Wenzheng Jia
Wenzheng Jia has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Litao Zhu
School of Computer and Artificial Intelligence, Nanjing University of Finance and Economics, Nanjing 210023, China
2 shared publications - Wen Dai
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
2 shared publications - Bo Wang
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
1 shared publication - Hao Wang
Reading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China
1 shared publication - He Wu
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
1 shared publication - Luyi Wang
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 211800, China
1 shared publication
Latest Publications
- Open Access
ASLIF: Accurate Lane-Area Perception Through Adaptive Fusion of Structural Lines and Instance Segmentation
Lane-level road inventory aims to jointly extract direction-consistent lane areas and visible lane counts from street-view images. However, existing line-based methods do not directly recover complete lane regions, whereas area-based methods may include irrelevant pavement. To address these challenges, we propose adaptive structural line–instance fusion (ASLIF), a prediction-level framework that combines structural line predictions with instance-level area predictions. ASLIF converts O2SFormer line predictions into regional masks and fuses them with the continuous regional response produced by Mask R-CNN. An image-level fusion coefficient adjusts the relative contributions of the two branches according to scene complexity, estimated lane density, and the difference in branch confidence. Experiments on a test set from Nanjing, China, show that ASLIF achieves a precision of 0.92, a micro-F1 of 0.90, a mean-F1 of 0.90, and a mean-IoU of 0.82, with the highest precision among the evaluated baseline semantic-segmentation methods. These results indicate that ASLIF provides a precision-oriented operating point that suppresses false-positive lane regions while retaining substantial regional coverage.
Open AccessASLIF: Accurate Lane-Area Perception Through Adaptive Fusion of Structural Lines and Instance Segmentation
Lane-level road inventory aims to jointly extract direction-consistent lane areas and visible lane counts from street-view images. However, existing line-based methods do not directly recover complete lane regions, whereas area-based methods may include irrelevant pavement. To address these challenges, we propose adaptive structural line–instance fusion (ASLIF), a prediction-level framework that combines structural line predictions with instance-level area predictions. ASLIF converts O2SFormer line predictions into regional masks and fuses them with the continuous regional response produced by Mask R-CNN. An image-level fusion coefficient adjusts the relative contributions of the two branches according to scene complexity, estimated lane density, and the difference in branch confidence. Experiments on a test set from Nanjing, China, show that ASLIF achieves a precision of 0.92, a micro-F1 of 0.90, a mean-F1 of 0.90, and a mean-IoU of 0.82, with the highest precision among the evaluated baseline semantic-segmentation methods. These results indicate that ASLIF provides a precision-oriented operating point that suppresses false-positive lane regions while retaining substantial regional coverage.
- Open Access
- 20 August 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (16) , 2818
Does Immersive VR Alter Landscape Perception? A Comparative Evaluation of UAV-Derived VR Versus 2D Imagery in Rural Villages
Traditional rural landscape evaluations have generally relied on ground-level photographs or videos. However, these approaches have limitations in spatial continuity, depth cues, and interactivity. Unmanned Aerial Vehicle (UAV) photogrammetry and immersive virtual reality (VR) were integrated into a comparative rural landscape evaluation framework to assess landscape aesthetic quality. UAV-derived 3D village models were generated and deployed on PICO 4 headsets through Unity 3D and the Cesium plugin, providing evaluators with spatially continuous and 6DoF-enabled immersive representations of village scenes. The evaluation included ten landscape feature factors, including color harmony, vegetation richness, building layout harmony, openness of view, and sense of spatial depth. Ratings were collected from 75 valid participants across 17 villages, with village-level mean scores serving as the primary unit of inference. Paired-samples t-tests, subgroup sensitivity analysis, expert-only presentation-order sensitivity analysis, Pearson correlations, Steiger tests for dependent correlations, stepwise multiple linear regression, nested leave-one-village-out cross-validation (LOOCV), and bootstrap variable-selection stability analysis were conducted to examine differences between the 2D photo-based and VR-based conditions. The results showed that: (1) overall satisfaction was significantly higher in the VR-based condition than in the 2D photo-based condition (3.46 vs. 3.24); (2) the condition-specific regression models retained different landscape feature factors: sense of spatial depth and color harmony in the 2D photo-based model, and vegetation distribution pattern and environmental comfort in the VR-based model; and (3) the VR-based regression model had a higher condition-specific internal than the 2D photo-based model ( vs. 0.569). Within the present dataset, the VR-based model also showed lower SD-normalized prediction error under nested LOOCV, while bootstrap resampling showed higher selection frequencies for the predictors retained in the VR-based model. Overall, the findings demonstrate the potential of UAV-derived immersive VR for rural landscape evaluation and provide new evidence on how presentation conditions influence landscape perception and evaluation.- 20 August 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (16) , 2818
Open AccessDoes Immersive VR Alter Landscape Perception? A Comparative Evaluation of UAV-Derived VR Versus 2D Imagery in Rural Villages
Traditional rural landscape evaluations have generally relied on ground-level photographs or videos. However, these approaches have limitations in spatial continuity, depth cues, and interactivity. Unmanned Aerial Vehicle (UAV) photogrammetry and immersive virtual reality (VR) were integrated into a comparative rural landscape evaluation framework to assess landscape aesthetic quality. UAV-derived 3D village models were generated and deployed on PICO 4 headsets through Unity 3D and the Cesium plugin, providing evaluators with spatially continuous and 6DoF-enabled immersive representations of village scenes. The evaluation included ten landscape feature factors, including color harmony, vegetation richness, building layout harmony, openness of view, and sense of spatial depth. Ratings were collected from 75 valid participants across 17 villages, with village-level mean scores serving as the primary unit of inference. Paired-samples t-tests, subgroup sensitivity analysis, expert-only presentation-order sensitivity analysis, Pearson correlations, Steiger tests for dependent correlations, stepwise multiple linear regression, nested leave-one-village-out cross-validation (LOOCV), and bootstrap variable-selection stability analysis were conducted to examine differences between the 2D photo-based and VR-based conditions. The results showed that: (1) overall satisfaction was significantly higher in the VR-based condition than in the 2D photo-based condition (3.46 vs. 3.24); (2) the condition-specific regression models retained different landscape feature factors: sense of spatial depth and color harmony in the 2D photo-based model, and vegetation distribution pattern and environmental comfort in the VR-based model; and (3) the VR-based regression model had a higher condition-specific internal than the 2D photo-based model ( vs. 0.569). Within the present dataset, the VR-based model also showed lower SD-normalized prediction error under nested LOOCV, while bootstrap resampling showed higher selection frequencies for the predictors retained in the VR-based model. Overall, the findings demonstrate the potential of UAV-derived immersive VR for rural landscape evaluation and provide new evidence on how presentation conditions influence landscape perception and evaluation.