International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data","authors":"Yapeng Wu, Weiguo Yu, Yangyang Gu, Qi Zhang, Yuan Xiong, Hengbiao Zheng, Chongya Jiang, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng","doi":"10.1016/j.jag.2024.104320","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104320","url":null,"abstract":"Rapid, accurate, and nondestructive estimation of grain number per panicle (GNPP) in winter wheat is crucial to accelerate smart breeding, improve precision crop management, and ensure food security. As two (panicle number per unit ground area and GNPP) of three commonly used yield components, GNPP was much less quantified with remotely sensed data than the former through visual counting. The limited research suffered from either low accuracies with ground canopy spectra or low efficiency with proximal panicle imaging systems. No studies have been reported on estimating GNPP with unmanned aerial vehicle (UAV) imagery, underscoring its strong advantages in high-resolution and efficient monitoring. To address these issues, this study proposed a practical approach for estimating GNPP in winter wheat by integrating UAV imagery and meteorological data with <ce:italic>meta</ce:italic>-learning ensemble regression. The potential contributions of different variables were examined for understanding the improvement in the spectral estimation of GNPP, including spectral indices (SIs), the optimal canopy height (CH) metric, and absorbed photosynthetic active radiation (APAR).","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"28 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model","authors":"Shuai Yang, Rui Chen, Binbin He, Yiru Zhang","doi":"10.1016/j.jag.2024.104311","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104311","url":null,"abstract":"The Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the underutilization of Visible Infrared Imaging Radiometer Suite (VIIRS) in LFMC inversion, this study harnessed the coupled radiative transfer models (RTMs) to probe the spectral sensitivity of the VIIRS to LFMC and pinpoint the optimal band combination for LFMC inversion. To tackle the challenge of ill-posed inversion, we leveraged the correlation coefficient matrix to mitigate erroneous combinations of free parameters in the construction of the lookup table. Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R<mml:math altimg=\"si106.svg\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo></mml:mrow></mml:math> 0.57, R<mml:math altimg=\"si106.svg\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo></mml:mrow></mml:math> 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE <mml:math altimg=\"si107.svg\" display=\"inline\"><mml:mo>=</mml:mo></mml:math> 5.84%), compared to results from the MODIS. By scrutinizing LFMC trends alongside precipitation (PP) data in four forest fires spanning from 2019 to 2022 in southwest China, varied degrees of LFMC decrease preceding fire outbreaks. Those results substantiated the validity of the proposed method for wildfire warning. Consequently, our study asserts the reliability of VIIRS in LFMC inversion, positioning it as a viable substitute and extension of MODIS. VIIRS offers continuous and effective product support for wildfire warning assessment, enhancing our ability to monitor and mitigate wildfire risks.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"34 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the potential of regional cloud vertical structure climatology statistical model in estimating surface downwelling longwave radiation","authors":"Shanshan Yu, Xiaozhou Xin, Hailong Zhang, Li Li, Qinhuo Liu","doi":"10.1016/j.jag.2024.104324","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104324","url":null,"abstract":"Cloud base height (CBH) is one of the most uncertain parameters in surface downward longwave radiation (SDLR) estimation. Climatology statistical models of cloud vertical structure (CVS), which provide 1-degree grid averages or latitude zone averages of CBH and cloud thickness (CT), have been frequently applied to improve coarse-resolution SDLR estimation. This study aims to develop a regional CVS climatology statistical model containing CT and CBH statistics at a kilometer scale, using CloudSat, CALIPSO, and MODIS data, and to explore its potential in kilometer-scale CBH and SDLR estimations. The RMSE of CBH estimated from the new CVS model ranges from 0.4 to 2.6 km for different cloud types when validated using CloudSat/CALIPSO data. CBH RMSEs are 2.20 km for Terra data and 1.99 km for Aqua data when validated against ground measurements. The simple Minnis CT model greatly overestimated CBH, while the new CVS model produced much better results. Using CBH from the new CVS model, the RMSEs of estimated cloudy SDLR are 26.8 W/m<ce:sup loc=\"post\">2</ce:sup> and 29.2 W/m<ce:sup loc=\"post\">2</ce:sup> for the Gupta-SDLR and Diak-SDLR models, respectively. These results are significantly better than those from the Minnis CT model and are comparable to those from the more advanced Yang-Cheng CT model. Moreover, the RMSEs of all-sky SDLR range from 22.6 to 21.5 W/m<ce:sup loc=\"post\">2</ce:sup> with resolution from 1 km to 20 km. These findings indicate that the regional CVS model is feasible for high-resolution CBH and SDLR estimation and can be effectively combined with other CBH estimation methods. This study provides a novel approach for estimating SDLR by integrating active and passive satellite data.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"2 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised deep depth completion with heterogeneous LiDAR and RGB-D camera depth information","authors":"Guohua Gou, Han Li, Xuanhao Wang, Hao Zhang, Wei Yang, Haigang Sui","doi":"10.1016/j.jag.2024.104327","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104327","url":null,"abstract":"In this work, a depth-only completion method designed to enhance perception in light-deprived environments. We achieve this through LidarDepthNet, a novel end-to-end unsupervised learning framework that fuses heterogeneous depth information captured by two distinct depth sensors: LiDAR and RGB-D cameras. This represents the first unsupervised LiDAR-depth fusion framework for depth completion, demonstrating scalability to diverse real-world subterranean and enclosed environments. To facilitate unsupervised learning, we leverage relative rigid motion transfer (RRMT) to synthesize co-visible depth maps from temporally adjacent frames. This allows us to construct a temporal depth consistency loss, constraining the fused depth to adhere to realistic metric scale. Furthermore, we introduce measurement confidence into the heterogeneous depth fusion model, further refining the fused depth and promoting synergistic complementation between the two depth modalities. Extensive evaluation on both real-world and synthetic datasets, notably a newly proposed LiDAR-depth fusion dataset, LidarDepthSet, demonstrates the significant advantages of our method compared to existing state-of-the-art approaches.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges","authors":"Daifeng Peng, Xuelian Liu, Yongjun Zhang, Haiyan Guan, Yansheng Li, Lorenzo Bruzzone","doi":"10.1016/j.jag.2024.104282","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104282","url":null,"abstract":"Change detection (CD) aims to compare and analyze images of identical geographic areas but different dates, whereby revealing spatio-temporal change patterns of Earth’s surface. With the implementation of the High-Resolution Earth Observation Project, an integrated sky-to-ground observation system has been continuously developed and improved. The accumulation of massive multi-modal, multi-angle, and multi-resolution remote sensing data have greatly enriched the CD data sources. Among them, high-resolution optical remote sensing images contain abundant spatial detail information, making it possible to interpret fine-grained scenes and greatly expand the application breadth and depth of CD. Generally, traditional optical remote sensing CD methods are cumbersome in steps and have a low level of automation. In contrast, artificial intelligence (AI) based CD methods possess powerful feature extraction and non-linear modeling capabilities, thereby gaining advantages that traditional methods cannot match. As a result, they have become the mainstream approaches in the field of CD. This review article systematically summarizes the datasets, theories, and methods of CD for optical remote sensing image. It provides a comprehensive analysis of AI-based CD algorithms based on deep learning paradigms from the perspectives of algorithm granularity. In-depth analysis of the performance of typical algorithms are further conducted. Finally, we summarize the challenges and trends of the CD algorithms in the AI era, aiming to provide important guidelines and insights for relevant researchers.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"24 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced network for extracting tunnel lining defects using transformer encoder and aggregate decoder","authors":"Bo Guo, Zhihai Huang, Haitao Luo, Perpetual Hope Akwensi, Ruisheng Wang, Bo Huang, Tsz Nam Chan","doi":"10.1016/j.jag.2024.104259","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104259","url":null,"abstract":"The tunnel environment is characterized by insufficient ambient light, obstructed view, and complex inner lining construction conditions. These factors frequently result in limited anti-interference capability, reduced recognition accuracy, and suboptimal segmentation results for defect extraction. We propose a deep network model utilizing an encoder–decoder framework that integrates Transformer and convolution for comprehensive defect extraction. The proposed model utilizes an encoder that integrates a hierarchical Transformer backbone with an efficient attention mechanism to fully explore complete information at multi-scale granularities. In the decoder, multi-scale information is initially aggregated using a Multi-Layer Perceptron (MLP) module. Additionally, the Stacking Filters with Atrous Convolutions (SFAC) module are implemented to enhance the perception of the complete defect scope. Furthermore, a Boundary-aware Attention Module (BAM) is implemented to enhance edge information to improve the detection of defects. With this well-designed decoder, the multi-scale information from the encoder can be fully aggregated and exploited for complete defect detection. Experimental findings illustrate the effectiveness of our proposed approach in addressing tunnel lining defects within the image dataset. The outcomes reveal that our proposed network achieves an accuracy (Acc) of 94.4% and a mean intersection over union (mIoU) of 78.14%. Compared to state-of-the-art segmentation networks, our model improves the accuracy of tunnel lining defect extraction, showcasing enhanced extraction effectiveness and anti-interference capability, thus meeting the engineering requirements for defect detection in complex environments of tunnels.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"1 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change detection in heterogeneous images based on multiple pseudo-homogeneous image pairs","authors":"Huifu Zhuang, Jianlin Guo, Ming Hao, Sen Du, Kefei Zhang, Xuesong Wang","doi":"10.1016/j.jag.2024.104321","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104321","url":null,"abstract":"Due to the significant disparities in feature spaces of multi-source images, change detection (CD) of heterogeneous remote sensing images (HRSIs) remains a highly challenging problem. Currently, CD methods based on domain transfer networks (DTNs) have garnered significant attention. However, the computer scientists underutilize knowledge in the field of CD during DTNs design, and the existing CD methods do not fully utilize the heterogeneous complementary features contained in HRSIs. Therefore, this study proposes a novel CD method based on multiple pseudo-homogeneous image pairs. First, a cycle-consistent generative adversarial network with knowledge constraints (named as KCGAN) was designed for obtaining good pseudo-homogeneous images. In detail, both the domain knowledge that there are land cover changes in multi-temporal images and that the objects in an image can be described from different scales were well integrated into the design of KCGAN. Then, a multi-modal difference Siamese fusion network (named as MDSiamF) was proposed to extract change information from the multiple pseudo-homogeneous image pairs generated with KCGAN. Experiments conducted on three datasets showed that: 1) compared to existing domain transfer methods, the unchanged areas in the pseudo-homogeneous images obtained by KCGAN exhibit better feature consistency (with a peak signal-to-noise ratio higher than 20.85 and a PHash value higher than 0.9); 2) compared to state-of-the-art methods for CD of HRSIs, the proposed method shows stable and good CD performance (with an overall accuracy higher than 0.98 and a F1 Score higher than 0.78).","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"45 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential of C-band Sentinel-1 InSAR for ground surface deformation monitoring in the southern boreal forest: An investigation in the Genhe River basin","authors":"Chenqi Huang, Lingxiao Wang, Lin Zhao, Shibo Liu, Defu Zou, Guangyue Liu, Guojie Hu, Erji Du, Yao Xiao, Chong Wang, Yuxin Zhang, Yuanwei Wang, Yu Zhang, Zhibin Li","doi":"10.1016/j.jag.2024.104302","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104302","url":null,"abstract":"The boreal forest surrounds the Arctic region and is the most extensive ecosystem on Earth; one-third of its soil is influenced by permafrost and accompanying wetlands. Interferometric Synthetic Aperture Radar (InSAR) technology has been widely utilized to monitor ground surface deformation in Arctic tundra and alpine grassland permafrost environments; however, its application in boreal forest areas is limited due to dense canopy cover and severe interferometric decorrelation. This study investigates the application of C-band InSAR to ground surface deformation monitoring in a southern boreal forest environment at Genhe River watershed in the northern part of the Greater Khingan Mountains, Northeast China. The analysis revealed that freezing-season interferograms have higher interferometric qualities and are more suitable for deformation monitoring. An InSAR pair correction and stacking algorithm was developed for retrieving extensive freezing-season deformation which could maximize the use of low-quality InSAR pairs, and reduce the effects of the snow depth phase and atmospheric distortions. The retrieved multiannual freezing-season deformation ranged from –32.8 mm to 129.1 mm. The uplift regions clearly indicate the extent of low-lying wetlands, which are influenced by frost heave caused by freezing soil water. Additionally, the “subsidence” areas correspond to farmland and evergreen coniferous forest regions in the study area, where liquid water content is higher than in other land cover types, thus resulting in a longer optical path for the radar signal. This study presents the first systematic analysis of applying C-band InSAR to ground surface deformation monitoring in the southern boreal forest environment. The retrieved seasonal deformation and deformation processes have a high potential for identifying wetlands, differentiating between forest types, and providing valuable insights into the hydrothermal conditions and dynamics of the boreal ecosystem.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"22 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating structure of understory bamboo for giant panda habitat by developing an advanced vertical vegetation classification approach using UAS-LiDAR data","authors":"Xin Shen, Lin Cao, Yisheng Ma, Nicholas C. Coops, Evan R. Muise, Guibin Wang, Fuliang Cao","doi":"10.1016/j.jag.2024.104304","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104304","url":null,"abstract":"Bamboo forests are natural habitat for the giant panda which is one of the most vulnerable mammal species. In structurally complex natural forests, bamboos are normally located under the canopy of taller trees, which makes them difficult to be quantified accurately. Although Light Detection and Ranging (LiDAR) technologies have been well established as the effective tool for forest structure assessment, the use of LiDAR to assess understory bamboo in structurally complex natural forests is less well known. We present a novel vertical vegetation classification (VVC) approach to map the structure of understory bamboos for giant panda forage in natural forests. An optimized demarcation point identification (DPI) model was developed for stratifying different vertical layers from coarse to fine scales. Three-dimensional understory bamboo point clouds were successfully isolated from the forest point cloud, then bamboo structure predictive models were developed through understory bamboo point cloud metrics and applied over the entire study area to generate spatially continuous maps of understory bamboo structure. Our results indicate that the isolation of the understory bamboo point cloud using the developed VVC approach performs well and has small bias, the extracted maximum height is close to field-measured maximum height (R<ce:sup loc=\"post\">2</ce:sup> = 0.77, rRMSE = 15.02 %). Height-related metrics have higher correlations with bamboo structure (mean natural and true height, basal diameter, and total aboveground biomass) than other metrics (<ce:italic>r</ce:italic> &gt; 0.8), and understory bamboo structures are estimated with relatively high accuracy (R<ce:sup loc=\"post\">2</ce:sup> = 0.84 – 0.91, rRMSE = 10.87 – 29.41 %). We also find varying effects of topography on the spatial distribution of different understory bamboo species. This study demonstrates the benefits of utilizing LiDAR data to ascertain fine-scale understory bamboo resources, providing critical supports for giant panda habitat assessment and conservation.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"43 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework","authors":"Ziyin Zeng, Qingyong Hu, Zhong Xie, Bijun Li, Jian Zhou, Yongyang Xu","doi":"10.1016/j.jag.2024.104309","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104309","url":null,"abstract":"We investigate the problem of 3D point clouds semantic segmentation. Recently, a large amount of research work has focused on local feature aggregation. However, the foundational framework of semantic segmentation of 3D point clouds has been neglected, where the majority of current methods default to the U-Net framework. In this study, we present U-Next, a small but mighty framework designed specifically for point cloud semantic segmentation. The key innovation of this framework is to capture multi-scale hierarchical features. Specifically, we construct the U-Next by stacking multiple U-Net <mml:math altimg=\"si10.svg\" display=\"inline\"><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> sub-networks in a dense arrangement to diminish the semantic gap. Concurrently, it integrates feature maps across various scales to proficiently restore intricate fine-grained details. Additionally, a multi-level deep supervision mechanism is introduced for smoothing gradient propagation and facilitating network optimization. We conduct extensive experiments on benchmarks, including the indoor S3DIS dataset, the LiDAR-based outdoor Toronto3D dataset, and the urban-scale photogrammetry-based SensatUrban dataset, demonstrate the superiority of U-Next. The U-Next framework consistently exhibits significant performance enhancements across various benchmarks and baselines, demonstrating its considerable potential as a versatile point-based framework for future endeavors. The code has been released at <ce:inter-ref xlink:href=\"https://github.com/zeng-ziyin/U-Next\" xlink:type=\"simple\">https://github.com/zeng-ziyin/U-Next</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"252 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}