Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data

This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data.This is a typical data and computation-intensive research covering a series of geocomputation tasks that have not been implemented efficiently or optimally in prior works.This paper introduces a three-step computational solution to resolve the problem.For any given digital elevation model(DEM)data,automatic generation of control viewpoints is the first step through map algebra calculation and hydrological modeling approaches.For each viewpoint,the viewshed calculation then has to be implemented.The combined viewshed derived from the viewshed of all viewpoints establishes the maximum viewshed coverage of the given DEM.Finally,detecting the minimum set of viewpoints for the maximum coverage is a Non-deterministic Polynomial-time hard problem.The outcome of the computation has broader societal impacts since the research questions and solutions can be adapted into realworld application and decision-making practice,such as the distribution,optimization and management of telecommunication infrastructure and wildfire observation towers,and military tactics and operations dependent upon landscape and terrain features.

Detecting events from the social media through exemplar-enhanced supervised learning

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests.For example,conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event.Consequently,a renovated workflow was designed and implemented.The workflow consists of four sequential procedures:(1)Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages;(2)Apply Affinity Propagation to identify exemplars of Twitter messages;(3)Apply the cosine similarity calculation again to automatically match the exemplars to known training results,and(4)Apply accumulative exemplars to classify Twitter messages using a support vector machine approach.The overall correction ratio was over 90%when a series of ongoing and historical wildfire events were examined.

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

Supervised image classification has been widely utilized in a variety of remote sensing applications.When large volume of satellite imagery data and aerial photos are increasingly available,high-performance image processing solutions are required to handle large scale of data.This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data.The solution is scalable and satisfies the need of change detection,object identification,and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.