Search Results (1,993)

Wide-area monitoring and accurate assessment of potential geohazards (PGHs) based on remote sensing will provide a crucial foundation for geohazard prevention and mitigation. Current remote sensing methods for PGH identification and evaluation require extensive manual effort and lack intelligence throughout the process. To effectively integrate multisource remote sensing data, we propose an automated method for identifying and assessing PGHs across a wide area. This approach integrates InSAR deformation, high-resolution optical remote sensing, terrain, and vector data of ground features to enable automated delineation of unstable zones, automatic identification of potentially threatened objects (PTOs), automatic screening of PGHs, and risk assessment. The proposed method is tested in the Hequ–Baode–Pianguan (HBP) region of Shanxi province. Using the DS-InSAR technique, we process 94 Sentinel-1 SAR images covering the HBP region from 2020 to 2024 to estimate surface stability. We automatically detect the boundaries of 161 active deformation areas (ADAs) in HBP. A deep learning model based on DeepLabV3+ processes optical remote sensing images of the study area at 0.5 m resolution to automatically identify all PTOs. By integrating terrain data and spatial relationships among PTOs and ADAs, we develop an algorithmic model to identify 90 PGHs and classify them into external-threat, internal-threat, and internal-external-threat geohazard zones. Finally, a risk matrix is created for an automatic geohazard risk assessment, producing results for all PGHs in the study area. This developed method will support wide-area screening and prioritization of potential geohazards on the Loess Plateau and improve PGH investigation capabilities. Full article

The identification of visible submerged hazards, including shallow-water shipwrecks and associated debris, is important for maritime safety, coastal management, environmental monitoring, and marine archeology. The scope is restricted to wrecks that remain optically visible from above in shallow or intertidal water. This study presents Submerged Hazard Identification and Processing using Augmented Image-based Detection (SHIP-AID), a modular GeoAI evaluation framework for high-resolution RGB imagery. Following site-level quality control, the independent source dataset contains 695 images from 403 wreck sites. The dedicated group-disjoint holdout contains 150 images from 88 sites and 184 annotated wreck objects. Five detector backbones and one task-aware underwater-enhancement baseline were evaluated using ten matched training seeds. Under the standard benchmark evaluation protocol, in which precision and recall are reported at the internally determined maximum-F1 point of the confidence sweep, the best configuration achieved precision 0.896, recall 0.861, mAP@50 0.927, and mAP@50–95 0.668 on the locked holdout. At the fixed, validation-selected operating threshold of 0.45, the primary detector produced threshold-specific precision 0.922 and recall 0.837. Site-clustered bootstrap intervals were 0.895–0.951 for mAP@50 and 0.625–0.704 for mAP@50–95. Physically informed attenuation and backscatter augmentation improved stricter-IoU performance relative to generic augmentation, whereas global Otsu thresholding reduced recall and localization accuracy. Performance remained stable under mild degradation, declined under moderate and strong degradation, and became unreliable under severe low visibility. SHIP-AID is therefore positioned as a decision-support framework for prioritizing optically visible shallow-water sites, with sonar, diving, hydrographic, or archeological evidence retained as the confirmation standard. Full article

Optical coherence tomography (OCT) is a high-resolution and non-contact optical imaging and sensing modality that provides depth-resolved cross-sectional visualization of retinal microstructures. It plays an important role in the assessment of retinal diseases, including age-related macular degeneration (AMD) and diabetic macular edema (DME). However, automated OCT image classification commonly relies on fully supervised models that require large-scale expert annotations, which are costly and time-consuming because of the complex layered anatomy and subtle pathological patterns present in retinal OCT images. To reduce annotation dependence, this study proposes a self-supervised representation learning framework with local dimensionality regularization for retinal OCT image classification. The proposed method estimates the local intrinsic dimensionality of learned representations and incorporates it into an asymptotic Fisher-Rao regularization objective to mitigate local dimensional degeneration and preserve fine-grained structural information. Logarithmic scaling and geometric averaging are further introduced to reduce sensitivity to outliers and improve optimization stability. Experiments on three independent OCT datasets achieved classification accuracies of 94.35%, 92.48%, and 92.56%, respectively, demonstrating competitive performance compared with mainstream self-supervised methods. These results demonstrate that explicitly modeling local feature geometry can improve the discrimination of sensor-acquired OCT images while reducing reliance on manual annotations, providing an effective approach for intelligent analysis of biomedical optical imaging data. Full article

The accurate evaluation of gastrointestinal (GI) diseases such as celiac disease (CeD) relies on the assessment of villous architecture, yet progress in imaging-based diagnostics, particularly video capsule endoscopy (VCE), is constrained by the absence of anatomically realistic and reproducible physical models of the intestinal mucosa. Existing benchtop phantoms typically reproduce gross luminal curvature but fail to capture the sub-millimeter villous microstructure, the optical scattering behavior, and the luminal folding of native mucosa that together shape its endoscopic appearance. We developed a modular fabrication framework for an anatomically realistic small intestinal phantom with controlled villous microstructure. High-resolution drop-on-demand photopolymer material jetting was used to print discrete patches of villous-like micropillar arrays with tunable height, diameter, and spacing parameterized from histological data spanning Marsh 0 to 3c classifications. The printed patches were then dyed for mucosal-color realism, bonded onto a polyester–spandex substrate, rolled into a continuous tube, and shaped with adjustable retainer rings to introduce luminal folds. Optical microscopy confirmed dimensional fidelity within ±10% of design values with patch-to-patch variation below 7%, and VCE imaging of healthy and atrophic configurations achieved structural similarity (SSIM) values of 0.625 and 0.761 against clinical mucosal imagery. This reproducible platform supports VCE device validation, imaging dataset generation, and clinician training in gastrointestinal imaging. Full article

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

Background and Clinical Significance: This paper presents a case of Vogt–Koyanagi–Harada (VKH)-like disease following nivolumab and ipilimumab therapy for squamous cell carcinoma of the lung, complicated by transient type 1 macular neovascularisation (MNV). Case Presentation: A 67-year-old man presented with reduced visual acuity, more pronounced in the left eye, accompanied by headache and neurosensory hearing loss for 10 days. He had been receiving combination therapy with nivolumab and ipilimumab for approximately nine weeks. Slit-lamp examination and multimodal imaging revealed multiple serous retinal detachments, choroidal folds, and bacillary layer detachment. A bilateral VKH-like syndrome was considered the most likely diagnosis, consistent with an immune-related adverse event (irAE). High-dose systemic corticosteroids were initiated, resulting in marked anatomical improvement and recovery of visual acuity. Following multidisciplinary discussion with the patient’s oncologist, ipilimumab was permanently discontinued and nivolumab was rechallenged in combination with chemotherapy after resolution of the ocular adverse events, given the progression of the underlying malignancy. Notably, optical coherence tomography angiography (OCTA) additionally demonstrated a type 1 non-exudative MNV, which resolved spontaneously during follow-up. Conclusions: Nivolumab and ipilimumab, targeting PD-1 and CTLA-4, respectively, are effective anticancer therapies but may induce immune-related adverse events involving the eye. VKH-like disease is a rare but potentially vision-threatening complication. Early recognition and prompt treatment are essential for favourable visual outcomes. In summary, this is a rare case of VKH-like disease associated with nivolumab and ipilimumab therapy, complicated by transient inflammatory type 1 MNV. Clear guidelines are needed regarding management of ocular immune-related adverse events and decisions on continuation or discontinuation of life-prolonging immunotherapy. Full article

Environmental monitoring of freshwater ecosystems faces significant challenges in detecting illicit wastewater discharges, which often remain unrecognized due to their intermittent nature and limited spatial footprint. This study presents a novel integrated strategy combining satellite remote sensing, Unmanned Aerial Vehicle (UAV)-based proximal sensing, and in situ measurements to enhance pollution detection in vulnerable aquatic environments. The methodology was applied to Lake Avernus (Italy), a volcanic lake historically affected by eutrophication and toxic cyanobacterial blooms. Landsat 8–9 thermal analysis revealed no detectable anomalies, reflecting the limitations of its coarse spatial resolution. Sentinel-2 multispectral imagery was then analyzed through spectral indices, band ratios, and reflectance signatures, revealing localized variations in surface reflectance and spatial heterogeneity in water optical properties. These satellite-derived anomalies guided targeted high-resolution UAV surveys. UAV-based thermal imaging revealed an elevated-temperature zone along the adjacent shoreline. In situ field screening flagged a candidate chemical anomaly at this location. The hierarchical framework demonstrates that satellite screening effectively identifies areas of concern, while UAV thermal imaging enables high-resolution localization of features invisible to satellite sensors, and in situ measurements provide essential ground-truth validation. This replicable, low-cost methodology offers a powerful tool for early warning, surveillance, and sustainable management of sensitive freshwater ecosystems. Full article

Nd laser posterior capsulotomy is the standard treatment for posterior capsule opacification following cataract surgery. Although generally considered safe, inadvertent laser impacts on the intraocular lens (IOL) optic may induce permanent surface defects that contribute to postoperative dysphotopsias and reduced visual quality. This experimental study investigated the optical consequences of Nd laser-induced damage on two commercially available hydrophobic acrylic IOLs, focusing on retinal light distribution and optical image quality. Two hydrophobic acrylic monofocal IOL models, the CT LUCIA (Carl Zeiss Meditec) and the AcrySof IQ (Alcon), were mounted on a customized experimental holder and exposed to standardized Nd laser applications consisting of 5, 10, or 15 laser shots. Laser interactions were documented using the PhysioGo.Lite laser platform combined with infrared thermal imaging. Untreated IOLs served as controls. Optical performance was subsequently evaluated using a standardized optical bench according to ISO recommendations. Point spread function (PSF) and modulation transfer function (MTF) analyses were performed to quantify retinal image quality, light scattering, and optical degradation. Retinal light distribution was assessed using a high-resolution projection screen simulating the retinal image. Laser exposure produced permanent focal defects on the anterior optical surface of both IOL models, resulting in measurable optical degradation. Even the lowest laser exposure (five shots) generated detectable alterations in light propagation, characterized by increased peripheral light scattering, enlargement of the PSF halo, reduced central peak intensity, and irregular light distribution across the simulated retinal plane. Descriptively, increasing numbers of laser impacts were associated with more pronounced optical disturbances, particularly in the AcrySof IQ samples. MTF analysis demonstrated a reduction in optical performance across multiple spatial frequencies, indicating deterioration of image contrast and resolving power. Although both hydrophobic acrylic IOL models exhibited optical alterations after laser exposure, descriptive differences in the magnitude and distribution of light scatter suggested a possible influence of material composition, refractive index, and surface microarchitecture. These observations should be considered preliminary because of the limited sample size and absence of inferential statistical analysis. Under the present experimental conditions, Nd:YAG laser-induced pitting was associated with measurable structural and optical alterations in two hydrophobic acrylic IOL models. Surface defects alter retinal light distribution, increase forward light scatter, and reduce optical quality, providing a possible optical mechanism that may contribute to postoperative dysphotopsias, although clinical visual symptoms were not directly evaluated in this study. These findings highlight the importance of meticulous laser focusing on the posterior capsule to minimize inadvertent IOL damage and preserve postoperative visual quality. Further investigations combining optical bench analyses with patient-reported visual outcomes are warranted to better define the clinical significance of laser-induced IOL pitting. Full article

This mini-review is intended to show how multimodal imaging could be developed from prototypes and proofs of concept by controlling the nanoscale for improved resolution of life science imaging for broad applications such as bioassays, early diagnoses and further applications. It intends to afford the presentation of multimodal approaches for imaging and bioimaging uses with potential applications to biological media. The application of multimodal nanoemitters provides enhanced bioimaging through the generation of various targeted and well-defined signals. Radionuclides and luminescent emitters were considered in the discussion for improved and enhanced signaling. In this manner, we intended to show the increase in the power of information by collecting varied optical signal–matter interactions. This could be important for Positron Emission Tomography and Computed Tomography (PET-CT), Fluorescence Tomography (FT), and other new modes of imaging contemplating the incorporation of nanotechnology. In the context of the design of new multimodal energy modes, key examples were shown from the literature, where the interactions of different energy modes could lead to enhanced and improved signaling. Electromagnetic fields and nanoplasmonics are involved in these different energy modes involving varied quantum particle interactions with modified properties. In this regard, multimodal imaging has experienced developments in nanoemitters and nanobiolabeling to track biomolecular events and targeted cells. A large quantity of research output actually focuses on nano- and quantum emissions. However, there are not as many studies dealing with enhanced emissions or laser emissions coupled with radionuclide emitters. A non-classical form of light emission, considering varied luminescent phenomena as well as further quantum signaling, showed interesting and high-impact perspectives when combined with nuclear emissions. This is the case for current trends focusing on innovative single-cell analysis. For example, the characterization and diagnosis of cells where the targeting of antibody–antigen interactions is required, providing light and energy from different sources to produce different details and imaging resolutions, has been noted. These are potential approaches that could be developed through various strategies targeting life science applications. In this regard, this article puts forward a discussion focused on nanotechnology contemplating radio-pharmacy and enhanced nanoemitters. Full article

Frame-based RGB image sensors and event-based vision sensors provide complementary sensing capabilities for human-perceptual imaging and machine vision. RGB image sensors capture dense spatial, color, and texture information that is essential for human-viewable imaging, semantic recognition, and conventional image signal processing pipelines. In contrast, dynamic vision sensors (DVSs) and event vision sensors (EVSs) asynchronously detect local brightness changes and provide sparse temporal information with low latency, high temporal resolution, and reduced redundant data output. Because neither modality alone satisfies all requirements of emerging vision systems, hybrid event–frame sensing has become an important direction for compact, low-latency, and energy-efficient sensing. This review presents a sensor-oriented taxonomy of hybrid event–frame sensing architectures and systems, including dual-camera event–frame systems, optically aligned event–frame systems, pixel-level shared hybrid image sensors, stacked CIS–DVS hybrid image sensors, homogeneous-pixel sensing systems, and event-only reconstruction systems. We analyze key sensor specifications, including latency, spatial resolution, color fidelity, power consumption, and form factor, and discuss how these specifications guide sensor configuration and design. The review identifies stacked CIS–DVS sensors as one of the most balanced and competitive architectures because they can support compact integration, synchronized event–frame sensing, and on-chip processing. However, important challenges remain, including color fidelity, demosaicing, event-pixel ratio optimization, calibration, benchmarking, and edge-AI deployment. Finally, we emphasize that future hybrid event–frame sensing systems should be developed through sensor–algorithm–ISP–AI co-design. This review provides practical guidelines for developing next-generation hybrid event–frame sensing systems for both human-perceptual imaging and machine vision. Full article

The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km² of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments. Full article

Digital elevation models (DEMs) and vegetation cover (FVC) are critical foundational data for ecological and environmental monitoring and land-use management; however, the application of domestically produced commercial high-resolution optical satellite stereo imagery for high-precision DEM construction and vegetation parameter inversion in areas with complex topography still requires further exploration. This study focuses on the hilly area in the northeastern part of Kunming City, Yunnan Province, and utilizes tri-view stereo imagery from the Beijing-3 (BJ3-N2) satellite to conduct research on DEM reconstruction, topographic correction, image fusion, and vegetation cover inversion. DEM products were generated by matching multiple sets of panchromatic and multispectral stereo imagery, and their accuracy was verified using ICESat-2 ATL08 laser altimetry data. The results indicate that DEMs generated from panchromatic imagery exhibit higher accuracy than those from multispectral imagery; among them, the PAN-0103 DEM demonstrated the best overall performance, with a global RMSE of 3.42 m. Topographic slope and land cover type were found to have significant effects on DEM accuracy. A terrain-constrained Gram–Schmidt fusion model was constructed based on the optimal DEM to generate the fused image MP-01, which had a spectral angle (SAM) of 3.2° and a band correlation coefficient (CC) of 0.92. Further, by integrating field RTK plot data, an improved pixel binary model was constructed to perform FVC inversion. The results showed that the Mahalanobis distance classification method yielded the best classification performance, with a model inversion RMSE of 0.074—a 41.7% reduction compared to the traditional fixed-end element model. The study demonstrates that the high-resolution stereo imagery from Beijing-3 can effectively support the refined construction of DEMs and quantitative monitoring of vegetation cover in complex hilly areas and can provide a technical reference for the application of domestically produced commercial remote sensing satellites in the field of ecological monitoring. Full article

Background: Carotid free-floating thrombus (CFFT) is an uncommon but clinically significant cause of transient ischemic attack and ischemic stroke, carrying a substantial risk of recurrent cerebral embolization. Despite advances in vascular imaging, the optimal management of CFFT remains controversial because available recommendations are largely based on retrospective studies, case series, and expert opinion. The present study reports a single-center experience with CFFT and provides an updated narrative review of current diagnostic and therapeutic strategies, with particular attention to the timing of intervention. Methods: A retrospective review of patients diagnosed with CFFT and managed at our institution over a 20-year period was performed. Clinical presentation, imaging findings, treatment strategy, and outcomes were analyzed. In parallel, a narrative review of the contemporary literature was conducted to summarize current evidence regarding medical management, carotid endarterectomy, endovascular techniques, hybrid approaches, and emerging concepts related to thrombus composition and maturation. Results: Five patients with symptomatic CFFT were identified. Four patients underwent carotid thromboendarterectomy, whereas one patient was managed medically with anticoagulation and antiplatelet therapy, resulting in complete thrombus resolution. Two patients experienced neurological deterioration while receiving initial anticoagulation and subsequently required urgent surgical intervention. No postoperative strokes occurred after carotid endarterectomy. Review of the literature confirmed that anticoagulation remains the cornerstone of initial therapy and achieves thrombus resolution in a substantial proportion of patients. However, recurrent neurological events continue to occur under medical treatment, while the indications and timing of invasive intervention remain poorly defined. Recent advances in high-resolution magnetic resonance imaging and optical coherence tomography suggest that thrombus age and composition may become important determinants of future treatment selection. Conclusions: CFFT represents a rare but potentially unstable vascular condition for which high-quality evidence is still lacking. Initial anticoagulation remains the most widely accepted treatment strategy, while carotid endarterectomy, endovascular thrombectomy, carotid artery stenting, and hybrid procedures may be appropriate in selected patients. Future management algorithms may ultimately move beyond a one-size-fits-all approach by incorporating thrombus biology, imaging characteristics, and individual patient risk profiles. Although this concept remains preliminary, advances in thrombus characterization may provide the foundation for more personalized treatment strategies as further clinical evidence becomes available. Full article

Ultraviolet (UV) and near-infrared (NIR) dual-band photodetection is critical for applications ranging from secure optical communication and environmental monitoring to biomedical imaging. However, existing dual-band systems typically rely on discrete single-band detectors combined with optical filters, leading to complex alignment and high cost. Herein, we demonstrate a bipolar heterojunction (BHJ) based on a 2H-MoSe₂/Si/1T-WS₂ structure for filter-free, high-speed UV-NIR dual-band photodetection. The optimized device achieves high responsivities of 0.7 A/W@365 nm and 0.8 A/W@1064 nm at bias voltage of −2 V, along with a fast response time of 1.28 μs and a −3 dB bandwidth of 70 kHz. Furthermore, in comparative evaluations with broadband Si photodiodes, the BHJ successfully enabled high-quality NIR single-pixel imaging, reconstructing high-resolution images with 128 × 128 pixels under ambient lighting conditions. This work validates the potential of such transition metal dichalcogenides-Si heterojunctions for next-generation multispectral sensing and imaging systems. Full article

Timely and reliable mapping of landslide-affected areas from high-spatial-resolution optical imagery is essential for disaster investigation and post-event assessment. However, this task remains challenging because landslides usually exhibit large-scale variations, irregular boundaries, and strong spectral–textural similarities with surrounding bare-surface objects, which often cause missed detections, false positives, incomplete delineation, and inaccurate boundary localization. To address these problems, this paper presents a Scale-View Interactive Attention Network, named SIA-Net, for RGB-based landslide segmentation. First, a Multi-Scale Attention Module (MSAM) is constructed to encourage information exchange among features with different spatial resolutions. By doing so, the network can better represent both small scattered landslide patches and large continuous landslide bodies. Second, a Multi-View Attention Module (MVAM) is introduced to aggregate contextual cues from multiple receptive field views. This design strengthens the model’s ability to distinguish landslides from visually confusing objects, including bare soil, roads, riverbanks, and terrain shadows. In addition, a Convolutional Block Attention Module (CBAM) is incorporated during feature reconstruction to enhance landslide-related channel and spatial responses, thereby improving segmentation completeness and boundary localization. Experiments on the CAS Landslide Dataset (CLD) and GVLM Dataset show that SIA-Net provides more accurate landslide masks than the compared segmentation networks under the adopted benchmark settings. These results indicate that integrating scale-level interaction, view-level contextual modeling, and attention-guided decoding can effectively improve landslide extraction in complex optical remote sensing scenes. Full article