Search Results (723)

Airside vehicles operate close to aircraft, service equipment and safety-critical surface markings, yet driver-assistance functions developed for road traffic do not directly transfer to airport aprons, taxiways and service roads. This article presents a vision-based driver-assistance and warning prototype for recognizing airport surface types and horizontal markings in video recorded at Poznan Airport. Two manually annotated segmentation datasets were prepared from GoPro HERO8 video acquired from a vehicle perspective: a four-class surface dataset covering asphalt, concrete, paving blocks and grass, and a three-class marking dataset covering red, white and yellow lines. The study compares You Only Look Once (YOLO) variants YOLOv8 and YOLOv11 with U-Net, DeepLabV3 and SegFormer under a common 512-by-512 input resolution and evaluates both model-level quality and complete video-application behavior. For semantic segmentation, SegFormer achieved the highest validation results, with Intersection over Union (IoU)/Dice of 0.7657/0.8624 for surfaces and 0.8852/0.9380 for markings. Among YOLO models, YOLOv8m obtained the highest surface mean average precision at an IoU threshold of 0.50 (mAP@50) of 0.7847, whereas YOLOv8s obtained the highest marking mAP@50 of 0.8449. On video recordings, paired YOLO configurations processed approximately 15–16 frames per second (FPS) on a personal computer (PC), while U-Net, DeepLabV3 and SegFormer processed approximately 10–11 FPS. A YOLOv8n pair compiled for Raspberry Pi 5 with Raspberry Pi AI HAT+ Hailo-8 reached 10.05 detection FPS and 18.15 processing FPS without GUI rendering. Under the adopted evaluation protocol, SegFormer achieved the highest mask quality in the conducted comparison, while the paired YOLOv8n configuration demonstrated embedded throughput feasibility. Full article

Continuous, non-invasive fish monitoring supports aquatic animal management, biodiversity assessment, and sustainable aquaculture, but embedded deployment requires a careful balance among accuracy, speed, memory, and computation under visually degraded underwater conditions. We developed ULFD-YOLO, an ultra-lightweight detector derived from YOLOv11n through coordinated redesign of the backbone, neck, and detection head. The model combines a custom convolutional MobileNetV4-tiny backbone, a hypergraph-based multi-scale fusion neck, and a lightweight MBConv head with channel attention. Experiments were conducted on Fish-BJ, an in-house dataset of 3402 images covering 21 species-informed aquarium-fish detection categories, and on a deliberately difficult 1180-image WildFish subset after dataset-specific training. On Fish-BJ, ULFD-YOLO achieved 0.960 mAP@0.5 and 0.732 mAP@0.5:0.95 with 1.3 M parameters, 2.6 GFLOPs, and a 3.0 MB model file, reducing parameters and computation by 50.0% and 58.7% relative to YOLOv11n. Bootstrap resampling yielded 95% confidence intervals of 0.946–0.973 and 0.638–0.821 for the two metrics, respectively. The model achieved 0.803 mAP@0.5 on WildFish and 19–24 FPS at 448 × 640 on a Jetson Orin Nano under its 15 W nvpmodel power mode. These results establish a practical accuracy–efficiency trade-off for embedded fish monitoring rather than peak localization accuracy. Full article

Landmark recognition for smart tourism is usually validated on curated benchmark images. In deployment, however, the classifier must handle user-generated photographs whose viewpoint, lighting, resolution, occlusion, and compression differ sharply from curated data. This paper evaluates a previously published multi-threshold enhancement and selective YOLO11n-cls ensemble under this shift, and provides a preliminary zero-shot comparison of three general-purpose multimodal large language models (MLLMs) on the same task. To measure the shift, we build Samarkand v2-SNS, a 300-image out-of-distribution test set of social-media photographs of 12 Samarkand landmarks, disjoint from the training and validation data. Under the shift, four supervised baselines fall by 12.73–22.08 percentage points to 73–80% accuracy, and their in-distribution ranking does not hold. The selective ensemble degrades least (99.24% to 93.00%, −6.24 points) and outperforms the strongest baseline by 13 points. A capacity-matched ablation shows that most of this robustness comes from enhancement diversity, not from generic ensembling. In a preliminary comparison, zero-shot MLLMs (GPT-5, Claude Sonnet 4.5, Gemini 2.5) reach only 24.81–54.26%, far below deployment needs. The results argue for reporting out-of-distribution accuracy alongside curated benchmarks, and for hybrid systems that pair compact specialised recognisers with MLLM-based interpretation. Full article

Seed germination and early survival are important phenotypes for plant breeding and agricultural management, yet they are still commonly assessed through labor-intensive manual stand counting. We present the Maize Seedling Detection Dataset (MSDD), a curated high-resolution red–green–blue (RGB) dataset derived from unmanned aerial vehicle (UAV) imagery collected over the 2019–2022 growing seasons. MSDD contains 3152 images and 163,921 annotated objects across three classes—single (92.47%), double (6.07%), and triple (1.45%) clusters of seedlings—and captures substantial variability in growth stage (V2–V12), illumination, soil appearance, wind, and camera viewpoint. Unlike many existing datasets, MSDD explicitly annotates clustered seedlings as double and triple classes, which are important for stand evaluation. We benchmarked YOLOv9, YOLO11, YOLOv12, and Faster-RCNN on MSDD to evaluate detection accuracy, class-specific performance, inference efficiency, and generalization across field conditions. Single-seedling detection was reliable across models, with the best mean average precision at 0.5 IoU (mAP@0.5) reaching 0.916, whereas double- and triple-seedling detection remained challenging because of class imbalance, occlusion, and annotation ambiguity. Detection was most reliable in high-contrast scenes and declined under wind, strong shadows, and bright soil backgrounds. YOLO11 provided the fastest evaluation throughput among the tested models (≈27 frames per second (fps)), while YOLOv9 achieved the strongest single-seedling detection performance. Synthetic augmentation improved class balance but did not improve generalization to naturally occurring clustered seedlings. Frames, labels, and trained models are available at Google Drive and Hugging Face. MSDD provides a public benchmark for maize seedling detection and for evaluating stand counting models under realistic field conditions. Full article

Glacial lakes are sensitive indicators of climate-driven cryospheric change, and their accurate mapping provides fundamental spatial information for water-resource assessment and glacial lake outburst flood (GLOF) hazard assessment. In southeastern Tibet, automatic extraction remains difficult because glacial lakes are small and easily confused with snow, mountain shadows, dark bedrock, riverine wetlands, and non-glacial water bodies. In this study, we integrate optical bands and water indices from Sentinel-2, topographic information derived from a digital elevation model, and radar backscatter from Sentinel-1 into a nine-channel multimodal dataset, and develop an improved YOLO11-seg model that combines spatial-to-depth downsampling, multi-scale attention, long-range context modeling, and content-aware upsampling to enhance small-lake detection, background suppression, and boundary delineation. Compared with U-Net, DeepLabV3+, YOLOv8-seg, YOLO11-seg, YOLO12-seg, and YOLO26-seg, the proposed model achieved the highest F1-Score of 0.9205 and an mAP50(M) of 0.9331, while its F1-Score and IoU on the independent test set reached 0.9209 and 0.8533, respectively. Using remote sensing imagery acquired in 2024, the model extracted 4766 glacial lakes in southeastern Tibet, covering 428.13 km²; 80.84% of these lakes were smaller than 0.10 km². The results demonstrate an effective and reproducible framework for automatic glacial lake mapping in complex alpine environments. Full article

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications. Full article

The physical condition of insulators on electrical transmission lines is critical for system reliability. Early fault detection such as broken discs or flashovers prevents outages and accidents. Traditional inspection methods are costly and difficult, driving demand for UAV-based autonomous systems. This article proposes a novel dataset enhancement method and a cascade deep learning model for detecting and classifying insulator disc conditions. Existing datasets were enriched by approximately 30% with wide-background, real-field images to improve model generalization. After comparative analyses, YOLO was selected for detection due to superior performance, while MobileNetV2 was chosen as the classifier for its processing speed advantage. CBAM attention module and Focal Loss function were integrated to boost classification performance and handle class imbalance. The resulting YOLO + MobileNetV2 model achieved 99.76% overall accuracy across “Normal”, “Broken”, and “Flashover” classes, with an F1-score of 0.99 for broken discs, operating with 185.60 ms latency, confirming its viability for real-time inspection. Full article

Mechanical component detection in industrial scenes is challenged by cluttered backgrounds, large-scale variation, specular reflection, high inter-class similarity, and class imbalance. To address the above problems, this paper proposes a lightweight YOLO-style detector that integrates a PP-HGNetV2 tiny backbone, an enhanced normalization-based attention module (ImNAM), and an improved complete intersection-over-union loss (ImCIoU). The HGNetV2 backbone enhances hierarchical multi-scale feature extraction and keeps the deployable computational complexity low. ImNAM has been modified to enhance discriminative representation by introducing dual-statistics channel weighting, orthogonal edge-aware spatial modeling and bipolar adaptive residual gating. ImCIoU enhances the accuracy of localization by combining quality-aware box scaling, scale-sensitive modulation and dynamic IoU-guided weighting. A class-balancing augmentation pipeline was applied to the four-category industrial dataset of Bearing, Bolt, Gear and Nut. All experimental results are reported as the mean ± standard deviation of five independent two-tailed training runs with different random seeds, and statistical significance is verified by paired t-tests (p < 0.05) with Bonferroni correction for multiple comparisons. Experimental results show that the proposed method achieves 90.82 ± 0.35% mean average precision (mAP@0.5), 91.95 ± 0.42% precision, and 82.98 ± 0.51% recall, outperforming nine mainstream lightweight detectors, including the latest YOLOv12n (2025) and RT-DETR-tiny. Extended evaluation on mAP@0.5:0.95, per-class AP and F1 score further confirms the advantages in localization accuracy and classification performance. Ablation studies confirm that the HGNetV2 family backbone provides the largest performance gain, while the improved attention mechanism and regression loss further enhance localization accuracy and robustness. With only 4.44 M parameters and 9.96GFLOPs, the proposed detector has achieved a good accuracy–efficiency trade-off and shows strong potential for intelligent industrial inspection on resource-constrained platforms, subject to further hardware-level deployment verification. Full article

Monocular per-object distance estimation aims to predict a metric distance for each detected object from a single RGB image. Although monocular dense depth estimation and monocular 3D object detection are well studied, indoor object-level distance estimation remains weakly standardized. In dense depth-based pipelines, object distance is commonly obtained by combining object detection with depth prediction and aggregating depth values within a region of interest. However, existing approaches differ in region selection, detection source, and aggregation strategy, limiting comparability across methods. This paper proposes a unified, deployment-aligned evaluation protocol for dense depth-based pipelines, where both predicted and reference distances are computed within the same predicted bounding box. This formulation standardizes region-of-interest selection, removes dependence on ground-truth boxes at inference time, and enables consistent evaluation across late-fusion methods. The framework integrates a curriculum-trained Depth Anything V2 ViT-S model for metric depth estimation with a YOLO11n detector for object localization. Under the same-box evaluation protocol on SUN RGB-D, the Depth Anything V2 ViT-S backbone achieves object-wise distance estimation accuracy of MAE = 0.1286 m, RMSE = 0.1817 m, AbsRel = 0.0662, and ${\delta }_1$ = 0.9785 using mean aggregation over valid box depths. Scaling the backbone from ViT-S to ViT-L further improves performance to MAE = 0.1059 m, RMSE = 0.1484 m, AbsRel = 0.0553, and ${\delta }_1$ = 0.9898, corresponding to approximately 17.7% lower MAE and 18.3% lower RMSE relative to ViT-S, and provides a standardized reference point for indoor object-level distance evaluation under the same-box protocol. Full article

To address the difficulty in stably detecting weld seam keypoints under complex industrial interferences, such as intense arc light, spatter, reflection, and partial occlusion, this paper proposes a lightweight weld seam keypoint detection model named StripePoint-YOLO. The proposed method formulates five typical types of weld seams as a unified detection-based keypoint localization task. Built upon YOLO11n, the model introduces a P2 detection head to enhance shallow high-resolution feature representation for keypoints and adopts SPDConv to reduce the loss of local details caused by early-stage downsampling. Meanwhile, the P5 detection output layer is removed, while its deep semantic features are retained for top-down feature fusion. This design reduces the negative influence of redundant coarse-scale predictions on the center localization of tiny keypoints. For optimization and training, WIoU v3 and NWDLoss are adopted as a joint regression loss to improve the stability of small-scale keypoint bounding box regression. In addition, an online physics-driven data augmentation strategy, OPDDA, is designed to simulate welding disturbances such as arc light, spatter, and dynamic occlusion. Experimental results show that StripePoint-YOLO achieves an mAP@50-95 of 80.46%, a Mean Center Error (MCE) of only 2.33 px, a parameter count of 1.86 M, and a computational cost of 19.42 GFLOPs, while reaching an inference speed of 159.80 FPS under the reported hardware configuration. Further MCE visualization and localization error analysis demonstrate that the proposed method maintains stable keypoint center localization across multiple weld seam types and complex noisy scenarios, verifying the effectiveness of StripePoint-YOLO for accurate and efficient weld seam keypoint detection in industrial welding images. Full article

Accurate detection of small, rotated objects in practical scenarios such as remote sensing imagery and autonomous driving remains a critical challenge. Existing methods often struggle to balance detection accuracy and real-time performance, and face limitations in capturing subtle features, fusing multi-scale information, and regressing oriented bounding box angles. To address these issues, this paper proposes an Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline. The model introduces systematic innovations at three levels: feature enhancement, attention mechanism, and feature fusion. Firstly, a high-resolution detection head is introduced, combined with a Cross-layer Soft-weighted Feature Fusion (CSFF) strategy, to adaptively fuse shallow detail information with deep semantic features, thereby significantly improving the model’s ability to capture features of tiny objects. Secondly, a Hybrid Attention Module (HAM), enhanced with global context, is designed. This module integrates spatial attention in parallel with an improved Global Context Aware Block, effectively strengthening the model’s focus on and discriminative power for key target regions. Finally, an Adaptive Feature Fusion Path Network (AFFPN) is constructed. By incorporating learnable weights and scale-sequence modeling, it achieves superior modeling of the nonlinear relationships between multi-scale features. Experimental results on the DOTA-V1.0 public dataset demonstrate that the proposed AS-YOLOR model achieves 82.72% on the core evaluation metric mAP@50, representing a significant improvement of 3.22 percentage points over the baseline YOLOv8 model, and outperforming various mainstream rotated object detection methods. Comprehensive ablation studies validate the effectiveness of each improved component within the model. This research provides a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects. Full article

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. Full article

Conformal prediction has emerged as a principled framework for uncertainty quantification in computer vision, offering rigorous finite-sample coverage guarantees. However, its application in object detection has remained largely confined to localization, as standard inference codebases typically yield only top-1 class scores, precluding full class-label conformalization. In this work, we bridge this gap by adapting four architecturally diverse detectors—Faster R-CNN, RetinaNet, YOLO11, and RT-DETRv2—to facilitate the extraction of comprehensive per-class score vectors and the estimation of background confidence in the absence of native background modeling. Leveraging these adapted architectures, we implement inductive conformal prediction (ICP) using five distinct nonconformity functions: Top-K, Adaptive Prediction Sets (APS), Hinge, Margin, and Brier score. Our framework is rigorously benchmarked across a curated 20-class subset of MS-COCO and two specialized parasite egg datasets (AI4NTD P1.5v2 and Chula-ParasiteEgg-11). In addition, a Naive cumulative-threshold method is included as a baseline for comparison with APS, given their comparable mathematical formulations. Across target coverage levels of 90%, 95%, and 99%, the conformalized models consistently achieved nominal coverage with only minor finite-sample deviations. Hinge and APS exhibited an optimal balance between statistical coverage and prediction-set efficiency, whereas Margin and Brier scores tended toward larger sets under high data complexity and strict coverage requirements. With empty prediction sets maintained below 0.1%, our findings establish ICP as a robust and adaptable paradigm for trustworthy class-label uncertainty estimation, particularly within safety-critical workflows such as automated parasite diagnostics. Full article

Multi-class oriented ship detection in optical remote sensing images remains challenging in densely berthed and nearshore scenes. Elongated hulls, arbitrary headings, background clutter, and similar vessel appearances can weaken feature aggregation and reduce the accuracy of rotated-box localization. This study proposes YOLO-CPCL, a compact oriented detector developed from YOLOv8n-OBB. In the final YOLO-CPCL architecture, a Ship-Oriented Slender Adaptive Fusion module (SOSA-Fuse) replaces all four C2f fusion units in the Neck. It combines learned content-adaptive sampling with a C2f-style split-and-concatenation pathway to improve feature aggregation for elongated and arbitrarily oriented ships. An Aspect-Ratio-Coupled Angle Supervision Loss (ARCAS-Loss) is further introduced by applying a bounded logarithmic aspect-ratio weight to a periodic cosine angle term. This formulation strengthens angle supervision for slender targets while limiting the influence of extreme samples. On the four-class HRSC2016 task, YOLO-CPCL improves precision, recall, mAP@50, mAP@75, and mAP@50–95 by 3.54, 7.39, 5.48, 8.60, and 7.23 percentage points, respectively. The parameter count is reduced from 3.08 M to 2.92 M, corresponding to a decrease of 5.19%, while the computational cost is reduced from 8.3 to 7.6 GFLOPs, a decrease of 8.43%. Additional evaluations on the Level-2 24-class setting of ShipRSImageNet and a custom DOTA-v1.0 protocol with multi-class training and ship-class reporting show positive aggregate gains. These results demonstrate that the proposed method improves ship recall and high-IoU oriented localization while reducing the parameter count and GFLOPs. Full article

Accurate extraction of vegetation information from high-resolution remote sensing (RS) imagery is crucial for efficient urban ecological environment monitoring and land use management. However, due to the high cost of manual annotation in remote sensing imagery and the complex textural variations and spectral confusion exhibited by vegetation under different terrains and lighting conditions, precise vegetation segmentation under small-sample conditions remains a significant challenge. Using the Nanjing Zijinshan region as a case study, this research conducts a systematic comparison of eight representative models within a unified high-resolution remote sensing small-sample experimental framework to address these complexity challenges. We fine-tuned and systematically compared the recently prominent “Segment Anything Model” (SAM) series (including SAM2-Tiny, SAM2.1-Tiny, MobileSAM, and MobileSAMV2), along with classic fully supervised models (U-Net, DeepLabV3+), open-vocabulary segmentation models (SegEarth-OV), and instance segmentation models (YOLO11s-seg), helping clarify the performance boundaries and applicable conditions of different technical paradigms in vegetation segmentation. Experimental results highlight the distinctive performance characteristics of these models. Notably, fine-tuned vision foundation models (such as SAM2.1-Tiny and SAM2-Tiny) demonstrated superior segmentation performance and cross-dataset generalization capabilities, with SAM2.1-Tiny achieving the highest mean Intersection over Union (mIoU; 0.7821) on the Zijinshan dataset, a 5.5% improvement over the classic U-Net model; SAM2-Tiny also maintained the most stable generalization performance in cross-dataset testing on LoveDA, Potsdam, and Vaihingen. In contrast, zero-shot SegEarth-OV and instance segmentation model YOLO11s-seg showed relatively lower performance in current semantic segmentation tasks, revealing the application boundaries of different paradigms. Beyond these findings, to further leverage unlabeled temporal imagery and break through small-sample constraints, we propose an innovative Cross-Temporal Pseudo-Label Self-Training (CT-PLST) strategy, which successfully improved SAM2-Tiny’s mIoU from 0.7776 to 0.7888 (+1.44%), providing a low-cost efficiency enhancement solution for remote sensing segmentation under scarce annotation conditions. To promote reproducible research in remote sensing and computer vision, we publicly release the fine-tuned models, related comparative experiment code, and a high-resolution remote sensing vegetation dataset covering multi-temporal scenarios; access details are provided in the Data Availability Statement. The findings of this study, combined with the proposed CT-PLST strategy and the high-precision segmentation results achieved by vision foundation models, can strongly support tracking analysis of vegetation cover changes, urban heat island effect assessment, and exploration of ecosystem dynamic evolution. Meanwhile, these achievements also provide valuable theoretical guidance and engineering references for practitioners and researchers in finding lightweight segmentation models suitable for specific image characteristics and computational cost constraints in practical applications such as rapid disaster risk assessment or forestry resource surveys. Full article