Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Satellite imagery of cloud cover is extremely important for understanding and predicting ather.  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 bcams.  collected a year's worth of satellite data and simultaneously-captured, geo-located outdoor bcam 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 bcam 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 bcams.  show that our hierarchical model is better able to incorporate sparse bcam measurements resulting in more accurate cloud maps in comparison to a standard dynamic textures implementation. Finally,  demonstrate that our model can be successfully applied to other natural image sequences from the DynTex database, suggesting a broader applicability of our method.
['Calvin Murdock', 'Nathan Jacobs', 'Robert Pless']
I am writing a research paper on a idea Building Dynamic Cloud Maps from the Ground Up
[{"from": "human", "value": "I am writing a research paper on a idea Building Dynamic Cloud Maps from the Ground Up"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Satellite imagery of cloud cover is extremely important for understanding and predicting ather.  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 bcams.  collected a year's worth of satellite data and simultaneously-captured, geo-located outdoor bcam 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 bcam 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 bcams.  show that our hierarchical model is better able to incorporate sparse bcam measurements resulting in more accurate cloud maps in comparison to a standard dynamic textures implementation. Finally,  demonstrate that our model can be successfully applied to other natural image sequences from the DynTex database, suggesting a broader applicability of our method."}]