Building dynamic cloud maps from the ground up

Calvin Murdock, Nathan Jacobs, Robert Pless

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Satellite imagery of cloud cover is extremely important for understanding and predicting weather. We demonstrate how this imagery can be constructed "from the ground up" without requiring expensive geo-stationary satellites. This is accomplished through a novel approach to approximate continental-scale cloud maps using only ground-level imagery from publicly-available webcams. We collected a year's worth of satellite data and simultaneously-captured, geo-located outdoor webcam images from 4388 sparsely distributed cameras across the continental USA. The satellite data is used to train a dynamic model of cloud motion alongside 4388 regression models (one for each camera) to relate ground-level webcam data to the satellite data at the camera's location. This novel application of large-scale computer vision to meteorology and remote sensing is enabled by a smoothed, hierarchically-regularized dynamic texture model whose system dynamics are driven to remain consistent with measurements from the geo-located webcams. We show that our hierarchical model is better able to incorporate sparse webcam measurements resulting in more accurate cloud maps in comparison to a standard dynamic textures implementation. Finally, we demonstrate that our model can be successfully applied to other natural image sequences from the DynTex database, suggesting a broader applicability of our method.

Original languageEnglish
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
Number of pages9
ISBN (Electronic)9781467383912
StatePublished - Feb 17 2015
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: Dec 11 2015Dec 18 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)1550-5499


Conference15th IEEE International Conference on Computer Vision, ICCV 2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Building dynamic cloud maps from the ground up'. Together they form a unique fingerprint.

Cite this