Shengwen Li
Datos Biográficos
| ID | 6309540 |
|---|---|
| NOMBRE | Shengwen Li |
| NOMBRES | Shengwen |
| APELLIDO | Li |
| FIRMA | LI S |
| AFILIACIONES | China University of Geosciences |
| ORCID | 0000-0002-1829-4006 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 10 |
| TOTAL DE CITAS | 1 |
| OBRAS COMO AUTOR | 10 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2019 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 1 |
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…
Text Geolocation Prediction via Self-Supervised Learning
Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labele…
HGeoKG
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective…
Integrating Multiple Semantics of Street View Imagery for Semi-Supervised Building Function Identification
Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant progress. However, these methods use the visual features of SVIs to infer building functions, which ignores the contributions of the multiple potential semantics of SVIs, resulting in suboptimal ident…
Tianhuang formula ameliorates liver fibrosis by inhibiting CCL2-CCR2 axis and Mapk/NF-κB signaling pathway
Measuring interaction among cities in China
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…
Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model
This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Ma…
Capturing what human eyes perceive
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…
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…
Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model
This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Ma…
Capturing what human eyes perceive
Measuring interaction among cities in China
Tianhuang formula ameliorates liver fibrosis by inhibiting CCL2-CCR2 axis and Mapk/NF-κB signaling pathway
Text Geolocation Prediction via Self-Supervised Learning
Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labele…
HGeoKG
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective…
Integrating Multiple Semantics of Street View Imagery for Semi-Supervised Building Function Identification
Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant progress. However, these methods use the visual features of SVIs to infer building functions, which ignores the contributions of the multiple potential semantics of SVIs, resulting in suboptimal ident…
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…
Computer Science (6 obras) · Artificial Intelligence (5 obras) · Geography (4 obras) · Data science (3 obras) · Human Mobility and Location-Based Analysis (3 obras) · Machine learning (3 obras) · China (2 obras) · Complex Network Analysis Techniques (2 obras) · Data mining (2 obras) · Economic geography (2 obras)