Nonparametric machine learning for mapping forest cover and exploring influential factors

Automatic CRP Mapping and Rectification using Nonparametric Machine Learning Approaches

This paper studies an uneven 2-class classification problem of satellite imagery, i.e., the mapping of United States Department of Agriculture (USDA)’s Conservation Reserve Program (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term resource conserving covers to improve soil, water and wildlife resources. With the recent program development, it is imperative to ...

Radar Imagery for Forest Cover Mapping

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Machine Processing of Landsat MSS Data and DMA Topographic Data for Forest Cover Type Mapping

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Nonparametric Bayesian Dictionary Learning for Machine Listening

Machine listening, i.e., giving machines the ability to extract useful information from the acoustic world in a manner similar to listeners, is a relatively undeveloped field that presents many interesting challenges. In the real world, sound rarely comes from a single source. For example, a piece of music may contain voices from the singers and accompaniments from different instruments, or a r...