ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Classification of urban road functional structure by integrating physical and behavioral features","authors":"Qiwen Huang ,&nbsp;Haifu Cui ,&nbsp;Longwei Xiang","doi":"10.1016/j.isprsjprs.2025.01.018","DOIUrl":"10.1016/j.isprsjprs.2025.01.018","url":null,"abstract":"<div><div>Multisource data can extract diverse urban functional features, facilitating a deeper understanding of the functional structure of road networks. Street view images and taxi trajectories, as forms of urban geographic big data, capture features of the urban physical environment and travel behavior, serving as effective data sources for identifying the functional structure of urban spaces. However, street view and taxi trajectory data often suffer from sparse and uneven distributions, and the differences between features are relatively small in the process of multiple feature fusion, which poses significant challenges to accurate classification of road functions. To address these issues, this study proposes the use of the Louvain algorithm and triplet loss methods to enhance features at the community level, resolving the sparse data distribution problem. Simultaneously, the attention mechanism of the graph attention network is applied to dynamically adjust the feature weights within the road network, capturing subtle differences between different features. The experimental results demonstrate that the effectiveness of feature enhancement and capturing differences has improved the accuracy of calculating complex urban road functional structures. Additionally, this study analyzes the degree of mixing and distribution of road functions and explores the relationship between the road functional structure and traffic. The work in this paper assesses urban functional structure at the street level and provides decision-making support for urban planning at a fine scale.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 753-769"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072524","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":"ChangeRD: A registration-integrated change detection framework for unaligned remote sensing images","authors":"Wei Jing ,&nbsp;Kaichen Chi ,&nbsp;Qiang Li ,&nbsp;Qi Wang","doi":"10.1016/j.isprsjprs.2024.11.019","DOIUrl":"10.1016/j.isprsjprs.2024.11.019","url":null,"abstract":"<div><div>Change Detection (CD) is important for natural disaster assessment, urban construction management, ecological monitoring, etc. Nevertheless, the CD models based on the pixel-level classification are highly dependent on the registration accuracy of bi-temporal images. Besides, differences in factors such as imaging sensors and season often result in pseudo-changes in CD maps. To tackle these challenges, we establish a registration-integrated change detection framework called ChangeRD, which can explore spatial transformation relationships between pairs of unaligned images. Specifically, ChangeRD is designed as a multitask network that supervises the learning of the perspective transformation matrix and difference regions between images. The proposed Adaptive Perspective Transformation (APT) module is utilized to enhance spatial consistency of features from different levels of the Siamese network. Furthermore, an Attention-guided Central Difference Convolution (AgCDC) module is proposed to mine the deep differences in bi-temporal features, significantly reducing the pseudo-change noise caused by illumination variations. Extensive experiments on unaligned bi-temporal images have demonstrated that ChangeRD outperforms other SOTA CD methods in terms of qualitative and quantitative evaluation. The code for this work will be available on GitHub.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 64-74"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823152","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":"Detection of non-stand replacing disturbances (NSR) using Harmonized Landsat-Sentinel-2 time series","authors":"Madison S. Brown ,&nbsp;Nicholas C. Coops ,&nbsp;Christopher Mulverhill ,&nbsp;Alexis Achim","doi":"10.1016/j.isprsjprs.2024.12.014","DOIUrl":"10.1016/j.isprsjprs.2024.12.014","url":null,"abstract":"<div><div>Non-stand replacing disturbances (NSRs) are events that do not result in complete removal of trees and generally occur at a low intensity over an extended period of time (e.g., insect infestation), or at spatially variable intensities over short time intervals (e.g., windthrow). These disturbances alter the quality and quantity of forest biomass, impacting timber supply and ecosystem services, making them critical to monitor over space and time. The increased accessibility of high frequency revisit, moderate spatial resolution satellite imagery, has led to a subsequent increase in algorithms designed to detect sub-annual change in forested landscapes across broad spatial scales. One such algorithm, the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) has shown promise with sub-annual change detection in temperate forested environments. Here, we evaluate the sensitivity of BEAST to detect NSRs across a range of severity levels and disturbance agents in Central British Columbia (BC), Canada. Moderate resolution satellite time series data were utilized by BEAST to produce rasters of change probability, which were compared to the occurrence, severity, and timing of disturbances as mapped by the annual British Columbia Aerial Overview Survey (BC AOS). Differences in the distributions of BEAST probabilities between agents and levels of severity were then compared to undisturbed pixels. In order to determine the applicability of the algorithm for updating forest inventories, BEAST probability distributions of major NSRs (&gt; 5 % of total AOS disturbed area) were compared between consecutive years of disturbances. Cumulatively, all levels of disturbances had higher and statistically significant (p &lt; 0.05) mean BEAST change probabilities compared with historically undisturbed areas. Additionally, 16 disturbance agents observed in the area had higher statistically significant (p &lt; 0.05) probabilities. All major NSRs showed an upwards and statistically significant (p &lt; 0.05) progression of BEAST probabilities over time corresponding to increases in BC AOS mapped area. The sensitivity of BEAST change probabilities to a wide range of NSR disturbance agents at varying intensities suggests promising opportunities for earlier detection of NSRs to inform continuously updating forest inventories and potentially inform adaptation and mitigation actions.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 264-276"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative networks for spatio-temporal gap filling of Sentinel-2 reflectances","authors":"Maria Gonzalez-Calabuig,&nbsp;Miguel-Ángel Fernández-Torres,&nbsp;Gustau Camps-Valls","doi":"10.1016/j.isprsjprs.2025.01.016","DOIUrl":"10.1016/j.isprsjprs.2025.01.016","url":null,"abstract":"<div><div>Earth observation from satellite sensors offers the possibility to monitor natural ecosystems by deriving spatially explicit and temporally resolved biogeophysical parameters. Optical remote sensing, however, suffers from missing data mainly due to the presence of clouds, sensor malfunctioning, and atmospheric conditions. This study proposes a novel deep learning architecture to address gap filling of satellite reflectances, more precisely the visible and near-infrared bands, and illustrates its performance at high-resolution Sentinel-2 data. We introduce GANFilling, a generative adversarial network capable of sequence-to-sequence translation, which comprises convolutional long short-term memory layers to effectively exploit complete dependencies in space–time series data. We focus on Europe and evaluate the method’s performance <em>quantitatively</em> (through distortion and perceptual metrics) and <em>qualitatively</em> (via visual inspection and visual quality metrics). Quantitatively, our model offers the best trade-off between denoising corrupted data and preserving noise-free information, underscoring the importance of considering multiple metrics jointly when assessing gap filling tasks. Qualitatively, it successfully deals with various noise sources, such as clouds and missing data, constituting a robust solution to multiple scenarios and settings. We also illustrate and quantify the quality of the generated product in the relevant downstream application of vegetation greenness forecasting, where using GANFilling enhances forecasting in approximately 70% of the considered regions in Europe. This research contributes to underlining the utility of deep learning for Earth observation data, which allows for improved spatially and temporally resolved monitoring of the Earth surface.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 637-648"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035306","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":"Nothing Stands Still: A spatiotemporal benchmark on 3D point cloud registration under large geometric and temporal change","authors":"Tao Sun ,&nbsp;Yan Hao ,&nbsp;Shengyu Huang ,&nbsp;Silvio Savarese ,&nbsp;Konrad Schindler ,&nbsp;Marc Pollefeys ,&nbsp;Iro Armeni","doi":"10.1016/j.isprsjprs.2025.01.010","DOIUrl":"10.1016/j.isprsjprs.2025.01.010","url":null,"abstract":"<div><div>Building 3D geometric maps of man-made spaces is a well-established and active field that is fundamental to numerous computer vision and robotics applications. However, considering the continuously evolving nature of built environments, it is essential to question the capabilities of current mapping efforts in handling temporal changes. In addition to the above, the ability to create spatiotemporal maps holds significant potential for achieving sustainability and circularity goals. Existing mapping approaches focus on small changes, such as object relocation within common living spaces or self-driving car operation in outdoor spaces; all cases where the main structure of the scene remains fixed. Consequently, these approaches fail to address more radical change in the structure of the built environment, such as on the geometry and topology of it. To promote advancements on this front, we introduce the <strong><strong>Nothing Stands Still</strong> (<strong>NSS</strong>)</strong> benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map. Specifically, the benchmark involves registering within the same coordinate system two or more partial 3D point clouds (fragments) originating from the same scene but captured from different spatiotemporal views. In addition to the standard task of <em>pairwise</em> registration, we assess <em>multi-way</em> registration of multiple fragments that belong to the same indoor environment and any temporal stage. As part of <strong>NSS</strong>, we introduce a dataset of 3D point clouds recurrently captured in large-scale building indoor environments that are under construction or renovation. The <strong>NSS</strong> benchmark presents three scenarios of increasing difficulty, with the goal to quantify the generalization ability of point cloud registration methods over space (within one building and across buildings) and time. We conduct extensive evaluations of state-of-the-art methods on <strong>NSS</strong> over all tasks and scenarios. The results demonstrate the necessity for novel methods specifically designed to handle large spatiotemporal changes. The homepage of our benchmark is at <span><span>http://nothing-stands-still.com</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 799-823"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072521","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":"Near-surface air temperature estimation for areas with sparse observations based on transfer learning","authors":"Wei Wang ,&nbsp;Stefan Brönnimann ,&nbsp;Ji Zhou ,&nbsp;Shaopeng Li ,&nbsp;Ziwei Wang","doi":"10.1016/j.isprsjprs.2025.01.021","DOIUrl":"10.1016/j.isprsjprs.2025.01.021","url":null,"abstract":"<div><div>Near-surface air temperature (NSAT) data is essential for climate analysis and applied research in areas with sparse ground-based observations. In recent years, machine learning (ML) techniques have been increasingly used to estimate NSAT, delivering promising results. However, in regions with limited observational samples, machine learning-based NSAT estimations may encounter challenges, potentially resulting in reduced accuracy. Therefore, this study introduces a novel model – TranSAT. TranSAT utilizes a transfer learning (TL) framework, deep neural network (DNN) and U-shape convolutional network (U-Net) to enhance the accuracy of NSAT estimations in regions with sparse observational data. Considering the scarcity of observation stations in certain regions, the Tibetan Plateau within the China's landmass (CNTP) is selected as the study region. The majority of observational stations are concentrated in the eastern and southeastern parts of CNTP, with a significant lack of stations in the northern and western regions. The scarce observations in the CNTP affect NSAT estimation accuracy in recent studies, thus limiting practical applications. The estimated NSAT (i.e., TranSAT NSAT) by TranSAT is evaluated by measurements of 10 independent meteorological stations from the Meteorological network in China's cold region (MSC). Evaluation results indicate an average coefficient of determination (<em>R</em>²) of 0.92 and a root mean squared error (RMSE) of 2.29 °C. The TranSAT NSAT exhibits an overall decrease of at least 7 % on RMSE compared to existing NSAT datasets, with a more significant enhancement of over 40 % in regions with sparse ground observations. These results highlight the good and consistent performance of TranSAT NSAT, further confirming that the proposed TranSAT model effectively improves NSAT estimation in areas with limited observational data.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 712-727"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035287","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":"Cross-view geolocalization and disaster mapping with street-view and VHR satellite imagery: A case study of Hurricane IAN","authors":"Hao Li ,&nbsp;Fabian Deuser ,&nbsp;Wenping Yin ,&nbsp;Xuanshu Luo ,&nbsp;Paul Walther ,&nbsp;Gengchen Mai ,&nbsp;Wei Huang ,&nbsp;Martin Werner","doi":"10.1016/j.isprsjprs.2025.01.003","DOIUrl":"10.1016/j.isprsjprs.2025.01.003","url":null,"abstract":"<div><div>Nature disasters play a key role in shaping human-urban infrastructure interactions. Effective and efficient response to natural disasters is essential for building resilience and sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about the disaster damage perception, which shows how badly people think that urban infrastructure has been damaged. The second information is geolocation awareness, which means how people’s whereabouts are made available. In this paper, we proposed a novel disaster mapping framework, namely CVDisaster, aiming at simultaneously addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery. CVDisaster consists of two cross-view models, where CVDisaster-Geoloc refers to a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder and CVDisaster-Est is a cross-view classification model based on a Coupled Global Context Vision Transformer (CGCViT). Taking Hurricane IAN as a case study, we evaluate the CVDisaster framework by creating a novel cross-view dataset (CVIAN) and conducting extensive experiments. As a result, we show that CVDisaster can achieve highly competitive performance (over 80% for geolocalization and 75% for damage perception estimation) with even limited fine-tuning efforts, which largely motivates future cross-view models and applications within a broader GeoAI research community. The data and code are publicly available at: <span><span>https://github.com/tum-bgd/CVDisaster</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 841-854"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RF-DET: Refocusing on the small-scale objects using aggregated context for accurate power transmitting components detection on UAV oblique imagery","authors":"Zhengfei Yan ,&nbsp;Chi Chen ,&nbsp;Shaolong Wu ,&nbsp;Zhiye Wang ,&nbsp;Liuchun Li ,&nbsp;Shangzhe Sun ,&nbsp;Bisheng Yang ,&nbsp;Jing Fu","doi":"10.1016/j.isprsjprs.2025.01.005","DOIUrl":"10.1016/j.isprsjprs.2025.01.005","url":null,"abstract":"<div><div>In transmission lines, regular inspections are crucial for maintaining their safe operation. Automatic and accurate detection of power transmission facility components (power components) in inspection imagery is an effective way to monitor the status of electrical assets within the Right of Ways (RoWs). However, the multitude of small-scale objects (e.g. grading rings, vibration dampers) in inspection imagery poses enormous challenges. To address these challenges, we propose a coarse-to-fine object detector named RF-DET. It adopts a Refocus Framework to refine the detection accuracy of small objects within the Regions of Interest of the Power Components (P-RoIs) generated through explicit context. On the basis above, an Implicit Context Aggregation Attention Module (ICAM) is proposed. ICAM utilizes a multi-branch structure to capture and aggregate multi-directional positional and global information, enabling in-depth mining of the implicit context among small objects. To verify the performance of this detector, a benchmark dataset named DOPI-UAV is constructed, comprising 4,438 UAV oblique images and 54,591 instances, encompassing six common categories of power components and one category of defect. Experimental results show that RF-DET achieves mAP of 62.7%, 55.7%, 84.6%, and 52.8% on the DOPI-UAV, Tower, CPLID, and InsD datasets, respectively. Compared to the state-of-the-art method, such as YOLOv9, RF-DET attains significant performance improvements, with increases of 5.2% in mAP and 6.4% in mAP₅₀, respectively. Especially, the AP_S shows an improvement of 8.3%. The datasets and codes are available at <span><span>https://github.com/DCSI2022/RF-DET</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 692-711"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161509","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":"3D automatic detection and correction for phase unwrapping errors in time series SAR interferometry","authors":"Ying Liu ,&nbsp;Hong’an Wu ,&nbsp;Yonghong Zhang ,&nbsp;Zhong Lu ,&nbsp;Yonghui Kang ,&nbsp;Jujie Wei","doi":"10.1016/j.isprsjprs.2024.12.013","DOIUrl":"10.1016/j.isprsjprs.2024.12.013","url":null,"abstract":"<div><div>Phase unwrapping (PhU) is one of the most critical steps in synthetic aperture radar interferometry (InSAR) technology. However, the current phase unwrapping methods cannot completely avoid the PhU errors, particularly in complex environments with low coherence. Here, we show that the PhU errors can be corrected well with the time series interferograms. We propose a three-dimensional automatic detection and correction (3D-ADAC) method based on phase closure for time-series InSAR PhU errors to improve the quality of the interferograms, especially for the regions with the same errors in different interferograms which cancel each other out in phase closure. The 3D-ADAC algorithm was evaluated with 26 Sentinel-1 SAR images and 72 phase closure loops over the Tianjin region, China, and compared with the popular MintPy and CorPhU methods. Our results demonstrate that the number of new arcs with model coherence coefficient greater than 0.7 achieved by the proposed method is 2.36 times that by the method used in the MintPy software and 3.07 times that by the CorPhU method. The corrected and improved interferograms will be helpful for accurately mapping the ground deformations or Earth topographies via InSAR technology. Codes and data are available at https://github.com/Lylionaurora/code3d-ADCD.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 232-245"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874572","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":"Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset","authors":"Huiqun Ren ,&nbsp;Xin Huang ,&nbsp;Jie Yang ,&nbsp;Guoqing Zhou","doi":"10.1016/j.isprsjprs.2024.12.023","DOIUrl":"10.1016/j.isprsjprs.2024.12.023","url":null,"abstract":"<div><div>Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (&lt;30 m) over long time spans (&gt;30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global long-term ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new outperformed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISA-new can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 354-376"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925284","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}