Accurate spatial localization of small, transient targets in low-texture aquatic environments remains a fundamental challenge in UAV-based remote sensing, where open-water surfaces often lack stable tie points, degrading exterior orientation estimation and conventional photogrammetric georeferencing. An integrated UAV framework combining DG/AAT-BA georeferencing with deep-learning-based oriented bounding box (OBB) detection was implemented for high-precision localization, validated on the Critically Endangered Yangtze finless porpoise (YFP, Neophocaena asiaeorientalis) in the Yangtze–Poyang Lake system. The georeferencing component selects direct georeferencing (DG) in open-water scenes and automated aerial triangulation with bundle adjustment (AAT-BA) in feature-rich nearshore scenes. Validation using two static verification points showed that, relative to DG, AAT-BA reduced geometric georeferencing RMSE from 2.59 to 0.62 m under straight-flight conditions and from 3.61 to 0.67 m under turning-flight conditions. For target detection, a lightweight Laplacian edge-enhancement convolution module (LapConv) was incorporated into YOLO-OBB backbones, amplifying weak-edge and low-contrast features of partially submerged targets. Across four representative YOLO-OBB models and three group-constrained partitions, LapConv consistently improved the mean mAP@0.5, with gains of 0.026, 0.024, 0.019, and 0.026 for YOLOv8, YOLO11, YOLO12, and YOLO26, respectively. Applying this framework to six UAV missions across three ecologically and hydrologically distinct subregions enabled georeferenced mapping of porpoise distributions and visualized spatial distribution characteristics during the survey period. The approach is reproducible, minimally invasive, and potentially transferable to UAV-based monitoring of other small aquatic wildlife, providing a methodological basis for fine-scale spatial surveys and subsequent habitat analysis.
Abstract. The evolution of mapping platforms has followed a consistent pattern: professional instruments are complemented by consumer devices that trade precision for scalability. Unmanned aerial systems transformed aerial photogrammetry by making it accessible beyond traditional aircraft, and smartphones, especially when equipped with high accuracy GNSS positioning, have demonstrated viable terrestrial mapping. This paper extends that progression to vehicle-based mapping by presenting SurveyXR, a web-based calibration and georeferencing framework that converts dashcam video from a GNSS-equipped vehicle into georeferenced imagery suitable for Structure-from-Motion (SfM) processing. By providing accurate per-frame exterior orientation parameters, the system enables direct georeferencing of the SfM output, eliminating the need for ground control points in the photogrammetric workflow. The pipeline implements checkerboard-based intrinsic calibration with automated quality diagnostics, Perspective-n-Point exterior orientation solving with automatic boresight detection, GNSS-synchronized frame extraction, and lever arm correction between the GNSS antenna and each camera. All computation runs in a browser or lightweight cloud backend, requiring no local software installation. The framework was evaluated on a 2026 Tesla Model Y equipped with a roof-mounted Emlid Reach RS4 Pro PPK GNSS receiver on the Ohio State University campus. Georeferencing accuracy was assessed against 71 independently surveyed RTK check points in two configurations: direct georeferencing only (no ground control) and GCP-constrained bundle adjustment. The paper documents the calibration methodology, time synchronization model, error budget analysis, and quantitative accuracy assessment.
R. Tamimi, C. Toth· The International Archives o...· 0 citations
Ground-based optical remote sensing of aerial targets at kilometer-scale standoff distances requires accurate keypoint localization for six-degree-of-freedom (6-DoF) pose recovery under variable illumination, motion blur, and atmospheric degradation. Many lightweight detectors use fixed-kernel convolutions, whose spatially invariant sampling may limit adaptation to heterogeneous target geometries and spatially varying image degradation. We introduce GeoAdapt, a compact keypoint detection framework that inserts deformable convolution v2 (DCNv2) modules between the feature pyramid network (FPN) neck and the detection head. GeoAdapt also replaces the standard object keypoint similarity (OKS) loss with a combination of Wing Loss and Bone Loss. The complete model contains 5.95 M parameters, 47.9% fewer than the You Only Look Once version 8 small pose model (YOLOv8s-pose). On a synthetic ground-based optical remote sensing benchmark, GeoAdapt achieved a percentage of correct keypoints (PCK) at a threshold of 0.05 times the bounding-box diagonal (PCK@0.05D) of 89.3% and a rotation error of 11.6°, improving PCK by 11.7 percentage points over YOLOv8s-pose. Zero-shot evaluation on manually annotated real ScanEagle and Matrice 200 imagery showed consistent advantages over YOLOv8s-pose and YOLO11s-pose in all six test scenarios. A factorial ablation indicated a positive interaction between DCNv2 and the Wing+Bone loss.
Yingwei Xia, Tianxiu Yu, Wang Xi et al.· Remote Sensing· 0 citations
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
Abstract. Mapping at the air–water interface in shallow coastal environments remains challenging due to the need to integrate heterogeneous datasets acquired under different geometric and operational conditions. This study presents a modular uncrewed surface vehicle (USV)-based system for simultaneous above- and underwater photogrammetric surveying supported by differential GNSS positioning. The system integrates a rigid multi-camera configuration, GNSS time synchronization, and a direct georeferencing workflow based on trajectory interpolation and lever-arm calibration. Experimental results from a rocky coastal site in Sardinia (Italy) show that underwater photogrammetry can achieve centimetric absolute accuracy (2–4 cm horizontally and ~8 cm vertically) without underwater ground control points. The USV enables controlled and repeatable acquisition in very shallow environments, while UAV photogrammetry complements the reconstruction of the emerged area. Limitations related to image quality and refraction effects are discussed. The system represents a flexible and scalable solution for integrated coastal mapping and monitoring.
Sergey Khokhlov, F. Menna, E. Nocerino· The International Archives o...· 0 citations
Abstract. Navigating Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments requires reliable autonomous localization techniques. This study proposes a vision-based localization framework utilizing satellite true orthophotos and Digital Surface Models (DSMs) as absolute geospatial references. The algorithmic pipeline integrates deep learning architectures—specifically SuperPoint and LightGlue—to establish robust image-to-map feature correspondences. The matched correspondences are used to estimate camera exterior orientation parameters through collinearity-based spatial resection with an Iteratively Reweighted Least Squares (IRLS) approach. To validate the proposed methodology, a multi-altitude dataset (100–250 m) was acquired across structurally diverse terrains, including dense building, high vegetation, and bare ground areas. Experimental evaluations demonstrate that the framework achieves meter-level absolute positioning accuracy and stable pose estimation. Analyses further reveal that matching robustness and localization success rates depend heavily on terrain texture and flight altitude; geometrically structured urban scenes at moderate-to-high altitudes consistently yield reliable correspondences, whereas low-texture environments and lower flight altitudes present persistent challenges for continuous visual tracking.
Tai-Cyuan Wang, Lai-Han Tsou, J. Jhan et al.· The International Archives o...· 0 citations
Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and other obstacles. We present a parallax-aware onboard platform that converts four synchronized fisheye streams into an open 1280x640 equirectangular panorama (ERP) interface. A purpose-built carbon-fiber airframe integrates the cameras, NVIDIA Jetson Orin NX, a flight controller, and a global navigation satellite system (GNSS) receiver. The formation pipeline selects projection depth per overlap and combines controlled seams and photometric fusion. Ours adds content-adaptive seam search and a validation-gated residual mesh and is evaluated under a sensor-rate deployment configuration. Evaluation uses more than 50,000 four-view groups from 18 field sequences. Relative to Fixed Depth, Ours reduces far-field P90 feature misalignment by 41.6% and achieves the lowest aggregate geometric errors across held-out sites. At a paced 20 Hz input rate, Ours sustains 19.99 frames/s at 13.29 W mean module-input power. Eight-sector ERP sampling reaches 90.8% mean daytime visual-place-recognition Recall@5. Together, these results validate an integrated onboard panoramic-perception architecture that unifies parallax-aware formation, sensor-rate embedded execution, and reusable downstream vision interfaces for ultra-low-altitude UAVs. Source code is available at https://github.com/DUNDAI1998/parallax-aware-uav-panorama.git.
Dun Dai, Zeping Lu, Cheng He et al.· 0 citations
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