Junfang Gong
Biographic Data
| ID | 4418355 |
|---|---|
| NAME | Junfang Gong |
| GIVEN NAMES | Junfang |
| FAMILY NAME | Gong |
| SIGNATURE | GONG J |
| AFFILIATIONS | China University of Geosciences |
| ORCID | 0000-0001-6176-9671 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Anchor-Guided Balanced Learning for Trajectory Representation
Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited ge…
From human mobility to building functions
Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events
Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different proper…
Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning
The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there a…
No prominent works on this page.
Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning
The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there a…
Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events
Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different proper…
From human mobility to building functions
Anchor-Guided Balanced Learning for Trajectory Representation
Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited ge…
Human Mobility and Location-Based Analysis (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Deep learning (2 works) · Machine learning (2 works) · Trajectory (2 works) · Activity recognition (1 work) · Automated Road and Building Extraction (1 work) · Benchmark (surveying (1 work) · Building model (1 work)