Di Yang

Assistant Professor

University of Wyoming

United States

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About

Dr. Di Yang is an Assistant Professor at the University of Wyoming, United States. His research areas encompass climate impacts on land cover changes, lake ecosystem dynamics, wildfire vulnerability analysis, and advanced image classification techniques using deep learning and reinforcement learning. His recent articles highlight his contributions to understanding biogeophysical impacts in Arctic regions, spatiotemporal patterns in Chinese lakes, and the application of AI in citizen science for environmental awareness.

Articles (24)

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2021

Collaborators (8)

Jue Yang

Brown University

UNITED STATES

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Martin Venturas

Profesor Contratado Doctor I3

Universidad Politécnica de Madrid

SPAIN

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Wenwen Cheng

Assistant Professor

University of Oklahoma

UNITED STATES

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Thomas Kolb

Professor

Northern Arizona University

UNITED STATES

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Yaqian He

Assistant Professor of Geography

University of Arkansas

UNITED STATES

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Ryan Carney

Assistant Professor of Digital Science

University of South Florida

UNITED STATES

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Farhat Binte Azam

University of South Florida

UNITED STATES

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Andrew R. Smith

Bangor University

UNITED KINGDOM

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Di Yang

Research Interests

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About

Articles (24)

Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023

The biogeophysical impacts of land cover change on climate extremes in the Arctic and Boreal regions

Classification and clustering analysis of standing dead trees and associated park asset wildfire vulnerability in Yellowstone National Park

Crowdsourcing geographic information for terrorism-related disaster awareness and mitigation: perspectives and challenges

Orthogonal Capsule Network with Meta-Reinforcement Learning for Small Sample Hyperspectral Image Classification

A Deep-Learning Workflow for CORONA-Based Historical Land Use Classifications

Mitigating drought mortality by incorporating topography into variable forest thinning strategies

A Metareinforcement-Learning-Based Hyperspectral Image Classification With a Small Sample Set

Fortifying Centers and Edges: Multidomain Feature Learning Meets Hyperspectral Image Classification

MHCFormer: Multiscale Hierarchical Conv-Aided Fourierformer for Hyperspectral Image Classification

GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science

Study on Forest and Grassland Ecological Space Structure in Eyu Mining Area and Potential Alternatives for Enhancing Carbon Sequestration

Spatio-Temporal Dynamic Characteristics of Carbon Use Efficiency in a Virgin Forest Area of Southeast Tibet

The Main Drivers of Wetland Evolution in the Beijing-Tianjin-Hebei Plain

Geospatial Modeling of Health, Socioeconomic, Demographic, and Environmental Factors with COVID-19 Incidence Rate in Arkansas, US

F3Net: Fast Fourier Filter Network for Hyperspectral Image Classification

SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Rapid Land Cover Classification Using a 36-Year Time Series of Multi-Source Remote Sensing Data

Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models

Coastal Vulnerability Assessment of Thrace Peninsula: Implications for Climate Change and Sea Level Rise

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

Remote Sensing and GIS-Based Suitability Mapping of Termite Habitat in the African Savanna: A Case Study of the Lowveld in Kruger National Park

Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level

Human Mobility to Parks Under the COVID‐19 Pandemic and Wildfire Seasons in the Western and Central United States

Collaborators (8)

Jue Yang

Martin Venturas

Wenwen Cheng

Thomas Kolb

Yaqian He

Ryan Carney

Farhat Binte Azam

Andrew R. Smith

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