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RailPC: A large-scale railway point cloud semantic segmentation dataset

CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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A Seasonal-Series Lstm Network for Irregular Urban Function Zone Recognition Using Sentinel-2 Images

SSRN

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SSRN 2024年

作者： Hu, Ting Han, Mengyu Guo, Zixuan School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Technology Innovation Center for Integration Application in Remote Sensing and Navigation Ministry of Natural Resources Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation/ Positioning and Smart Applications Nanjing210044 China

Urban Functional Zone (UFZ) is the fundamental unit for urban planning and management, which plays a crucial role in improving urban management efficiency and optimizing urban layout. The classification of UFZs has benefited from the rich information and fine features provided by high spatial resolution (HSR) remote sensing images. The seasonal variations of ground objects differ across different UFZs. However, HSR remote sensing images typically lack seasonal information and come with high acquisition costs. Therefore, this study proposes a classification framework for UFZ based on all-seasonal Sentinel-2 remote sensing images, learning the spectral-spatial-temporal features for detailed mapping of UFZs. Firstly, to balance the significant scale difference of block (i.e., the fundamental mapping unit) size in 10-meter resolution remote sensing images, an adaptive gradient perception (AGP) mechanism is used to guide the feature extraction of different-scale blocks. Secondly, the bag of visual words (BOVW) model is employed to extract block-level spectral-spatial features. Thirdly, a seasonal series LSTM network is proposed to capture block-level temporal dynamic information, particularly focusing on the spectral-temporal patterns of different UFZs. The proposed framework is applied to UFZ classification in four typical cities in China. The overall accuracy (OA) for all cases reaches around 93%, which is an improvement of approximately 7% compared to other methods. Our results demonstrate the superiority and portability of this framework, as well as the significant potential of open-source remote sensing images in large-scale UFZ mapping. © 2024, The Authors. All rights reserved.

关键词： remote sensing

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Estimation of winter wheat chlorophyll content by combing canopy spectrum red edge parameters with random forest machine learning

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2024年第4期40卷 166-176页

作者： Wang, Zhen Li, Yingxue Wu, Fang Zou, Xiaochen School of Remote Sensing and Surveying Engineering Nanjing University of Information Science and Technology Nanjing210044 China Technology Innovation Center of Integration Applicaitons in Remote Sensing and Navigation Ministry of Natural Resources P.R. China Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications Nanjing210044 China School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Xinghua Meteorological Bureau Xinghua225700 China

The sudden increase in vegetation canopy reflectance from low reflectance in red band to near infrared band forms the red-edge spectral characteristics, which is unique to healthy vegetation. Many parameters that can describe this characteristic have been designed and developed as important indicators of crop growth and nutrition status. However, few studies have systematically compared and evaluated the applicability of these red-edge parameters to estimate winter wheat leaf CHL values at different growth stages and nitrogen application levels. In this study, canopy spectral reflectance and leaf CHL of winter wheat at 4 key growth stages (jointing stage, heading stage, flowering stage and filling stage) and 3 nitrogen application levels were obtained through a 4-year field experiment. The sensitivity of 47 spectral red-edge parameters to CHL was evaluated, and the relative importance of spectral red-edge parameters was used to optimize the random forest machine learning model to estimate winter wheat CHL. The results showed that the sensitivity of spectral red edge parameter to CHL was affected by the growth period and nitrogen application level of winter wheat, and the correlation R2 between the best red edge parameter and CHL in a single growth period was between 0.39 and 0.89. The best red-edge parameter in the whole growth period was NDDRmid, and the coefficient of determination between it and CHL was 0.76. The sensitivity was the highest in filling stage, and the coefficient of determination between the best red edge parameter RVI5 and CHL was 0.89 and the R2 between red edge parameters REPRpi, NDDRmid, RVI2, RVI4, RVI5, RVI6, NDRE, RVI12 and RVI13 with CHL were all higher than 0.80. Nitrogen application level increased the sensitivity of red-edge parameter to CHL. At single nitrogen application level, the coefficient of determination between best red edge parameter and CHL was between 0.75 and 0.81. At N1 and N2 conditions the best red-edge parameter is NDDRm

关键词： Nitrogen

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Cross-Resolution Land Cover Classification Using Outdated Products and Transformers

arXiv

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arXiv 2024年

作者： Ni, Huan Zhao, Yubin Guan, Haiyan Jiang, Cheng Jie, Yongshi Wang, Xing Shen, Yiyang School of Remote Sensing & Geomatics Engineering Nanjing University of Information Science & Technology Nanjing210044 China Technology Innovation Center of Integration Applications in Remote Sensing and Navigation Ministry of Natural Resources Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation Positioning and Smart Applications Nanjing210044 China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing Institute of Space Mechanics and Electricity Beijing100094 China

Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an improvement in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labeled data is still a challenge, hindering the large-scale application of land cover classification methods. In this paper, we propose a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data. First, to capture long-range dependencies without missing the fine-grained details of objects, we propose a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention. Second, we propose an anti-noise loss calculation (ANLC) module based on optimal transport (OT). Anti-noise loss calculation identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products. By introducing a weakly supervised loss with weights and employing unsupervised loss, the RDM-based U-Net-like Transformer was trained. remote sensing images with 1 m resolution and the corresponding ground-truths of six states in the United States were employed to validate the performance of the proposed method. The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017. The results show the superiority of the proposed method compared to state-of-the-art methods. The code is available at https://***/yu-ni1989/ANLC-Former. © 2024, CC BY-NC-ND.

关键词： Semantics

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MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of remote sensing Images

arXiv

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arXiv 2023年

作者： Wang, Libo Dong, Sijun Chen, Ying Meng, Xiaoliang Fang, Shenghui Fei, Songlin School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Technology Innovation Center of Integration Applications in Remote Sensing and Navigation Ministry of Natural Resources China Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications Nanjing210044 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China China Telecom Research Institute Shanghai200120 China Purdue University West LafayetteIN47907 United States

Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation applications, such as land use land cover mapping, environment monitoring, and sustainable development. Driven by rapid developments in artificial intelligence, deep learning (DL) has emerged as the mainstream for semantic segmentation and has achieved many breakthroughs in the field of remote sensing. However, most DL-based methods focus on unimodal visual data while ignoring rich multimodal information involved in the real world. Non-visual data, such as text, can gather extra knowledge from the real world, which can strengthen the interpretability, reliability, and generalization of visual models. Inspired by this, we propose a novel metadata-collaborative segmentation network (MetaSegNet) that applies vision-language representation learning for semantic segmentation of remote sensing images. Unlike the common model structure that only uses unimodal visual data, we extract the key characteristic (e.g. the climate zone) from freely available remote sensing image metadata and transfer it into geographic text prompts via the generic ChatGPT. Then, we construct an image encoder, a text encoder, and a crossmodal attention fusion subnetwork to extract the image and text feature and apply image-text interaction. Benefiting from such a design, the proposed MetaSegNet not only demonstrates superior generalization in zero-shot testing but also achieves competitive accuracy with the state-of-the-art semantic segmentation methods on the large-scale OpenEarthMap dataset (70.4% mIoU) and the Potsdam dataset (93.3% mean F1 score) as well as the LoveDA dataset (52.0% mIoU). Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of remote sensing Imagery

arXiv

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arXiv 2024年

作者： Wang, Libo Li, Dongxu Dong, Sijun Meng, Xiaoliang Zhang, Xiaokang Hong, Danfeng School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Technology Innovation Center for Integration Applications in Remote Sensing and Navigation Ministry of Natural Resources Jiangsu Nanjing210044 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China The School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate semantic segmentation of remote sensing images remains a challenge due to the complex spatial-temporal scenes and multi-scale geo-objects. Driven by the wave of deep learning (DL), CNN- and Transformer-based semantic segmentation methods have been explored widely, and these two architectures both revealed the importance of multi-scale feature representation for strengthening semantic information of geo-objects. However, the actual multi-scale feature fusion often comes with the semantic redundancy issue due to homogeneous semantic contents in pyramid features. To handle this issue, we propose a novel Mamba-based segmentation network, namely PyramidMamba. Specifically, we design a plug-and-play decoder, which develops a dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in multi-scale feature fusion. Comprehensive ablation experiments illustrate the effectiveness and superiority of the proposed method in enhancing multi-scale feature representation as well as the great potential for real-time semantic segmentation. Moreover, our PyramidMamba yields state-of-the-art performance on three publicly available datasets, i.e. the OpenEarthMap (70.8% mIoU), ISPRS Vaihingen (84.8% mIoU) and Potsdam (88.0% mIoU) datasets. The code will be available at https://***/WangLibo1995/GeoSeg. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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A Progressive Registration-Fusion Co-Optimization A-Mamba Network: Towards Deep Unregistered Hyperspectral and Multispectral Fusion

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IEEE Transactions on Geoscience and remote sensing 2025年 63卷

作者： Li, Zan Wen, Yue Xiao, Song Qu, Jiahui Li, Nan Dong, Wenqian Xidian University State Key Laboratory of Integrated Service Network Xi’an710071 China Beijing Electronic Science and Technology Institute Department of Electronic and Communication Engineering Beijing100070 China Xidian University School of Telecommunications Engineering Xi’an710071 China Chuzhou University Anhui Province Key Laboratory of Physical Geographic Environment Anhui Engineering Research Center of Remote Sensing and Geoinformatics Anhui Center for Collaborative Innovation in Geographical Information Integration and Application Chuzhou239000 China

The existing methods of hyperspectral image (HSI) and multispectral image (MSI) fusion usually overlook the fact that multi-source images acquired under different imaging conditions are generally not perfectly registered. Despite the many such methods that have begun to address registration issues, it is still a challenge that most works perform registration and fusion as two separate steps, resulting in a cumulative error. To address this challenge, we propose a progressive registration-fusion co-optimization A-Mamba network (PRFCoAM), which iteratively optimizes the modal-aligned progressively registration-fusion (MAPRF) module to adaptively corrects the deformation from an extensive to a detailed level and refines the fusion results at each level to achieve progressive registration-fusion co-optimization. The proposed MAPRF module integrates the modal unified local aware registration (MULAR) block and interactive attention Mamba fusion (IAMF) block, which facilitates the network comprehensively and efficiently capture features of different levels. Specifically, MULAR adaptively learns spectral and spatial degradation functions to transform the input images into a unified modality and progressively repairs non-rigid pixel offsets by capturing the correlations and differences between corresponding regions of images. IAMF multi-directionally scans the spatial and spectral global dependent features of the well-registered images, which can stimulate the potential of Mamba in fusion and achieve a win-win situation of computational efficiency and selectivity advantages in the global acceptance domain. Extensive experiments demonstrate PRFCoAM can flexibly deal with different degrees and kinds of non-rigid deformation and achieves state-of-the-art performance. © 1980-2012 IEEE.

关键词： Image registration

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Hybrid CNN-Transformer Network With a Weighted MSE Loss for Global Sea Surface Wind Speed Retrieval From GNSS-R Data

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IEEE Transactions on Geoscience and remote sensing 2025年 63卷

作者： Qiao, Xin Yan, Qingyun Huang, Weimin Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John’sNLA1B 3X5 Canada Nanjing University of Information Science and Technology School of Environmental Science and Engineering Nanjing210044 China Chuzhou University Anhui Province Key Laboratory of Physical Geographic Environment Anhui Engineering Research Center of Remote Sensing and Geoinformatics Anhui Center for Collaborative Innovation in Geographical Information Integration and Application Chuzhou239000 China

The global navigation satellite system reflectometry (GNSS-R) plays a crucial role in sea surface wind speed measurement, and convolutional neural networks (CNNs) have been a widely used method for wind speed retrieval from GNSS-R data. However, CNNs have limitations in global feature extraction due to the fixed convolutional kernels. Moreover, current studies on wind speed retrieval from GNSS-R data exhibit significant overfitting at low wind speed and underestimation at high wind speed due to the extremely imbalanced data distribution. To address these issues, a hybrid CNN-transformer network (CTN) with a weighted mean square error (mse) loss is proposed in this study. Specifically, the designed CTN incorporates CNN and transformer encoder blocks to capture local and global features, with a dedicated feature fusion module to integrate these features. In addition, a novel weighted mse loss function is designed to tackle the issue of imbalanced data distribution and enhance the estimation accuracy at high wind speeds. The proposed method is validated on Cyclone GNSS (CYGNSS) data and demonstrates improved performance compared with other machine learning algorithms. It achieves a root mean square difference (RMSD) of 1.417 m/s compared to the European center for Medium-Range Weather Forecasts (ECMWF) reanalysis data and 1.620 m/s compared to the buoy data from the National Data Buoy center (NDBC). Notably, the proposed model trained with the weighted mse loss function shows an improvement of 20.3% in RMSD for samples with wind speeds exceeding 15 m/s, highlighting the effectiveness of the designed loss function in handling high wind speed data. © 1980-2012 IEEE.

关键词： Global positioning system

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Aerosol Optical Depth Estimate Using Ground-Measured Spectral Skylight Ratio Method

Aerosol Optical Depth Estimate Using Ground-Measured Spectra...

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Jing Nie Huazhong Ren Hui Zeng Jiaji Dong Jinxin Guo Yitong Zheng Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China Geographic Information System Technology Innovation Center of the Ministry of Natural Resources Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Aerosol Optical Depth (AOD) is an important physical quantity of atmospheric turbidity and a key factor for the atmospheric correction of optical remote sensing image. Several approaches have been developed to retrieve AOD from remote sensing data, and at ground level the sunphotometer is usually used to measure AOD. However, the sunphotometer is expensive and not portable for field campaign, which make it difficult to synchronously obtain AOD data along with other relevant measurement. In order to overcome this problem, this study proposed a new way to estimate AOD using Ground-measured Spectral Skylight Ratio method (GSSR) on the basis that skylight ratio is highly determined by such parameter. In the GSSR method, a look-up table (LUT) containing spectral skylight ratio in 400-1000nm was first established from the MODTRAN code under various AOD levels, solar zenith angles (SZA), and atmospheric types. Based on the LUT and actual skylight ratio measured from a ground spectrometer, the AOD was then determined using a shortest distance between the measurement and the spectral values in the LUT. Finally, the AOD derived from the GSSR method was validated using the AOD data from the CE318 sunphotometer data, and it was found that AOD error was about 0.0185, which demonstrated that the GSSR method can be used to estimate AOD accurately, and can be regarded as an alternative method to estimate AOD in the field work if no sunphotometer is available.

关键词： Atmospheric measurements Table lookup Aerosols Standards remote sensing Optical variables measurement Optical sensors

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Spatiotemporal dynamics of coastal dead zones in the Gulf of Mexico over 20 years using remote sensing

引用

Science of the Total Environment 2025年 979卷

作者： Li, Yingjie Xia, Zilong Nguyen, Lan Wan, Ho Yi Wan, Luwen Wang, Mengqiu Jia, Nan Matli, Venkata Rohith Reddy Li, Yi Seeley, Megan Moran, Emilio F. Liu, Jianguo Center for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East LansingMI48823 United States Environmental Science and Policy Program Michigan State University East LansingMI48823 United States Natural Capital Project Woods Institute for the Environment Doerr School of Sustainability Stanford University StanfordCA94305 United States Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Jiangsu Nanjing210023 China Department of Biological Sciences University of Calgary CalgaryABT2N 1N4 Canada Department of Wildlife California State Polytechnic University Humboldt ArcataCA95521 United States Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL32611 United States Department of Earth System Science Stanford University StanfordCA94305 United States Earth and Environmental Sciences Michigan State University East LansingMI48824 United States School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Earth Sciences The University of Hong Kong Hong Kong 999077 China Center for Geospatial Analytics North Carolina State University RaleighNC27607 United States College of the Environment and Ecology Xiamen University Xiamen361102 China School of Geographical Sciences and Urban Planning Arizona State University TempeAZ85281 United States Center for Global Discovery and Conservation Science Arizona State University TempeAZ85281 United States Center for Global Change and Earth Observations Michigan State University East LansingMI48824 United States Department of Geography Environment and Spatial Science

Spreading marine dead zones (or hypoxia) are threatening coastal ecosystems and affecting billions of people's livelihoods globally. However, the lack of field observations makes it challenging to estimate dead zones with spatial precision and across large scales. While satellites offer great potential for detecting environmental changes through large-scale and temporal consistent data, they have yet to be fully integrated into the spatio-temporal dynamic mapping of hypoxia. To address this limitation, we integrated satellite imagery with field observations in random forest models on the Google Earth Engine platform to characterize dead zone dynamics from 2000 to 2019. We applied the workflow to the Gulf of Mexico, which has the largest dead zones in North America. Our model explained 64 % (± 5 %) of the variance in predicting dead zones using satellite data. The analysis revealed that dead zones in the Gulf peaked in 2009 (17,699 ± 679 km2) and contracted afterward in terms of both size and persistence (% days with hypoxia). Despite this contraction, the average size between 2010 and 2019 was twice that of the coastal reduction goal (2) set by the Gulf of Mexico Hypoxia Task Force. Furthermore, dead zones occurred more frequently in the western Gulf, and nearly half of the western region experienced dead zones annually. In addition to inter-annual changes, our analysis highlighted the intra-annual dynamics of this phenomenon. Notably, dead zones expanded in June, peaking in size from mid-August to early September. The high temporal and spatial resolution of this dataset allows policymakers to develop targeted management plans and environmental policies. Our approach, which incorporates remote sensing for long-term monitoring of coastal dead zones, can be applied to worldwide monitoring initiatives when paired with local field observations. © 2025 Elsevier B.V.

关键词： Eutrophication

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