Publications - Zhe Chen

WHU-Synthetic: A Synthetic Perception Dataset for 3D Multi-task Model Research

Jiahao Zhou*, Chen Long*, Yue Xie, Jialiang Wang, Conglang Zhang, Boheng Li, Haiping Wang, Zhe Chen†, Zhen Dong

Transactions on Geoscience and Remote Sensing (IF: 7.5) 2025

We introduce WHU-Synthetic, a large-scale 3D synthetic perception dataset designed for multi-task learning, from the initial data augmentation, through scene understanding , to macro-level tasks. Besides, we implement several novel settings, making it possible to realize certain ideas that are difficult to achieve in real-world scenarios, such as sampling on city-level models, providing point clouds with different densities, and simulating temporal changes.

[Papar] [Code]

WHU-Synthetic: A Synthetic Perception Dataset for 3D Multi-task Model Research

Jiahao Zhou*, Chen Long*, Yue Xie, Jialiang Wang, Conglang Zhang, Boheng Li, Haiping Wang, Zhe Chen†, Zhen Dong

Transactions on Geoscience and Remote Sensing (IF: 7.5) 2025

We introduce WHU-Synthetic, a large-scale 3D synthetic perception dataset designed for multi-task learning, from the initial data augmentation, through scene understanding , to macro-level tasks. Besides, we implement several novel settings, making it possible to realize certain ideas that are difficult to achieve in real-world scenarios, such as sampling on city-level models, providing point clouds with different densities, and simulating temporal changes.

[Papar] [Code]

SparseDC: Depth Completion From Sparse and Non-uniform Inputs

Chen Long*, Wenxiao Zhang*, Zhe Chen, Haiping Wang, Yuan Liu, Zhen Cao, Zhen Dong†, Bisheng Yang

Information Fusion (IF: 14.8) 2024

SparseDC is a model for Depth Completion from Sparse and non-uniform inputs. Numerous experiments conducted both indoors and outdoors show how robust and effective the framework is when facing sparse and non-uniform input depths.

[Papar] [Code]

SparseDC: Depth Completion From Sparse and Non-uniform Inputs

Chen Long*, Wenxiao Zhang*, Zhe Chen, Haiping Wang, Yuan Liu, Zhen Cao, Zhen Dong†, Bisheng Yang

Information Fusion (IF: 14.8) 2024

SparseDC is a model for Depth Completion from Sparse and non-uniform inputs. Numerous experiments conducted both indoors and outdoors show how robust and effective the framework is when facing sparse and non-uniform input depths.

[Papar] [Code]

City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China

Zhe Chen, Bisheng Yang†, Rui Zhu, Zhen Dong

Applied Energy (IF: 10.1) 2024

This study proposes a framework for estimating the solar PV potential of city-level building surfaces without human annotation and data acquisition costs. Buildings are extracted from Google satellite images through multi-space joint optimization domain adaptation network, and LoD1 models are generated by combining global DSM with building footprints. The framework was verified by taking Wuhan city as an example.

[Papar] [Code]

City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China

Zhe Chen, Bisheng Yang†, Rui Zhu, Zhen Dong

Applied Energy (IF: 10.1) 2024

This study proposes a framework for estimating the solar PV potential of city-level building surfaces without human annotation and data acquisition costs. Buildings are extracted from Google satellite images through multi-space joint optimization domain adaptation network, and LoD1 models are generated by combining global DSM with building footprints. The framework was verified by taking Wuhan city as an example.

[Papar] [Code]

Joint alignment of the distribution in input and feature space for cross-domain aerial image semantic segmentation

Zhe Chen, Bisheng Yang, Ailong Ma, Mingjun Peng, Haiting Li, Tao Chen, Chi Chen†, Zhen Dong

International Journal of Applied Earth Observation and Geoinformation (IF: 7.6) 2022

A framework to jointly align the distribution in input and feature space for cross-domain aerial image semantic segmentation, our method demonstrates excellent performance in various cross-domain scenarios, including the discrepancy in geographic position and the discrepancy in both geographic position and imaging mode.

[Paper] [Code]

Joint alignment of the distribution in input and feature space for cross-domain aerial image semantic segmentation

Zhe Chen, Bisheng Yang, Ailong Ma, Mingjun Peng, Haiting Li, Tao Chen, Chi Chen†, Zhen Dong

International Journal of Applied Earth Observation and Geoinformation (IF: 7.6) 2022

A framework to jointly align the distribution in input and feature space for cross-domain aerial image semantic segmentation, our method demonstrates excellent performance in various cross-domain scenarios, including the discrepancy in geographic position and the discrepancy in both geographic position and imaging mode.

[Paper] [Code]