Article
Computer Science, Artificial Intelligence
Maoguo Gong, Tongfei Liu, Mingyang Zhang, Qingfu Zhang, Di Lu, Hanhong Zheng, Fenlong Jiang
Summary: This paper proposes a context-content collaborative network (C3Net) with an encoder-decoder structure to achieve a good trade-off between precision and completeness in building extraction. The C3Net consists of a context-content aware module (C2AM) and an edge residual refinement module (ER2M) that capture the localization information of buildings and refine the features of decoder output, respectively. Extensive experiments show that the C3Net achieves competitive performance and a better trade-off between precision and completeness.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Environmental Sciences
Yong Wang, Xiangqiang Zeng, Xiaohan Liao, Dafang Zhuang
Summary: In this study, a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net) was proposed. Experimental results demonstrated that B-FGC-Net exhibited remarkable performance in extracting buildings from high resolution remote sensing images and successfully integrated information from both small and large scale buildings.
Article
Environmental Sciences
Liang Huang, Qiuyuan Tian, Bo-Hui Tang, Weipeng Le, Min Wang, Xianguang Ma
Summary: A change detection network for building VHR remote sensing images based on Siamese EfficientNet B4-MANet (Siam-EMNet) is proposed, which integrates dual attention mechanism to accurately detect change regions and rough edges of buildings.
Article
Environmental Sciences
Ziwei Liu, Mingchang Wang, Fengyan Wang, Xue Ji
Summary: Extracting road information from high-resolution remote sensing images is crucial but challenging due to increased spatial heterogeneity between different types of roads. To address this, RALC-Net is proposed to extract a complete and continuous road network using residual attention and local context-aware network, with further enhancement in performance through multi-scale dilated convolution modules.
Article
Computer Science, Information Systems
Leilei Xu, Yujun Liu, Peng Yang, Hao Chen, Hanyue Zhang, Dan Wang, Xin Zhang
Summary: The study introduces a novel method and model for automatic extraction of buildings from high-resolution remote sensing images, achieving improved segmentation accuracy and performance.
Article
Computer Science, Information Systems
Ramesh Raghavan, Dinesh Chander Verma, Digvijay Pandey, Rohit Anand, Binay Kumar Pandey, Harinder Singh
Summary: Building extraction is crucial in urban dynamics for disaster management, change detection, and population estimation. However, extracting buildings from satellite data is challenging due to variations in illumination and structure. To overcome this, a convolutional neural network and Mask-RCNN algorithm with advanced image augmentation technique are proposed. The results show improved accuracy in building extraction.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Environmental Sciences
Chong Ma, Hongyang Yin, Liguo Weng, Min Xia, Haifeng Lin
Summary: This research proposes a network based on feature differences and attention mechanisms to address issues in target detection, including target misdetection, false alarms, and blurry edges. Experimental results demonstrate that this method effectively alleviates these problems.
Article
Chemistry, Analytical
Jia Song, A-Xing Zhu, Yunqiang Zhu
Summary: This article explores the role of Vision Transformer networks in extracting building footprints from high-resolution satellite images. Different hyperparameter values were used to design and compare Transformer-based models, and their impact on accuracy was analyzed. The results suggest that smaller image patches and higher-dimensional embeddings contribute to higher accuracy. Furthermore, the Transformer-based network is shown to be scalable and can be trained with general-scale GPUs while achieving higher accuracy than convolutional neural networks.
Article
Engineering, Electrical & Electronic
Jie Wan, Zhong Xie, Yongyang Xu, Siqiong Chen, Qinjun Qiu
Summary: The DA-RoadNet is a road extraction network with dual attention mechanism, designed to effectively solve discontinuous problems and preserve the integrity of extracted roads by utilizing a deep learning network model and a novel attention mechanism module. Additionally, a hybrid loss function is employed to address class imbalance, ensuring stable training of the network model and avoiding local optima.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Geography, Physical
Xuan Yang, Shanshan Li, Zhengchao Chen, Jocelyn Chanussot, Xiuping Jia, Bing Zhang, Baipeng Li, Pan Chen
Summary: This paper proposes a multipath attention-fused network structure to address feature fusion challenges in semantic segmentation of remote sensing images. By fusing high-level abstract features and low-level spatial features, the network achieves state-of-the-art performance on two 2D datasets.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Dan Feng, Hongyun Chu, Ling Zheng
Summary: This paper proposes a frequency spectrum intensity attention network (FSIANet) for automatic building detection in high-resolution remote sensing imagery, which achieves state-of-the-art performance by introducing frequency spectrum intensity attention mechanism and atrous frequency spectrum attention pyramid.
Article
Environmental Sciences
Dejun Feng, Xingyu Shen, Yakun Xie, Yangge Liu, Jian Wang
Summary: This paper proposes an effective method to improve road extraction accuracy and reconstruct broken road lines caused by ground occlusion. An attention mechanism-based convolution neural network is established for feature enhancement, and a heuristic method based on connected domain analysis is proposed for road reconstruction. Experimental results show the effectiveness of the method in road extraction.
Article
Environmental Sciences
Liegang Xia, Xiongbo Zhang, Junxia Zhang, Haiping Yang, Tingting Chen
Summary: This study introduces a semi-supervised deep learning approach based on an edge detection network for extracting building roof boundaries from high-resolution remote sensing images, achieving satisfactory results with a small number of labeled samples and abundant unlabeled images used for joint training.
Article
Environmental Sciences
Getachew Workineh Gella, Lorenz Wendt, Stefan Lang, Dirk Tiede, Barbara Hofer, Yunya Gao, Andreas Braun
Summary: This study investigates the use of a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The model was trained using transfer learning from historical images, and showed better performance compared to training from scratch.
Article
Computer Science, Information Systems
Lei Lu, Tongfei Liu, Fenlong Jiang, Bei Han, Peng Zhao, Guoqiang Wang
Summary: With the development of VHR remote-sensing technology, automatic identification and extraction of building footprints play a significant role in urban development tracking. However, VHR technology, while characterizing building details accurately, also enhances background interference and noise, degrading the fine-grained detection of building footprints. To address these issues, this study proposes a denoising frequency attention network (DFANet) for extracting building footprints in VHR images, incorporating a denoising frequency attention module and a pyramid pooling module into the network architecture. Experimental results demonstrate the effectiveness and superiority of the proposed method, emphasizing the critical role it plays.
Article
Environmental Sciences
Xiaoshuang Ma, Hongming Hu, Penghai Wu
Summary: Denoising is a fundamental preprocessing step in SAR image processing. Evaluating the edge-preservation performance of filters has been done using various indices, but most of them do not fully exploit the statistical traits of SAR images. This paper reviews some indices and proposes a new referenceless index.
Article
Environmental Sciences
Ziyin Zeng, Yongyang Xu, Zhong Xie, Jie Wan, Weichao Wu, Wenxia Dai
Summary: This paper proposes a random graph-based graph convolution network, RG-GCN, to address the issue of insufficient samples in point cloud semantic segmentation. Through data augmentation and feature extraction, the network achieves excellent performance on indoor and outdoor datasets.
Article
Environmental Sciences
Jingzheng Zhao, Liyuan Wang, Hui Yang, Penghai Wu, Biao Wang, Chengrong Pan, Yanlan Wu
Summary: The study introduces a novel deep learning fusion network DSLN that utilizes NDVI to enhance land cover classification of HRRS images, with experiments showing promising results on the GF-1 dataset and good applicability for temporal and spatial distribution.
Article
Chemistry, Multidisciplinary
Xuejing Xie, Yawen Liu, Yongyang Xu, Zhanjun He, Xueye Chen, Xiaoyun Zheng, Zhong Xie
Summary: The functional classification of buildings is important for urban planning and government departments. A semi-supervised graph structure network combined with a unified message passing model is introduced for building function recognition. By utilizing the spatial distribution, characteristics, and POIs information of buildings, this method can capture more meaningful information with limited labels and achieve better classification results.
APPLIED SCIENCES-BASEL
(2022)
Article
Astronomy & Astrophysics
Bin Feng, Lirong Chen, Yongyang Xu, Yu Zhang
Summary: Based on stream sediment data in the Cu-Zn-Ag metallogenic area in southwest Fujian province, this study used three deep learning models: autoencoder (AE), multi-convolutional autoencoder (MCAE), and fusion convolutional autoencoder (FCAE) to extract features for geochemical anomaly identification. The results showed that FCAE had the highest consistency with known copper mineral occurrences, followed by MCAE and AE. FCAE combined the advantages of MCAE and AE, focusing more on structural distribution and mixed features.
EARTH AND SPACE SCIENCE
(2022)
Article
Environmental Sciences
Liufeng Tao, Yuqiong Cui, Yongyang Xu, Zhanlong Chen, Han Guo, Bo Huang, Zhong Xie
Summary: Urban fires pose threats to the economy and safety of urban residents. An optimized placement of fire stations is necessary to cover as many areas as possible and focus on high-risk areas. This study proposes a multi-objective optimization model for fire station planning based on the backup coverage model, using the SAVEE model to quantify the spatial heterogeneity of urban fires. The results demonstrate the effectiveness of the proposed model in covering high-risk areas with as few fire stations as possible.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2023)
Article
Environmental Sciences
Yongchuang Wu, Penghai Wu, Yanlan Wu, Hui Yang, Biao Wang
Summary: This study proposes a deep learning remote sensing crop classification method that highlights phenological features, and applies attention mechanism and residual connectivity. The method demonstrates high accuracy and reliability in classifying soybean, maize, and rice crops, with an overall classification accuracy of 90.73% and average F1 and IOU values of 89.57% and 81.48%, respectively. The proposed method can be readily applied to crop area estimations in different regions and years.
Article
Environmental Sciences
Yuting He, Penghai Wu, Xiaoshuang Ma, Jie Wang, Yanlan Wu
Summary: This study proposes a physical-based spatial-spectral deep fusion network (PSSDFN) to improve the accuracy of chlorophyll-a (Chl-a) retrieval. By combining MODIS and Sentinel-2 MSI data, the method achieved significant improvement in coastal water areas of large- and medium-sized lakes/oceans.
Article
Environmental Sciences
Yongchuang Wu, Yanlan Wu, Biao Wang, Hui Yang
Summary: In this article, a method to improve the accuracy of remote sensing crop mapping by extending the neighborhood window through a multiscale network was proposed. Experimental results showed that this method effectively reduced spatial inconsistency and boundary blurring in crop mapping, thereby improving mapping accuracy.
Article
Environmental Sciences
Binbin Song, Songhan Min, Hui Yang, Yongchuang Wu, Biao Wang
Summary: This study applies frequency-domain deep learning to classify crops in remote sensing images, enhancing interclass differences and reducing intraclass variations by adjusting different frequency components, leading to improved classification accuracy and robustness.
Article
Geochemistry & Geophysics
Anna Hu, Liang Wu, Siqiong Chen, Yongyang Xu, Haitao Wang, Zhong Xie
Summary: This article proposes a building shape-preserving framework to solve the imperfect boundary problem and eliminate sawtooth noise from building extractions. By using instance segmentation method and boundary network, the building boundaries can be extracted more accurately, and a footprint information evaluation algorithm is used to evaluate the extracted building shape. The experimental results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Remote Sensing
Yongyang Xu, Wei Tang, Ziyin Zeng, Weichao Wu, Jie Wan, Han Guo, Zhong Xie
Summary: 3D point cloud semantic segmentation is crucial for understanding 3D environments. Existing approaches of local context learning in point clouds are based on predefined neighbors, but K-nearest neighbor algorithm (KNN) is suboptimal. This study proposes NeiEA-Net, a simple and effective network that optimizes local neighbors in high-dimensional feature space for semantic segmentation of point clouds. The network further reduces redundant information by adaptively aggregating features of different scales. Experimental results on three large-scale benchmarks demonstrate the superiority of this network.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Engineering, Electrical & Electronic
Jie Wan, Ziyin Zeng, Qinjun Qiu, Zhong Xie, Yongyang Xu
Summary: This paper proposes an innovative network called PointNest, which achieves accurate point segmentation by learning multiscale point feature propagation. The introduction of a deep supervision strategy further improves training efficiency and performance. PointNest outperforms existing mainstream methods on three public benchmarks.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Remote Sensing
Ziyin Zeng, Yongyang Xu, Zhong Xie, Wei Tang, Jie Wan, Weichao Wu
Summary: In this study, a network called LEARD-Net is proposed for semantic segmentation of large-scale point cloud scene data. The network utilizes color information and employs local feature extraction and aggregation modules to effectively process the point cloud data.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Studies
Yuting Lu, Penghai Wu, Kaijian Xu
Summary: This study analyzes the evolution of urban heat island in Hefei, China based on multi-time scale land surface temperature data. It explores the role and mechanism of different function-oriented zones in the thermal environment. The study finds that heat islands are concentrated in the main city zone and there are significant differences among landscapes at different time scales. MFOZ planning has a positive effect in alleviating the urban thermal environment.
