automatically classify 3D points based on both geometry and color
Visualize and improve the classified points in Pix4Dmapper rayCloud
Classify 10 million points in 3 minutes

About Pix4D
Pix4D is a developer of leading software that converts images taken by hand, by drone, or by plane into survey-grade accurate and georeferenced 2D mosaics, 3D models and point clouds. Founded in 2011, Pix4D is rapidly expanding from its headquarters in Lausanne, Switzerland, to offices in Shanghai, San Francisco and Berlin.

< Prev   Next >


Amerisurv Exclusive Online-only Article ticker
Featured Amerisurv Events
List Your Event Here
contact Amerisurv


Geneq Introduces
Net20 Pro Receiver

press [at] amerisurv.com
Online Internet Content


News Feeds

Subscribe to Amerisurv news & updates via RSS or get our Feedburn
xml feed

Need Help? See this RSS Tutorial

Historic Maps

post a job
Reach our audience of Professional land surveyors and Geo-Technology professionals with your GeoJobs career ad. Feel free to contact us if you need additional information.


Social Bookmarks

Amerisurv on Facebook 

Amerisurv LinkedIn Group 

Amerisurv Flickr Photos 

Amerisurv videos on YouTube 



The American Surveyor © All rights reserved / Privacy Statement
Spatial Media LLC
7820B Wormans Mill Road, #236
Frederick MD 21701
301-695-1538 - fax