Hai Lan

Orcid: 0000-0002-4119-4388

Affiliations:
  • George Mason University, NSF Spatiotemporal Innovation Center, Fairfax, USA
  • University of Maryland, Department of Geographical Sciences, College Park, MD, USA (PhD 2020)
  • New York University, Department of Computer Science and Engineering, Brooklyn, NY, USA


According to our database1, Hai Lan authored at least 12 papers between 2018 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2022
Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study.
Remote. Sens., 2022

An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example.
ISPRS Int. J. Geo Inf., 2022

Challenges and opportunities of the spatiotemporal responses to the global pandemic of COVID-19.
Ann. GIS, 2022

2021
Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012-2018.
Remote. Sens., 2021

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records.
IEEE Access, 2021

2020
Addressing Geographical Challenges in the Big Data Era utilizing Cloud Computing.
PhD thesis, 2020

Modeling urban growth by coupling localized spatio-temporal association analysis and binary logistic regression.
Comput. Environ. Urban Syst., 2020

Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in Mainland China Using Nighttime Light and Air Quality Data.
Remote. Sens., 2020

Taking the pulse of COVID-19: a spatiotemporal perspective.
Int. J. Digit. Earth, 2020

A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research.
Data, 2020

An Environmental Data Collection for COVID-19 Pandemic Research.
Data, 2020

2018
Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data.
J. Sensors, 2018


  Loading...