Zhaotang Liang
Biographic Data
| ID | 10706262 |
|---|---|
| NAME | Zhaotang Liang |
| GIVEN NAMES | Zhaotang |
| FAMILY NAME | Liang |
| SIGNATURE | LIANG Z |
| AFFILIATIONS | Institute of Space and Earth Information Science, The Chinese University of Hong Kong |
| ORCID | 0000-0001-9261-5261 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2019 |
| H-INDEX | 0 |
A human-machine adversarial scoring framework for urban perception assessment using street-view images
Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local …
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to u…
No prominent works on this page.
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to u…
A human-machine adversarial scoring framework for urban perception assessment using street-view images
Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local …
Artificial Intelligence (2 works) · Cartography (2 works) · Computer Science (2 works) · Data science (2 works) · Geography (2 works) · Adversarial system (1 works) · Civil engineering (1 works) · Computer vision (1 works) · Data mining (1 works) · Data-Driven Disease Surveillance (1 works)