When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjectiv...
This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps...
The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to...
Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of...
Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating over
Cold-water coral reefs are rich, yet fragile ecosystems found in colder oceanic waters. Knowledge of their spatial distribution on continental shelves, slopes, seamounts and ridge systems is vital for marine spatial planning and conservation. Cold-wa...
Industrial facilities combine production-driven loads, large-span structures, specialised environmental control, safety constraints and frequent process reconfiguration, limiting the direct transfer of generic green-building criteria. This PRISMA 202...
Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coe...
In supervised training for underwater image enhancement (UIE), many deep learning methods rely on approximate reference images; however, they tend to neglect the inherent uncertainty of these references. To address this problem, this study proposes M...
This work proposes a multiple unmanned aerial vehicles (UAVs) cooperative trajectory planning scheme constructed by multi-agent reinforcement learning with hybrid critics, improving the searching and tracking efficiency and fairness when the dynamic...
The poor access to data on the marine environment is a handicap to government decision-making, a barrier to scientific understanding and an obstacle to economic growth. In this light, the European Commission initiated the European Marine Observation...
The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Moti...
Reliable crop mapping under incomplete seasonal observation is difficult because temporal evidence is sparse and public crop maps can disagree in parcel boundaries and class assignments. We investigate sparse-temporal crop mapping in Jilin Province u...
Health professionals’ recommendations increase vaccine uptake. We aimed to document stances, practices regarding adult vaccination, and their predictors among undergraduate medical and biomedical science students, as well as their perspectives...
The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for...
Small-scale object detection remains a significant challenge in semi-supervised object detection (SSOD), particularly in panoramic dental X-rays. Due to the small lesion size, low contrast, and complex anatomical background, conventional teacher mode...
Object detectors often rely on multiple metrics to reflect their accuracy and speed performances independently. This article introduces object detector efficiency index (ODEI), a hardware-agnostic metric designed to assess object detector efficiency...
Integrating information from heterogeneous data sources poses significant mathematical challenges, particularly in ensuring the reliability and reducing the uncertainty of predictive models. This paper introduces the Geometric Orthogonal Multimodal F...
The identification of senior residential concentrations requires geospatial methods that combine fine-scale population modeling with robust uncertainty assessment. This study introduces NORC-SIMCLUST, a framework that integrates dasymetric disaggrega...
Landslide susceptibility mapping (LSM) based on ensemble learning is commonly evaluated using deterministic metrics, whereas prediction reliability and resampling-based prediction uncertainty remain insufficiently explored. This study assessed whethe...
Space agencies, international and national organisations and institutions recognize the importance of regularly updated and homogenized land cover information, in the context of both nomenclature and spatial resolution. Moreover, ensuring credibility...
Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, an...
Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of...
High-resolution satellite imagery supports scalable mapping, but dense-to-vector pipelines can misrank overlapping candidates and retain local geometric errors. We present SAGE-Map, a framework for structure–appearance guided calibration and ge...
This study compares adjacent and bivariate maps in communicating variance-based global sensitivity analysis (GSA) results for a geodiversity assessment spatial multi-criteria model and examines the influence of prior exposure to geodiversity and map...
This study presents an updated global mangrove forest baseline for 2010: Global Mangrove Watch (GMW) v2.5. The previous GMW maps (v2.0) of the mangrove extent are currently considered the most comprehensive available global products, however areas we...
This paper investigates how well eGeMAPS features can be used to classify depression from a patient’s speech audio samples through the use of statistical resampling methods. We use permutation tests to evaluate, with high confidence, whether eG...
High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recent...
This research paper employed a multi-session framework to present an innovative approach to map monitoring within the domain of high-definition (HD) maps. The proposed methodology uses a machine learning algorithm to derive a confidence level for the...
Industrial visual inspection plays a crucial role in intelligent manufacturing. However, existing anomaly-detection methods based on unsupervised learning paradigms often struggle with issues such as overlooking minor defects and blurring component e...
While transparent objects present persistent challenges for RGB-D sensing due to complex refraction and reflection effects, these optical phenomena frequently precipitate unreliable depth measurements and compromise depth completion performance. In t...
Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacl...
This paper assesses algorithms proposed for constructing confidence ellipses in multidimensional scaling (MDS) solutions and proposes a new approach to interpreting these confidence ellipses via hierarchical cluster analysis (HCA). It is shown that t...
Climate change raises serious concerns for policymakers that want to ensure the success of long-term policies. To guarantee satisfactory decisions in the face of deep uncertainties, adaptive policy pathways might be used. Adaptive policy pathways are...
The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range o...
Swift and accurate semantic segmentation of underwater images is key for precise object recognition in complex underwater environments. Nonetheless, the inherent complexity of these environments and the limited availability of labeled data pose signi...
This paper reports a case study, developed in K-12 Portuguese Education, that aimed to analyze the computer science teachers’ knowledge, interest, and self-confidence to use educational robotics and other programable objects in classroom activities t...
In this study, we propose a novel approach for human pose estimation (HPE) in occluded scenes by progressively fusing features extracted from RGB-D images, which contain RGB and depth images. Conventional bottom-up human pose estimation models that r...
Mosquito-borne diseases can pose serious risks to human health. Therefore, mosquito surveillance and control programs are essential for the wellbeing of the community. Further, human-assisted mosquito surveillance and population mapping methods are t...
Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipm...
Efficient autonomous exploration in unknown environments is a core challenge for Unmanned Aerial Vehicle (UAV) applications in unstructured settings. The primary challenges are exploration speed, coverage efficiency, and the autonomous, efficient, an...
Indoor geomagnetic positioning provides a low-cost solution for smartphone-based localization without requiring additional infrastructure. However, conventional geomagnetic positioning methods depend on labor-intensive offline magnetic data surveys,...
Object detection in low-resolution optical-SAR remote sensing imagery is challenged by degraded object structures, residual cross-modal spatial inconsistency, and unreliable SAR responses. To address these issues, we propose TPA-NET, a progressive cr...
The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various sc...
In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire...
Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping ter...
In this paper a methodology is proposed to elaborate landslide activity maps through the use of PS (Persistent Scatterer) data. This is illustrated through the case study of Tramuntana Range in the island of Majorca (Spain), where ALOS (Advanced Land...
The purpose of this project was to evaluate the impact of a comprehensive medication adherence packaging (RxMAP) service on patient medication-taking behaviors and patient-centered outcomes. Adult patients who utilized a single independent community...
Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potenti...
Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper pre...
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