Jul 2026· ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XI-2-2026, pp. 503-509· 0 citations· 8 references
TL;DR
Results show that coarse semantic cues, when combined with approximate geolocation, provide a resilient basis for identifying target vessels under high uncertainty, enabling faster and more reliable prioritization during wide-area maritime search operations.
Abstract
Abstract. Identifying a distressed vessel among many ships detected in wide-area aerial imagery is a critical challenge in maritime Search and Rescue (SAR) operations. Conventional methods cannot determine which vessel matches the incident description, especially when Automatic Identification System (AIS) reports are uncertain. This study proposes an integrated framework that combines MLLM-based semantic attribute extraction with geolocation fusion to prioritize candidate vessels according to their consistency with Situation Report (SITREP) based scenarios. The method detects vessels using YOLOv8, tracks them with Deep Simple Online and Real-time Tracking (DeepSORT), and performs image-based georeferencing using onboard metadata. A Multi-modal Large Language Model (MLLM) extracts appearance/status attributes from representative vessel images, while scenario descriptions are also converted to attributes. Both sets are encoded using MiniLM embeddings. Finally, semantic similarity is fused with geolocation proximity within an Support Vector Machine (SVM) classifier to produce a probability-ranked list of candidates. Experiments using real aerial search footage demonstrate robust identification performance across a range of scenario quality levels. The correct vessel appears within the top three candidates in more than 73% of cases and within the top five in more than 91%, even when attribute extraction is affected by low resolution, illumination effects, or missing scenario information. These results show that coarse semantic cues, when combined with approximate geolocation, provide a resilient basis for identifying target vessels under high uncertainty. The proposed framework offers a practical foundation for automated SAR decision support, enabling faster and more reliable prioritization during wide-area maritime search operations.
Abstract. In the initial response to wildfires, securing rapid and accurate geographic information is essential. However, helicopter imagery acquired on-site often lacks precise sensor metadata, such as camera pose and internal parameters, making the application of georeferencing difficult. In particular, obliquely captured wildfire imagery presents additional registration challenges due to severe viewpoint changes, scale variations, and low-texture environments. This study proposes an automated georeferencing pipeline capable of operating under these constraints. The proposed method consists of five stages: preprocessing, image retrieval, feature extraction and matching, Exterior Orientation Parameters (EOP) estimation, and orthomosaic generation. An initial Area of Interest (AOI) is defined using inaccurate initial position data, and the Region of Interest (ROI) within the reference map is obtained through a ResNet50-based image retrieval approach. Subsequently, virtual Ground Control Points (GCPs) are generated through deep learning-based feature matching. Elevation data is then assigned using a Digital Elevation Model (DEM), and EOP are estimated via Perspective-n-Point (PnP) and RANSAC algorithms. Intermediate frames are initialized via interpolation and refined through bundle adjustment to produce the final orthomosaic. Experimental results demonstrated that utilizing SuperGlue and LightGlue complementarily increased the number of successfully georeferenced intervals from 5 to 9. Furthermore, a minimum RMSE of 28.30 m was achieved in the most accurate interval. This method proves that by automating the feature-based georeferencing process, practical geographic information can be rapidly provided for initial disaster response, even in sensor-limited environments.
Seongyun Kim, Jeonghyo Oh, J. Cheon et al.· The International Archives o...· 0 citations
In the present generation of increasing geospatial data, accurate and automated extraction of building footprints from high-resolution aerial and satellite imagery has become crucial for various applications such as urban planning, infrastructure development, disaster management, and GIS database maintenance, as manual tracing is time-consuming and unstable for large-scale mapping. This study compares conventional image processing techniques such as thresholding, edge detection, morphological operations through a machine learning approach using Random Forest (RF), and deep learning-based semantic segmentation models, namely U-Net and DeepLabV3+, along with the Segment Anything Model (SAM) using a pre-trained prompt-based setup. All methods are tested on the same set of data, and a standardized data preprocessing is performed for fair comparison. The overall results indicate that the application of DeepLabV3+ is best, with an IoU of 82% and an F1 score of 90%. U-Net achieves second high IoU and F1 scores of 74% and 84% respectively, while Random Forest shows a high IoU of 60% and an F1-score of 72%. SAM has the lowest scores with an IoU of 50% and an F1 score of 51%.
The proposed model can support accurate regional image geolocation while enabling object level inspection of the visual cues underlying its predictions, demonstrating that object frequency and attention based visual evidence capture different aspects of a scene.
Muhamad Syukron, Danish Rafie Ekaputra, T. D. A. Widhianingsih· arXiv.org· 0 citations
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.
Object detection in remote sensing imagery faces challenges such as extreme scale variations and complex backgrounds. Although current methods have made significant strides in visual feature extraction, their predominant focus remains on the image itself, overlooking the potential of integrating external knowledge. To address this limitation, we introduce the knowledge-aware network with region-adaptive fusion for detection (KARFDet), which seamlessly integrates region-specific semantic information with visual features. First, a multiscale fused kernel attention (MSFKA) module is introduced, leveraging a parallel multibranch architecture to enhance contextual feature extraction. Second, a knowledge graph semantic extraction (KGSE) module is designed, employing the random walk with restart (RWR) algorithm to transform discrete knowledge into computable semantic associations. Finally, a novel triple-order knowledge integration (TOKI) mechanism is proposed, which adaptively fuses original, second-order, and probabilistic semantic knowledge, dynamically allocating knowledge weights based on target scale characteristics. Experiments on the DIOR, NWPU VHR-10, and SIMD datasets show that KARFDet achieves mAP50 scores of 65.6%, 91.9%, and 75.8%, respectively, significantly outperforming the baseline model and establishing a new paradigm for semantic-aware detection in complex scenarios. The code is available at https://github.com/ChengXCode/KARFDet
Xin Cheng, Yibo Liu, Haisu Zhang et al.· IEEE Transactions on Geoscie...· 0 citations
Accurate detection of land-use changes from aerial imagery is essential for urban development, environmental monitoring, and infrastructure management. While deep learning has advanced automated change detection, existing solutions remain sensitive to seasonal variations, lighting conditions, and image heterogeneity. This study presents an exploratory workflow-level evaluation of GPT-4o for semantic aerial imagery change detection and compares its behaviour with selected traditional, GIS-assisted, GAN-based, and U-Net-based approaches within the same imagery scenario. Unlike prior work focused on semantic segmentation, we investigate whether a general-purpose multimodal LLM can detect and describe changes without pixel-level training. NDVI exhibited inconsistent class separation; GAN-based mapping achieved a Structural Similarity Index Measure (SSIM) of 0.73 but lacked class fidelity; U-Net produced high accuracy for well-represented classes but struggled to generalize. GPT-4o achieved the best event-level performance, correctly identifying 89.17% of manually annotated changes and providing contextual descriptions and approximate spatial localization. Although promising, LLM performance depends on prompt specification and non-deterministic inference, raising reproducibility challenges. We address these by releasing prompt templates, raw outputs, and controlled inference settings. The results should therefore be interpreted as a case study of one proprietary multimodal model rather than as a comprehensive benchmark of all vision-language models. This exploratory study highlights the emerging potential of multimodal LLMs for interpretable and flexible geospatial analysis while outlining current limitations and future research directions.
Marek Ružička, Ján Staš, Stanislav Ondáš· IEEE Access· 0 citations
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