The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.
{"title":"Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map","authors":"Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singhc","doi":"10.1016/j.jag.2025.104365","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104365","url":null,"abstract":"The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"57 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990288","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}
Pub Date : 2025-01-18DOI: 10.1016/j.jag.2025.104374
Qi Li, Xingyuan Zu, Ming Zhang, Jinghua Li, Yan Feng
Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as HUTDNet, which utilizes the material type and abundance information for downstream semantic tasks. Specifically, a nonlinear underwater unmixing network is designed to extract pure underwater endmembers and their associated abundance information, which is essential in assisting the subsequent target detection task. The network also extracts underwater virtual endmembers and their abundance values to reconstruct a more realistic underwater HSI. Then, the abundance weighting module determines the abundance weighting factor by calculating the spectral distance between a priori target spectra and the estimated underwater pure endmembers, generating a weighted abundance map. Finally, due to the inherent limitations in the characterization capabilities of abundance maps and endmembers, the detection network extracts key spectral feature maps from the input underwater HSI. These feature maps serve as complementary terms, fused with the original and weighted abundance maps. Subsequently, convolutional and fully connected layers are employed to extract deeper features and generate the target detection maps. Experiments on both real and synthetic datasets demonstrate the superior performance and efficiency of the proposed method in this paper compared to other state-of-the-art methods.
{"title":"HUTDNet: A joint unmixing and target detection network for underwater hyperspectral imagery","authors":"Qi Li, Xingyuan Zu, Ming Zhang, Jinghua Li, Yan Feng","doi":"10.1016/j.jag.2025.104374","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104374","url":null,"abstract":"Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as HUTDNet, which utilizes the material type and abundance information for downstream semantic tasks. Specifically, a nonlinear underwater unmixing network is designed to extract pure underwater endmembers and their associated abundance information, which is essential in assisting the subsequent target detection task. The network also extracts underwater virtual endmembers and their abundance values to reconstruct a more realistic underwater HSI. Then, the abundance weighting module determines the abundance weighting factor by calculating the spectral distance between a priori target spectra and the estimated underwater pure endmembers, generating a weighted abundance map. Finally, due to the inherent limitations in the characterization capabilities of abundance maps and endmembers, the detection network extracts key spectral feature maps from the input underwater HSI. These feature maps serve as complementary terms, fused with the original and weighted abundance maps. Subsequently, convolutional and fully connected layers are employed to extract deeper features and generate the target detection maps. Experiments on both real and synthetic datasets demonstrate the superior performance and efficiency of the proposed method in this paper compared to other state-of-the-art methods.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"13 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990287","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}
Frequent algal blooms pose a serious threat to the marine ecosystem of the East China Sea. The Geostationary Ocean Color Imager-Ⅱ (GOCI-Ⅱ), a second-generation geostationary satellite sensor, is crucial for monitoring marine environmental dynamics. To evaluate the potential of GOCI-II for identifying and monitoring the diurnal variation of algal blooms in the East China Sea, we combined a coupled ocean–atmosphere model with the eXtreme Gradient Boosting (XGBoost) method to develop an atmospheric correction algorithm for coastal waters (XGB-CW). Validation showed that this algorithm derived remote sensing reflectance (Rrs) from GOCI-Ⅱ with higher accuracy than those provided by the National Ocean Satellite Center of South Korea (NOSC). To further evaluate GOCI-Ⅱ’s potential for algal bloom types identification, we compared three identification algorithms’ (Bloom Index (BI), Diatom Index (DI), and Rslope) results with Rrs data derived by XGB-CW. And the BI algorithm performed best in distinguishing the diatoms and dinoflagellates blooms, while Rslope was effective under high biomass conditions. The DI algorithm was good for diatoms blooms but less effective for dinoflagellates. Using Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) data, we analyzed the influence of these factors on the daily variations and characteristics of Akashiwo sanguinea (Dinoflagellate) and Chaetoceros curvisetus (Diatom). The results showed more pronounced daily variations in A. sanguinea compared to C. curvisetus. GOCI-Ⅱ, combined with accurate atmospheric correction and identification algorithms, plays a crucial role in algal bloom monitoring.
频繁的藻华对东海的海洋生态系统构成严重威胁。地球同步海洋彩色成像仪-Ⅱ（GOCI-Ⅱ）是第二代地球同步卫星传感器，对监测海洋环境动态至关重要。为了评估GOCI-II识别和监测东海藻华日变化的潜力，我们将海洋-大气耦合模型与极端梯度增强（XGBoost）方法相结合，开发了一种沿海水域大气校正算法（XGB-CW）。验证结果表明，该算法获得GOCI-Ⅱ遥感反射率（Rrs）的精度高于韩国国家海洋卫星中心（NOSC）提供的遥感反射率。为了进一步评价GOCI-Ⅱ对藻华类型识别的潜力，我们将三种识别算法（bloom Index （BI）、硅藻Index （DI）和Rslope）的结果与XGB-CW获得的Rrs数据进行了比较。BI算法在区分硅藻和鞭毛藻华方面效果最好，而Rslope算法在高生物量条件下效果最好。DI算法对硅藻华效果较好，但对鞭毛藻效果较差。利用光合有效辐射（PAR）和海温（SST）资料，分析了这些因素对赤潮赤藻（Akashiwo sanguinea, Dinoflagellate）和弯角毛藻（Chaetoceros curvisetus, Diatom）的日变化和特征的影响。结果显示，与C. curvisetus相比，A. sanguinea的每日变化更为明显。GOCI-Ⅱ结合精确的大气校正和识别算法，在藻华监测中起着至关重要的作用。
{"title":"Identifying algal bloom types and analyzing their diurnal variations using GOCI-Ⅱ data","authors":"Renhu Li, Fang Shen, Yuan Zhang, Zhaoxin Li, Songyu Chen","doi":"10.1016/j.jag.2025.104377","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104377","url":null,"abstract":"Frequent algal blooms pose a serious threat to the marine ecosystem of the East China Sea. The Geostationary Ocean Color Imager-Ⅱ (GOCI-Ⅱ), a second-generation geostationary satellite sensor, is crucial for monitoring marine environmental dynamics. To evaluate the potential of GOCI-II for identifying and monitoring the diurnal variation of algal blooms in the East China Sea, we combined a coupled ocean–atmosphere model with the eXtreme Gradient Boosting (XGBoost) method to develop an atmospheric correction algorithm for coastal waters (XGB-CW). Validation showed that this algorithm derived remote sensing reflectance (<ce:italic>R</ce:italic><ce:inf loc=\"post\">rs</ce:inf>) from GOCI-Ⅱ with higher accuracy than those provided by the National Ocean Satellite Center of South Korea (NOSC). To further evaluate GOCI-Ⅱ’s potential for algal bloom types identification, we compared three identification algorithms’ (Bloom Index (BI), Diatom Index (DI), and R<ce:inf loc=\"post\">slope</ce:inf>) results with <ce:italic>R</ce:italic><ce:inf loc=\"post\">rs</ce:inf> data derived by XGB-CW. And the BI algorithm performed best in distinguishing the diatoms and dinoflagellates blooms, while R<ce:inf loc=\"post\">slope</ce:inf> was effective under high biomass conditions. The DI algorithm was good for diatoms blooms but less effective for dinoflagellates. Using Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) data, we analyzed the influence of these factors on the daily variations and characteristics of <ce:italic>Akashiwo sanguinea</ce:italic> (Dinoflagellate) and <ce:italic>Chaetoceros curvisetus</ce:italic> (Diatom). The results showed more pronounced daily variations in <ce:italic>A. sanguinea</ce:italic> compared to <ce:italic>C. curvisetus</ce:italic>. GOCI-Ⅱ, combined with accurate atmospheric correction and identification algorithms, plays a crucial role in algal bloom monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"23 19 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990289","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}
Pub Date : 2025-01-16DOI: 10.1016/j.jag.2025.104363
Dongliang Ma, Fang Zhao, Likai Zhu, Xiaofei Li, Jine Wei, Xi Chen, Lijun Hou, Ye Li, Min Liu
The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution global DO concentration. The results derived by Oxyformer demonstrate an accelerated decline in global oceanic DO content, estimated at approximately 1045 ± 665 Tmol decade−1 from 2003 to 2020. The observed trends exhibit considerable variability across different regions and depths, with some new hotspots of recent DO change including the Equatorial Indian Ocean, the South Pacific Ocean, the North Atlantic Ocean, and the Western Coast of California. The unprecedented modeling approach provides a powerful tool to track changes in global DO contents and to facilitate the understanding of their influences on ocean ecosystems and biogeochemical processes.
{"title":"Deep learning reveals hotspots of global oceanic oxygen changes from 2003 to 2020","authors":"Dongliang Ma, Fang Zhao, Likai Zhu, Xiaofei Li, Jine Wei, Xi Chen, Lijun Hou, Ye Li, Min Liu","doi":"10.1016/j.jag.2025.104363","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104363","url":null,"abstract":"The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution global DO concentration. The results derived by Oxyformer demonstrate an accelerated decline in global oceanic DO content, estimated at approximately 1045 ± 665 Tmol decade<ce:sup loc=\"post\">−1</ce:sup> from 2003 to 2020. The observed trends exhibit considerable variability across different regions and depths, with some new hotspots of recent DO change including the Equatorial Indian Ocean, the South Pacific Ocean, the North Atlantic Ocean, and the Western Coast of California. The unprecedented modeling approach provides a powerful tool to track changes in global DO contents and to facilitate the understanding of their influences on ocean ecosystems and biogeochemical processes.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"57 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990324","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}
The application value of orthographic projection images is substantial, especially in the field of remote sensing for True Digital Orthophoto Map (TDOM) generation. Existing methods for orthographic projection image generation primarily involve geometric correction or explicit projection of photogrammetric mesh models. However, the former suffers from projection differences and stitching lines, while the latter is plagued by poor model quality and high costs. This paper presents NeRFOrtho, a new method for generating orthographic projection images from neural radiance fields at arbitrary angles. By constructing Neural Radiance Fields from multi-view images with known viewpoints and positions, the projection method is altered to render orthographic projection images on a plane where projection rays are parallel to each other. In comparison to existing orthographic projection image generation methods, this approach produces orthographic projection images devoid of projection differences and distortions, while offering superior texture details and higher precision. We also show the applicative potential of the method when rendering TDOM and the texture of building façade.
{"title":"NeRFOrtho: Orthographic Projection Images Generation based on Neural Radiance Fields","authors":"Dongdong Yue, Xinyi Liu, Yi Wan, Yongjun Zhang, Maoteng Zheng, Weiwei Fan, Jiachen Zhong","doi":"10.1016/j.jag.2025.104378","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104378","url":null,"abstract":"The application value of orthographic projection images is substantial, especially in the field of remote sensing for True Digital Orthophoto Map (TDOM) generation. Existing methods for orthographic projection image generation primarily involve geometric correction or explicit projection of photogrammetric mesh models. However, the former suffers from projection differences and stitching lines, while the latter is plagued by poor model quality and high costs. This paper presents NeRFOrtho, a new method for generating orthographic projection images from neural radiance fields at arbitrary angles. By constructing Neural Radiance Fields from multi-view images with known viewpoints and positions, the projection method is altered to render orthographic projection images on a plane where projection rays are parallel to each other. In comparison to existing orthographic projection image generation methods, this approach produces orthographic projection images devoid of projection differences and distortions, while offering superior texture details and higher precision. We also show the applicative potential of the method when rendering TDOM and the texture of building façade.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"7 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990292","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}
Pub Date : 2025-01-16DOI: 10.1016/j.jag.2025.104361
Jinpeng Li, Yuan Li, Shuhang Zhang, Yiping Chen
Farmland extraction has been a pivotal research challenge for decades in remote sensing. Breakthrough progress has been made by relevant studies due to the advanced deep learning-based techniques. However, existing methods still pay little attention to the simultaneous instance-level farmland extraction and semantic-based 3D attribute analysis, which are essential for enabling more various agricultural applications. Additionally, most bimodal methods apply simple projection to convert high-dimensional features to low-dimensional space for feature interaction, which inevitably underutilizes the advantages of bimodal learning and leads to lamentable information loss. To address this issue, we propose a novel end-to-end bimodal network, named Image-Point Cloud Embedding Network (IPCE-Net), that innovatively employs a dual-stream branch architecture to concurrently perform image-based farmland instance segmentation and point cloud-based semantic segmentation. Furthermore, by leveraging the Heterogeneous Conversion Module (HCM), the IPCE-Net effectively reconciles the modality disparities between images and point clouds and achieves stage-by-stage feature interaction during the bimodal learning process, thus achieving higher performance than unimodal learning. Experiments on two datasets show that IPCE-Net achieves superior performance in both farmland instance extraction and point cloud semantic segmentation tasks. For farmland instance extraction, the instance-level mAP and pixel-level IoU metrics reach 74.9% and 79.6%, respectively, being considerably higher than other classical image-based instance segmentation methods. For the point cloud semantic segmentation, the OA and mIoU metrics are 93.8% and 66.1%, with a remarkable improvement of at least 1.3% and 8.2%, respectively, compared with the state-of-the-art semantic segmentation approaches. Moreover, intelligent analysis based on the interconnection of IPCE-Net and GPT-4 transforms the abstract categorical information into easy-to-understand measurable information, demonstrating its great potential for practical applications in precision and smart agriculture.
{"title":"Image-point cloud embedding network for simultaneous image-based farmland instance extraction and point cloud-based semantic segmentation","authors":"Jinpeng Li, Yuan Li, Shuhang Zhang, Yiping Chen","doi":"10.1016/j.jag.2025.104361","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104361","url":null,"abstract":"Farmland extraction has been a pivotal research challenge for decades in remote sensing. Breakthrough progress has been made by relevant studies due to the advanced deep learning-based techniques. However, existing methods still pay little attention to the simultaneous instance-level farmland extraction and semantic-based 3D attribute analysis, which are essential for enabling more various agricultural applications. Additionally, most bimodal methods apply simple projection to convert high-dimensional features to low-dimensional space for feature interaction, which inevitably underutilizes the advantages of bimodal learning and leads to lamentable information loss. To address this issue, we propose a novel end-to-end bimodal network, named Image-Point Cloud Embedding Network (IPCE-Net), that innovatively employs a dual-stream branch architecture to concurrently perform image-based farmland instance segmentation and point cloud-based semantic segmentation. Furthermore, by leveraging the Heterogeneous Conversion Module (HCM), the IPCE-Net effectively reconciles the modality disparities between images and point clouds and achieves stage-by-stage feature interaction during the bimodal learning process, thus achieving higher performance than unimodal learning. Experiments on two datasets show that IPCE-Net achieves superior performance in both farmland instance extraction and point cloud semantic segmentation tasks. For farmland instance extraction, the instance-level mAP and pixel-level IoU metrics reach 74.9% and 79.6%, respectively, being considerably higher than other classical image-based instance segmentation methods. For the point cloud semantic segmentation, the OA and mIoU metrics are 93.8% and 66.1%, with a remarkable improvement of at least 1.3% and 8.2%, respectively, compared with the state-of-the-art semantic segmentation approaches. Moreover, intelligent analysis based on the interconnection of IPCE-Net and GPT-4 transforms the abstract categorical information into easy-to-understand measurable information, demonstrating its great potential for practical applications in precision and smart agriculture.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"102 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990325","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}
Pub Date : 2025-01-15DOI: 10.1016/j.jag.2025.104364
Yanan Jiang, Qiang Xu, Ran Meng, Chao Zhang, Linfeng Zheng, Zhong Lu
The Mountain Excavation and City Construction project (MECC) in Yan’an New District (YND) on the Chinese Loess Plateau is one of the largest geotechnical works globally. Ground deformation resulting from these extensive earthworks continues to evolve spatially and temporally even after construction is completed. Monitoring this deformation is crucial for understanding uneven post-construction subsidence and ensuring the structural integrity of infrastructure. This study proposes a framework for monitoring and predicting post-construction ground settlement (PCGS) using a dual-polarization Multi-temporal InSAR method (dual-pol MT-InSAR) and Self-Attention Memory Convolutional Long Short-Term Memory (SAM-ConvLSTM) model. Compared to single-polarization (single-pol) MT-InSAR methods, the dual-pol MT-InSAR approach, which utilizes both polarization channels of Sentinel-1 (S1) SAR data, achieves a 24 % increase in Permanent Scatterer (PS) density for PS-InSAR and improves average coherence while reducing coherence standard deviation for Small Baseline Subset (SBAS). The study further examines the factors contributing to uneven ground deformation, including fill and excavation activities (e.g., the thickness and geotechnical properties of loess), construction activities and surface loads, and precipitation. A consolidation settlement model is employed to simulate and assess ground settlement decay due to loess compaction. Based on this analysis, the most affected area in Qiaoergou is selected for spatiotemporal forecasting using MT-InSAR measurements and the SAM-ConvLSTM model. The results indicate that regions with significant subsidence form a characteristic funnel shape, with subsidence increasing over time and the deformation perimeter expanding outward. The model achieved an average absolute error of 1.6 mm, with the majority of errors concentrated within 5 mm.
{"title":"Remote sensing characterizing and deformation predicting of Yan'an New District’s Mountain Excavation and City Construction with dual-polarization MT-InSAR method","authors":"Yanan Jiang, Qiang Xu, Ran Meng, Chao Zhang, Linfeng Zheng, Zhong Lu","doi":"10.1016/j.jag.2025.104364","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104364","url":null,"abstract":"The Mountain Excavation and City Construction project (MECC) in Yan’an New District (YND) on the Chinese Loess Plateau is one of the largest geotechnical works globally. Ground deformation resulting from these extensive earthworks continues to evolve spatially and temporally even after construction is completed. Monitoring this deformation is crucial for understanding uneven post-construction subsidence and ensuring the structural integrity of infrastructure. This study proposes a framework for monitoring and predicting post-construction ground settlement (PCGS) using a dual-polarization Multi-temporal InSAR method (dual-pol MT-InSAR) and Self-Attention Memory Convolutional Long Short-Term Memory (SAM-ConvLSTM) model. Compared to single-polarization (single-pol) MT-InSAR methods, the dual-pol MT-InSAR approach, which utilizes both polarization channels of Sentinel-1 (S1) SAR data, achieves a 24 % increase in Permanent Scatterer (PS) density for PS-InSAR and improves average coherence while reducing coherence standard deviation for Small Baseline Subset (SBAS). The study further examines the factors contributing to uneven ground deformation, including fill and excavation activities (e.g., the thickness and geotechnical properties of loess), construction activities and surface loads, and precipitation. A consolidation settlement model is employed to simulate and assess ground settlement decay due to loess compaction. Based on this analysis, the most affected area in Qiaoergou is selected for spatiotemporal forecasting using MT-InSAR measurements and the SAM-ConvLSTM model. The results indicate that regions with significant subsidence form a characteristic funnel shape, with subsidence increasing over time and the deformation perimeter expanding outward. The model achieved an average absolute error of 1.6 mm, with the majority of errors concentrated within 5 mm.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"31 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990327","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}
Pub Date : 2025-01-15DOI: 10.1016/j.jag.2025.104356
Jingpeng Gao, Xiangyu Ji, Geng Chen, Yuhang Huang, Fang Ye
Unsupervised domain adaptation (UDA) has made great progress in cross-scene hyperspectral image (HSI) classification. Existing methods focus on aligning the distribution of source domain (SD) and target domain (TD). However, they all ignore the implicit class distribution information of TD data, which can help the model predict the class with a higher posterior probability. To solve the above issue, we propose pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification (PCDM-UDA). We transform the label correction into a zero–one programming problem and optimize it with the estimated pseudo-class distribution as a constraint. The corrected labels are used to fine-tune the network, which can integrate class distribution information into the network. The frequency domain phase view is introduced as an additional branch to extract domain stable feature. To credibly fuse the information from the prediction of the two branches, we introduce the Subjective logic and Dempster’s rule into our method. In addition, we design an adaptive style learning module to enhance the inter-class separability of the model. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods. The source code is available at https://github.com/jixiangyu0501/PCDM-UDA.
{"title":"Pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification","authors":"Jingpeng Gao, Xiangyu Ji, Geng Chen, Yuhang Huang, Fang Ye","doi":"10.1016/j.jag.2025.104356","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104356","url":null,"abstract":"Unsupervised domain adaptation (UDA) has made great progress in cross-scene hyperspectral image (HSI) classification. Existing methods focus on aligning the distribution of source domain (SD) and target domain (TD). However, they all ignore the implicit class distribution information of TD data, which can help the model predict the class with a higher posterior probability. To solve the above issue, we propose pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification (PCDM-UDA). We transform the label correction into a zero–one programming problem and optimize it with the estimated pseudo-class distribution as a constraint. The corrected labels are used to fine-tune the network, which can integrate class distribution information into the network. The frequency domain phase view is introduced as an additional branch to extract domain stable feature. To credibly fuse the information from the prediction of the two branches, we introduce the Subjective logic and Dempster’s rule into our method. In addition, we design an adaptive style learning module to enhance the inter-class separability of the model. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods. The source code is available at <ce:inter-ref xlink:href=\"https://github.com/jixiangyu0501/PCDM-UDA\" xlink:type=\"simple\">https://github.com/jixiangyu0501/PCDM-UDA</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"9 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990328","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}
Pub Date : 2025-01-14DOI: 10.1016/j.jag.2025.104370
Mengyuan Xu, Haoxuan Yang, Annan Hu, Lee Heng, Linyi Li, Ning Yao, Gang Liu
Deep learning (DL) based methods have recently made remarkable progress in remote sensing (RS) soil moisture (SM) retrieval applications. However, their purely “black box” algorithms suffer from a lack of interpretability, whereas methods based solely on physical mechanisms often underperform in complex scenarios. In this study, we attempt to use an SM downscaling approach that integrates thermal inertia (TI) theory with the DenseNet deep network algorithm. This approach provides partial interpretability of the physical mechanisms while utilizing DenseNet’s superior nonlinear learning ability and feature reuse capability for downscaling the Soil Moisture Active Passive (SMAP) satellite product, generating daily 1 km × 1 km SM. A comprehensive assessment of the downscaled results using in-situ SM acquired from 264 International Soil Moisture Network (ISMN) sites densely distributed across the continental U.S. indicated that this downscaling approach had an overall high accuracy, with an average unbiased root mean square error (ubRMSE) of 0.048 m3/m3. In addition, the downscaled SM exhibited marked improvement in spatial details over the original SMAP SM maps, providing clearer land surface features. The proposed SM downscaling approach is a valuable attempt to adopt DL methods with more practical physical meaning and more interpretability in the current RS Big Data era.
基于深度学习（DL）的方法最近在遥感（RS）土壤湿度（SM）检索应用中取得了显著进展。然而，这些纯粹的 "黑箱 "算法缺乏可解释性，而仅基于物理机制的方法在复杂场景中往往表现不佳。在本研究中，我们尝试使用一种将热惯性（TI）理论与 DenseNet 深度网络算法相结合的 SM 降尺度方法。这种方法提供了物理机制的部分可解释性，同时利用 DenseNet 优越的非线性学习能力和特征重用能力对土壤水分主动被动（SMAP）卫星产品进行降尺度，生成每日 1 km × 1 km 的土壤水分。利用密集分布在美国大陆的 264 个国际土壤水分网络（ISMN）站点获取的原位土壤水分，对降级结果进行了综合评估，结果表明这种降级方法总体精度较高，平均无偏均方根误差（ubRMSE）为 0.048 m3/m3。此外，缩小尺度后的 SM 与原始的 SMAP SM 地图相比，在空间细节方面有明显改善，提供了更清晰的地表特征。所提出的SM降尺度方法是在当前RS大数据时代采用更具实际物理意义和可解释性的DL方法的有益尝试。
{"title":"A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory","authors":"Mengyuan Xu, Haoxuan Yang, Annan Hu, Lee Heng, Linyi Li, Ning Yao, Gang Liu","doi":"10.1016/j.jag.2025.104370","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104370","url":null,"abstract":"Deep learning (DL) based methods have recently made remarkable progress in remote sensing (RS) soil moisture (SM) retrieval applications. However, their purely “black box” algorithms suffer from a lack of interpretability, whereas methods based solely on physical mechanisms often underperform in complex scenarios. In this study, we attempt to use an SM downscaling approach that integrates thermal inertia (TI) theory with the DenseNet deep network algorithm. This approach provides partial interpretability of the physical mechanisms while utilizing DenseNet’s superior nonlinear learning ability and feature reuse capability for downscaling the Soil Moisture Active Passive (SMAP) satellite product, generating daily 1 km × 1 km SM. A comprehensive assessment of the downscaled results using <ce:italic>in-situ</ce:italic> SM acquired from 264 International Soil Moisture Network (ISMN) sites densely distributed across the continental U.S. indicated that this downscaling approach had an overall high accuracy, with an average unbiased root mean square error (ubRMSE) of 0.048 m<ce:sup loc=\"post\">3</ce:sup>/m<ce:sup loc=\"post\">3</ce:sup>. In addition, the downscaled SM exhibited marked improvement in spatial details over the original SMAP SM maps, providing clearer land surface features. The proposed SM downscaling approach is a valuable attempt to adopt DL methods with more practical physical meaning and more interpretability in the current RS Big Data era.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990330","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}
Pub Date : 2025-01-14DOI: 10.1016/j.jag.2025.104357
Seyed Vahid Razavi-Termeh, Ali Pourzangbar, Abolghasem Sadeghi-Niaraki, Mário J. Franca, Soo-Mi Choi
Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. Artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify flood-prone areas. However, creating accurate flood susceptibility maps with AI remains a significant challenge. Therefore, the present work endeavors to cope with this gap and produce the most efficient flood susceptibility maps employing Categorical Boosting (CatBoost) algorithms and three system-based metaheuristic methods, including Augmented Artificial Ecosystem Optimization (AAEO), Germinal Center Optimization (GCO), and Water Circle Algorithm (WCA). We selected Jahrom County, Iran, to develop machine learning-based models as our case study. We used 13 flood conditioning geophysical factors as input parameters and flood occurrence (binary classification), derived from satellite imagery, as the output. Our results show that CatBoost-AAEO performs better in flood susceptibility mapping than the other combined models, CatBoost-WCA, CatBoost-GCO, and the basic CatBoost model, which are mentioned in descending order of performance. The partial Dependence Plots (PDP) approach is used to interpret the results of the developed algorithms, highlighting that low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers significantly impact the performance of ML models to predict flood occurrence. The findings of this research can help planners manage and prevent floods and avoid development in sensitive areas to reduce financial losses caused by floods.
{"title":"Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping","authors":"Seyed Vahid Razavi-Termeh, Ali Pourzangbar, Abolghasem Sadeghi-Niaraki, Mário J. Franca, Soo-Mi Choi","doi":"10.1016/j.jag.2025.104357","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104357","url":null,"abstract":"Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. Artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify flood-prone areas. However, creating accurate flood susceptibility maps with AI remains a significant challenge. Therefore, the present work endeavors to cope with this gap and produce the most efficient flood susceptibility maps employing Categorical Boosting (CatBoost) algorithms and three system-based metaheuristic methods, including Augmented Artificial Ecosystem Optimization (AAEO), Germinal Center Optimization (GCO), and Water Circle Algorithm (WCA). We selected Jahrom County, Iran, to develop machine learning-based models as our case study. We used 13 flood conditioning geophysical factors as input parameters and flood occurrence (binary classification), derived from satellite imagery, as the output. Our results show that CatBoost-AAEO performs better in flood susceptibility mapping than the other combined models, CatBoost-WCA, CatBoost-GCO, and the basic CatBoost model, which are mentioned in descending order of performance. The partial Dependence Plots (PDP) approach is used to interpret the results of the developed algorithms, highlighting that low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers significantly impact the performance of ML models to predict flood occurrence. The findings of this research can help planners manage and prevent floods and avoid development in sensitive areas to reduce financial losses caused by floods.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"56 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990331","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}
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