Mingzheng Yang
Biographic Data
| ID | 4894908 |
|---|---|
| NAME | Mingzheng Yang |
| GIVEN NAMES | Mingzheng |
| FAMILY NAME | Yang |
| SIGNATURE | YANG M |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0002-2936-3003 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Rapid Disaster Response and Damage Estimation with Social Media and Pretrained Large Language Models
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on using post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate dama…
Deciphering spatial-temporal dynamics of flood exposure in the United States
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
Sensing the pulse of the pandemic
Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users' reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities …
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
VictimFinder
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
VictimFinder
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
Deciphering spatial-temporal dynamics of flood exposure in the United States
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
Sensing the pulse of the pandemic
Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users' reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities …
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on using post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate dama…
Rapid Disaster Response and Damage Estimation with Social Media and Pretrained Large Language Models
Geography (5 works) · Computer Science (4 works) · Disaster Management and Resilience (4 works) · Artificial Intelligence (3 works) · Cartography (3 works) · Social media (3 works) · 2019-20 coronavirus outbreak (2 works) · Coronavirus disease 2019 (COVID-19 (2 works) · COVID-19 Pandemic Impacts (2 works) · Data science (2 works)