Shisong Cao

Orcid: 0000-0001-9164-5805

According to our database1, Shisong Cao authored at least 11 papers between 2019 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data.
Remote. Sens., January, 2024

2023
Local climate zone mapping using remote sensing: a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime light data.
Int. J. Digit. Earth, December, 2023

Identifying Major Diurnal Patterns and Drivers of Surface Urban Heat Island Intensities across Local Climate Zones.
Remote. Sens., October, 2023

Spatially Explicit Modeling of Anthropogenic Heat Intensity in Beijing Center Area: An Investigation of Driving Factors with Urban Spatial Forms.
Sensors, September, 2023

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data.
Remote. Sens., July, 2023

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data.
Remote. Sens., 2023

2021
Urban Sprawl and Changes in Land-Use Efficiency in the Beijing-Tianjin-Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data.
Remote. Sens., 2021

Integrating Aerial LiDAR and Very-High-Resolution Images for Urban Functional Zone Mapping.
Remote. Sens., 2021

How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA.
Remote. Sens., 2021

The Influence of Sky View Factor on Daytime and Nighttime Urban Land Surface Temperature in Different Spatial-Temporal Scales: A Case Study of Beijing.
Remote. Sens., 2021

2019
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods.
Sensors, 2019


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