Advanced Camera Research Articles

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning

Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.

HST Proper Motion of Andromeda III. Another Satellite Coorbiting the M31 Satellite Plane

We measure the absolute proper motion of Andromeda III (And III) using Advanced Camera for Surveys/Wide Field Channel and WFPC2 exposures spanning an unprecedented 22 yr time baseline. The WFPC2 exposures have been processed using a deep-learning centering procedure recently developed as well as an improved astrometric calibration of the camera. The absolute proper motion zero point is given by 98 galaxies and 16 Gaia EDR3 stars. The resulting proper motion is (μ α , μ δ ) = (−10.5 ± 12.5, 47.5 ± 12.5) μas yr−1. We perform an orbit analysis of And III using two estimates of M31's mass and proper motion. We find that And III’s orbit is consistent with dynamical membership to the Great Plane of Andromeda system of satellites although with some looser alignment compared to the previous two satellites NGC 147 and NGC 185. And III is bound to M31 if M31's mass is M vir ≥ 1.5 × 1012 M ⊙.

Photometric redshift estimation for CSST survey with LSTM neural networks

ABSTRACT Accurate estimation of photometric redshifts (photo-zs) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on recurrent neural networks with long short-term memory (LSTM) to predict photo-z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalogue, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-z estimation. For instance, when trained on the same data set and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers $f_{\mathrm{out}}$ observed with a multi-layer perceptron neural network (MLP) model, while the normalized median absolute deviation $\rm \sigma _{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-zs of galaxies using photometric data from large-scale survey projects.

All Puffed Up: Exploring Ultra-diffuse Galaxy Origins Through Galaxy Interactions

We present new follow-up observations of two ultra-diffuse galaxies (UDGs) selected for their distorted morphologies and tidal features, suggestive of tidal influence. Using Hubble Space Telescope Advanced Camera for Surveys F555W and F814W imaging, we identify 8 ± 2 globular clusters in KUG 0203-Dw1 and 6 ± 2 in KDG 013, abundances typical for normal dwarf galaxies of similar stellar mass. Jansky Very Large Array data reveal a clear H i detection of KUG 0203-Dw1 with a gas mass estimate of logMHI/M⊙≲7.4 and evidence of active stripping by the host, while KDG 013 has no clear gas detection. The UDGs likely originated as normal dwarf galaxies that have been subjected to significant stripping and tidal heating, causing them to become more diffuse. These two UDGs complete a sample of five exhibiting tidal features in the full Canada–France–Hawaii Telescope Legacy Survey area (∼1502 deg). These tidally influenced UDGs exhibit diverse properties; one stands out as a potential result of a dwarf merger, while the remainder suggest tidal heating origins. We also cannot conclusively rule out that these galaxies became UDGs in the field before processing by the group environment, underscoring the need for broader searches of diffuse galaxies to better understand the impact of galaxy interactions.

Application of noise remote sensing to conquer loud vehicles in Amsterdam

Techniques for remote sensing of noise are rapidly evolving, allowing for automatic and unsupervised monitoring of individual noise events. In the H2020 project NEMO, noise remote sensing was developed and applied to detect individual loud vehicles in normal road or rail traffic. Potential applications include urban vehicle access restrictions (UVAR) such as in low emission zones, based on real world emissions rather than type approval data. The city of Amsterdam is also experimenting with influencing driving behaviour and raising road user awareness to reduce the number of high noise emitters. Amsterdam uses dynamic roadside signalling for this, which shows a message "too loud" to noise vehicles passing by. New tests are now performed with a more advanced acoustic camera to localize and track the individual vehicle, connected to license plate and speed detectors. This paper describes how NEMO results are used to achieve an actual reduction of high emitters in Amsterdam. First results of experiments are presented along with technical challenges that need to be overcome. These challenges include reduction of measurement uncertainty as well as uncertainty as to what vehicle a high noise event belongs to.

The Clearing Timescale for Infrared-selected Star Clusters in M83 with HST

We present an analysis of Hubble Space Telescope data from Wide Field Camera 3 (WFC3)/Ultraviolet Imaging Spectrometer, WFC3/IR, and the Advanced Camera for Surveys, investigating the young stellar cluster (YSC) population in the face-on spiral galaxy M83. Within the field of view of the IR pointings, we identify 454 sources with compact F814W continuum and Paβ line emission with a S/N ≥ 3 as possible YSC candidates embedded in dust. We refine this selection to 97 candidates based on their spectral energy distributions, multiwavelength morphology, and photometric uncertainties. For sources that are detected in all bands and have mass >102.8 M ⊙ (53 sources), we find that by 2 Myr 75% of IR-selected star clusters have an A V ≤ 1 and that by 3 Myr the fraction rises to ∼82%. This evidence of early clearing implies that presupernova (pre-SN) feedback from massive stars is responsible for clearing the majority of the natal gas and dust that surround IR-selected star clusters in M83. Further, this result is consistent with previous estimates based on WFC3 observations and adds to the growing body of literature suggesting pre-SN feedback to be crucial for YSC emergence in normal star-forming galaxies. Finally, we find a weak correlation between the YSC concentration index and age over the first 10 Myr, which matches previous studies and indicates little or no change in the size of YSCs in M83 during their early evolution.

Damage investigation of hybrid flax-glass/epoxy composites subjected to impact fatigue under water ageing

The aim of this study is to investigate the fatigue behaviour of hybrid flax-glass/epoxy composites under repeated impact loading subsequent to water ageing. Different plates of these composite materials were fabricated using the vacuum infusion technique. Five stacking sequences were considered: [F8], [G/F3]S, [G2/F2]S, [G3/F]S, and [G8], where F and G stand for flax/epoxy and glass/epoxy plies, respectively. Water ageing was conducted by immersing the composite specimens in tap water at room temperature for various durations, and until saturation was reached. Fatigue impact tests were carried out using three impact energies: 3, 4, and 5 J. An advanced high-resolution camera was used to monitor the evolution of damage mechanisms occurring on the non-impacted surfaces, while a laser thermometer was considered to track the temperature variations within each composite specimen. The obtained results show that flax-glass hybridization reduces the mass of absorbed water in flax/epoxy composite by up to 70%. Furthermore, there is a more pronounced decrease in longitudinal modulus and maximum stress in aged composites, with reductions of up to 70% compared to unaged ones. Additionally, visible damage occurs even at low energy levels, manifesting from the initial impacts in both aged and unaged composite laminates. Moreover, a correlation between the number of impacts to failure and the cumulative energy is revealed. Ultimately, water aging reduces the strength of the studied composite laminates and their resistance to impact fatigue. Furthermore, the hybrid laminates with high proportion of flax layers are particularly susceptible to water ageing effects.

A Strong Lensing Model of RCS1 J0224−0002.5

We present the details of a strong lensing model of RCS1 J0224−0002.5, a strong lensing cluster at z = 0.773, based on data from Hubble Space Telescope/WFC3, Advanced Camera for Surveys, and WFC2. Of particular interest in this field is a z = 4.8786 Lyα emitter (LAE), 15″ from the brightest cluster galaxy (BCG) that stretches northwest to west of the BCG in three different images, with a fourth counter-image ∼4″ southeast of the BCG. Using methods consistent with those used in studying five other LAEs in A. Navarre et al., we find that the cluster is best modeled by two cluster-scale dark matter halos aligned nearly perpendicular to each other at the center of the cluster. A z = 2.396 source is also lensed with six images, providing several constraints that were instrumental in producing this model.

The progenitor star of SN 2023ixf: a massive red supergiant with enhanced, episodic pre-supernova mass loss

ABSTRACT We identify the progenitor star of SN 2023ixf in Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) images. The supernova, localized with diffraction spikes and high-precision astrometry, unambiguously coincides with a progenitor candidate of $m_\text{F814W}=24.87\pm 0.05$ (AB). Given its reported infrared excess and semiregular variability, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined data set of HST optical, ground-based near-infrared, and Spitzer Infrared Array Camera (IRAC) [3.6], [4.5] photometry. The progenitor resembles an RSG of $T_\text{eff}=3488\pm 39$ K and $\log (L/\mathrm{L}_\odot)=5.15\pm 0.02$, with a $0.13\pm 0.01$ dex ($31.1\pm 1.7$ per cent) luminosity variation at a period of $P=1144.7\pm 4.8$ d, obscured by a dusty envelope of $\tau =2.92\pm 0.02$ at $1\, \mu \text{m}$ in optical depth (or $A_\text{V}=8.43\pm 0.11$ mag). The signatures match a post-main-sequence star of $18.2_{-0.6}^{+1.3}\, \mathrm{M}_\odot$ in zero-age main-sequence mass, among the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of $\dot{M}=(4.32\pm 0.26)\times 10^{-4} \, \mathrm{M}_\odot \, \text{yr}^{-1}$. The dense and confined circumstellar material is ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for variations of $\tau$ or $T_\text{eff}$ along with luminosity, a necessary assumption to reproduce the wavelength-dependent variability, which implies periodic dust sublimation and condensation. Given the observed SED, partial dust obscuration remains possible, but any unobstructed binary companion over $5.6\, \mathrm{ M}_\odot$ can be ruled out.

Delivery of luminescent particles to plants for information encoding and storage

In the era of smart agriculture, the precise labeling and recording of growth information in plants pose challenges for modern agricultural production. This study introduces strontium aluminate particles coated with H3PO4 as luminescent labels capable of spatial embedding within plants for information encoding and storage during growth. The encapsulation with H3PO4 imparts stability and enhanced luminescence to SrAl2O4:Eu2+,Dy3+ (SAO). Using SAO@H3PO4 as a low-damage luminescent label, we implement its delivery into plants through microneedles (MNs) patches. The embedded SAO@H3PO4 within plants exhibits sustained and unaltered high signal-to-noise afterglow emission, with luminous intensity remaining at approximately 78% of the original for 27 days. To cater to diverse information recording needs, MNs of various geometric shapes are designed for loading SAO@H3PO4, and the luminescent signals in different shapes can be accurately identified through a designed program, the corresponding information can be conveniently viewed on a computer. Additionally, inspired by binary information concepts, MNs patches with specific arrangements of luminescent and non-luminescent points are created, resulting in varied luminescent MNs arrays on leaves. An advanced camera system with a tailored program accurately identifies and maps the labels to the corresponding recorded information. These findings showcase the potential of low-damage luminescent labels within plants, paving the way for convenient and widespread storage of plant growth information.

Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review

As global demands on the poultry production and welfare both intensify, the precision poultry farming technologies such as computer vision-based cybernetics system is becoming important in addressing the current issues related to animal welfare and production efficiencies. The integration of computer vision technology has become a catalyst for transformative change in precision farming, particularly concerning productivity and welfare. This review paper delineates the central role of computer vision in precision poultry farming, focusing on its applications in non-contact monitoring methods that employ advanced sensors and cameras to enhance farm biosecurity and bird observation without disturbance. We delved into the multifaceted advancements such as the utilization of convolutional neural networks (CNNs) for behavior analysis and health monitoring, evidenced by the high accuracy sorting of eggs and identification of health concerns within target-dense farm environments. The review paper underscores advancements in precision agriculture, including accurate egg weight estimation and egg classification within cage-free systems, paralleling the poultry sector’s evolution towards more ethical farming practices. Moreover, it addresses the progress in poultry growth monitoring and examines case studies of commercial farms, showcasing how these innovations are being practically applied to enhance productivity and animal welfare. Challenges remain, particularly in terms of environmental variability and data annotation for deep learning models. Nevertheless, the review emphasizes the scope for future innovations like voice-controlled robotics and virtual reality applications, which have the potential to enhance poultry farming to new levels of efficiency, humanity, and sustainability. The insights assert that the continued exploration and development in computer vision technologies are not only instrumental for the poultry sector but also offer a blueprint for agricultural enhancement at large.

Transforming Agriculture with Drones: Applications, Challenges and Implementation Strategies

The integration of drone technology, or Unmanned Aerial Vehicles (UAVs), has revolutionized the agricultural sector, offering a new era of precision and efficiency. This paper examines the diverse applications of drones in agriculture, including crop health assessment, precision crop spraying, soil analysis, irrigation management, and livestock monitoring. Equipped with advanced sensors and cameras, drones provide real-time data collection and analysis, enabling farmers to make informed decisions, optimize resource use, and swiftly respond to environmental changes. This technological advancement enhances productivity, conserves resources, and promotes sustainable farming practices. However, the adoption of drones in agriculture faces challenges such as high costs, regulatory uncertainties, and the need for specialized skills, along with concerns over privacy and weather dependency. Despite these obstacles, the potential benefits like improved resource utilization and reduced environmental impact underscore the importance of drones in meeting the growing global food demand. This paper aims to provide an extensive overview of the role and potential of drone technology in reshaping the future of agriculture, empowering farmers to optimize yields, conserve resources and embrace a more sustainable approach in the face of global challenges

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions.

In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

Surface temperature measurement from infrared synthetic diagnostic in preparation for ITER operations

The protection of ITER in-vessel components and the plasma-wall interaction studies will be based on a large network of infrared (IR) cameras covering 70% of the tokamak. The surface temperature measurement from IR images remains challenging due to the presence of metallic targets, with changes in surface thermo-radiative properties (emissivity) and the presence of multiple reflections. The paper provides an overview of major progress to improve the interpretation of IR image and to get more reliable surface temperature from IR synthetic diagnostics. The paper presents the latest development of (1) the forward model to include the modelling of the edge localised modes and a new advanced camera that is better adapted to experimental data (2) the inverse model to retrieve the emissivity of the targets and the surface temperature from a neural network trained exclusively from synthetic IR images. Promising results have been obtained both from simulated test images with an estimated emissivity better than 0.05 and a surface temperature better than 10%, and from WEST experimental images of ITER-like wide-angle to filter reflection patterns.

Photogrammetry scans for neuroanatomy education-a new multi-camera system: technical note.

Photogrammetry scans has directed attention to the development of advanced camera systems to improve the creation of three-dimensional (3D) models, especially for educational and medical-related purposes. This could be a potential cost-effective method for neuroanatomy education, especially when access to laboratory-based learning is limited. The aim of this study was to describe a new photogrammetry system based on a 5 Digital Single-Lens Reflex (DSLR) cameras setup to optimize accuracy of neuroanatomical 3D models. One formalin-fixed brain and specimen and one dry skull were used for dissections and scanning using the photogrammetry technique. After each dissection, the specimens were placed inside a new MedCreator® scanner (MedReality, Thyng, Chicago, IL) to be scanned with the final 3D model being displayed on SketchFab® (Epic, Cary, NC) and MedReality® platforms. The scanner consisted of 5 cameras arranged vertically facing the specimen, which was positioned on a platform in the center of the scanner. The new multi-camera system contains automated software packages, which allowed for quick rendering and creation of a high-quality 3D models. Following uploading the 3D models to the SketchFab® and MedReality® platforms for display, the models can be freely manipulated in various angles and magnifications in any devices free of charge for users. Therefore, photogrammetry scans with this new multi-camera system have the potential to enhance the accuracy and resolution of the 3D models, along with shortening creation time of the models. This system can serve as an important tool to optimize neuroanatomy education and ultimately, improve patient outcomes.

A Video App for Screening Osgood-Schlatter Disease Using Soccer Instep Kicking Motion Analysis.

Osgood-Schlatter disease (OSD) is a type of osteochondrosis and traction apophysitis that results from repeated contractions of the quadriceps femoris muscle on the tibial tuberosity. Its prevention, early diagnosis, and treatment are crucial because it causes chronic knee pain and surgical approaches are required if left untreated. Three-dimensional motion analysis is a useful approach for elucidating the pathological factors of OSD; however, it requires advanced cameras, sophisticated facilities, and expensive software. Conversely, the advent of technology has provided affordable video recording devices, and smartphone/tablet-based applications have enabled two-dimensional (2D) motion analysis. This emerging tool and artificial intelligence technology were used to analyze the pivot leg from videos recorded on a tablet device during the instep kicks of adolescent soccer players. Therefore, in this study, we aimed to determine whether the pathological factors for OSD occurring in the pivot foot can be identified through a simple 2D motion analysis using a tablet device. In total, 94 knees of 47 soccer players (aged 14.1±0.8 years, all male) who belong to a single soccer club were evaluated. OSD was diagnosed using ultrasonography and physical examination (a positive bone fragment on ultrasonography or tenderness at the tibial tuberosity). Lower limb muscle tightness was evaluated using the finger-floor distance, straight leg raising test, heel-buttock distance, Thomas test, and ankle range of motion using a goniometer. We then performed motion analysis, and the instep kicking motion was recorded using a video camera on a tablet device. The joint angles of the hip, knee, and ankle were measured using a real-time human-pose detection system. Data were compared between the OSD and non-OSD groups. Overall, six of the 47 players (12.8%) were diagnosed with OSD. No correlation was found between lower limb tightness and the occurrence of OSD in all indices. However, the 2D motion analysis revealed that the knee flexion angle at the time of plantar placement during the instep kick movement was significantly larger in the OSD group than in the non-OSD group (OSD group: 42.0±7.2˚, non-OSD group: 33.5±6.6˚, *p=0.013). A video motion analysis revealed that the knee flexion angle during the instep kicking motion was significantly greater in athletes with OSD of the supporting foot.

UAS Photogrammetry for Precise Digital Elevation Models of Complex Topography: A Strategy Guide

Abstract. The presented research investigates different strategies to acquire high-precision digital elevation models (DEMs) of complex and inaccessible terrain using Structure-from-Motion and Multi-View Stereo applied to data of an unoccupied aerial system (UAS) equipped with real-time-kinematic (RTK)-GNSS. The survey scenarios are taken from real-life situations and thus, in comparison to many previous studies, provide information on how to operate under challenging conditions in difficult terrain. Among others, the study examines the influence of different flight configurations (parallel axes and cross-grid), flight altitudes (relative to ellipsoid or terrain) and associated variations in ground sampling distance, image orientations (nadir and oblique), advanced camera self-calibration techniques and georeferencing strategies in image block processing (direct and integrated) on the overall accuracy of the resulting DEMs. Random and systematic errors, including spatial patterns such as doming and bowling, are quantified using check points and differences between DEM calculations and independently acquired surface data from laser scans. This comprehensive analysis contributes valuable insights for UAS-based analysis of complex terrain with improved accuracy in DEM generation and subsequent applications like change detection.

A Type Ia Supernova near a Globular Cluster in the Early-type Galaxy NGC 5353

No progenitor of a Type Ia supernova (SN Ia) is known, but in old-population early-type galaxies, one may find SNe Ia associated with globular clusters, yielding a population age and metallicity. It also provides insight into the formation path and the SN enhancement rate in globular clusters. We sought to find such associations and identified SN 2019ein to be within the ground-based optical positional uncertainty of a globular cluster candidate within the early-type galaxy NGC 5353 at D ≈ 30 Mpc. We reduced the positional uncertainties by obtaining Hubble Space Telescope images with the Advanced Camera for Surveys, using filters F475W and F814W obtained in 2020 June. We find that the globular cluster candidate has a magnitude, color, and angular extent that are consistent with it being a typical globular cluster. The separation between the globular cluster and SN 2019ein is 0.″43, or 59 pc in projection. The chance occurrence with a random globular cluster is ≈3%, favoring but not proving an association. If the SN progenitor originated in the globular cluster, one scenario is that SN 2019ein was previously a double-degenerate white dwarf binary that was dynamically ejected from the globular cluster and exploded within 10 Myr; models do not predict this to be common. Another, but less likely scenario is where the progenitor remained bound to the globular cluster, allowing the double-degenerate binary to inspiral on a much longer timescale before producing an SN.

AI-Powered Autonomous Checkout System for Retail Stores

Abstract: Auto Bill offers a smart, automated checkout service for shops, leveraging state-of-the-art machine learning and computer vision technologies to provide an exceptional shopping experience. Designed to minimize interactions for security purposes during the pandemic, the innovation lies in utilizing modern techniques like image processing and weight sensors to create a novel shopping experience. This system aims to surpass traditional weight scales by incorporating advanced image recognition technologies, benefiting customers with a highly user-friendly interface, reduced checkout times, and improved accessibility. With a multifaceted approach involving deep learning neural networks and a web-based interface for transactions and inventory monitoring, Auto Bill envisions a future where retail checkout is taken to new heights. Supermarkets adopting this system can enhance customer service throughits advanced machine learning and camera technology, offering a seamless shopping experience. By integrating easily with existing POS systems, this solution streamlines operations and improves overall efficiency in the retail industry. With continuous testing and adjustments, Auto Bill aims to refine retail checkout to unprecedented levels of accuracy and customer satisfaction.

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.