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Akademický článek

Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach

Autor: Zhonghui Lv, Karinna Nunez, Ethan Brewer, Dan Runfola

Publikováno v: GIScience & Remote Sensing, Vol 61, Iss 1 (2024)

ABSTRACTCoastal wetlands, especially tidal marshes, play a crucial role in supporting ecosystems and slowing shoreline erosion. Accurate and cost-effective identification and classification of various marsh types, such as high and low marshes, are im

Externí odkaz: https://doaj.org/article/5b14975723984b7594031aaab41bd9d8

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Akademický článek

Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning

Autor: Ethan Brewer, Giovani Valdrighi, Parikshit Solunke, Joao Rulff, Yurii Piadyk, Zhonghui Lv, Jorge Poco, Claudio Silva

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5668-5679 (2024)

Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help s

Externí odkaz: https://doaj.org/article/cb1e829b75bb462caa9c9400c75f8c84

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Akademický článek

Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation

Autor: Guofei Xiang, Songyi Dian, Shaofeng Du, Zhonghui Lv

Publikováno v: Sensors, Vol 23, Iss 2, p 762 (2023)

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL

Externí odkaz: https://doaj.org/article/aa0e3356f4574a6c82ece32014285bdc

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GeoQuery: Integrating HPC systems and public web-based geospatial data tools

Autor: Zhonghui Lv, Seth Goodman, Ariel BenYishay, Daniel M. Runfola

Publikováno v: Computers & Geosciences. 122:103-112

Interdisciplinary use of geospatial data requires the integration of data from a breadth of sources, and frequently involves the harmonization of different methods of sampling, measurement, and technical data types. These integrative efforts are ofte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::92228cf529399f414be6e895bee452a1
https://doi.org/10.1016/j.cageo.2018.10.009

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