Aerial Photogrammetry Research Articles

REALITY CAPTURE METHODS FOR REMOTE INSPECTION OF BUILDING WORK

Abstract. Conventional building inspection, which requires in-person visits by a qualified inspector, can be costly, time-consuming, and even pose health and safety risks. The travel restrictions of the global Covid-19 pandemic further highlighted the need for remote inspection methods. Using reality capture techniques to create a digital 3D representation of the site offers promise for remote inspection of building work. This paper aims to assess different reality capture methods and their visualizations for remote inspection of building work through a case study conducted in Melbourne, Australia. The reality capture methods included terrestrial and mobile laser scanning, RGB-D imaging, and aerial photogrammetry using a UAV. Professional building inspectors participated in the evaluation process by conducting remote inspection based on the captured data and comparing it to the on-site experience. The assessment involved different visualizations of the data, including 3D panoramas, point clouds, and triangulated surface meshes. The results indicate that image visualisation and the ability to make measurement on images are desirable by professional inspectors. These findings support the adoption of reality capture techniques for remote inspection of building work, which can enhance the safety and efficiency of the process.

PHOTOGRAMMETRY IN ARCHITECTURAL EDUCATION: DEPLOYING AERIAL AND TERRESTRIAL MEANS

Abstract. Incorporating emerging surveying technologies, such as unmanned aerial vehicles (UAVs), into architectural education poses multiple challenges. This study places photogrammetry in an academic context and examines its potential impact on conventional architectural surveying methodologies. The methodology presented in this study outlines theoretical foundations and how to deploy them in the field. We taught architecture students with no experience in photogrammetry basic theoretical concepts, and we connected the theoretical framework to practical application through a case study. We considered existing methodologies suggested in the literature and their potential use in architectural education, aiming to extend architectural training to complex building surveying. For this purpose, a case study of surveying a historic building complex at the center of Athens, the Varvakeios market, is presented. The Varvakeios market was captured with aerial photogrammetry and terrestrial techniques, and a high-precision three-dimensional model was generated. The case study findings indicated that integrating photogrammetry into architectural education is viable.

Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring

Detailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too broad a spatial scale. In this study, we propose a methodology for mapping regenerating vegetation composition at 2 × 2 m2 spatial resolution, using very high spatial resolution (<1 m) remote sensing imagery obtained from remotely piloted aerial systems (RPAS) in conjunction with digital aerial photogrammetry (DAP) techniques for reconstructing vegetation structure. We applied logistic regression on multispectral orthomosaics, clusters of vegetation structure, and local illumination estimates to develop presence-absence models for eight key plant groups at various taxonomic levels as well as six plant functional types (conifer tree seedlings, grasses, tall- and low-growing forbs, shrubs, and mosses). Our results show higher accuracies for plant functional types (mean F-score = 0.67) compared to lower taxonomic levels (0.57). Notably, shrubs (F-score = 0.79), low-growing forbs (0.70), and mosses (0.69) exhibited the highest accuracies, while grasses (0.46), the aster family (Asteraceae spp; 0.48), and spruce seedlings (Picea spp; 0.54) demonstrated lower accuracies. Vegetation structure variables were identified as the most influential in the models, with mean NIRv ranking highest among spectral variables. High average ranks of spectral variation metrics (e.g., standard deviation of NIRv) implied the influence of environmental determinants such as plant co-occurrences and micro-habitat conditions, which drive spectral variation. Discrete composition maps were produced for three restoration sites and analogous wildfire-disturbed sites. Plant compositions found at one site pair exhibited similarity (Bray-Curtis = 0.28), however, certain key plant groups covered larger extents of the restoration site than anticipated. Willows (Salix spp; 25.4% vs. 9.3%), which are typically planted for soil stabilization and obstruction, and clovers (Trifolium spp; 11.1% vs. 3.6%), which represent non-native agronomic vegetation, were prominent. The developed methodology facilitates the generation of detailed plant composition maps, aiding evaluations of vegetation patterns that are difficult to discern visually or through conventional field sampling. This approach can effectively help assess restoration goals and guide adaptive management strategies, especially when incorporating the expertise of restoration ecologists in understanding how different vegetation types affect habitat quality.

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

We are currently experiencing an unprecedented moment in forest restoration, where monitoring activities in recovery areas play a vital role in problem identification and method adaptation. However, it is crucial to shift away from expensive traditional methodologies and develop technologies that enable large‐scale monitoring using more accessible and cost‐effective tools. In an effort to provide an evidence‐based perspective, we conducted a systematic review of how remotely piloted aircraft systems equipped with various sensors have been employed for forest monitoring. We consulted three databases and included 53 articles in our review. The results revealed a trend toward research in tropical ecosystems, with forest structure being the most frequently assessed attribute, and canopy height being the most widely measured structural indicator. Red‐green‐blue sensors were commonly used, both individually and in combination with others, and there was a noticeable shift toward the use of light detection and ranging. Data validation primarily relied on forest inventory methods, often involving comparisons of outputs from different sensors and the use of artificial intelligence algorithms. Despite the wide range of studies utilizing sensor‐equipped drones to analyze forest attributes, there is a notable scarcity of research specifically addressing the application of these technologies in forest restoration monitoring. Filling this research gap is essential, as employing techniques that enable large‐scale monitoring, such as aerial photogrammetry and remote sensing, aligns with current trends and contributes to global commitments to environmental restoration and conservation.

Rill investigation on landslide scar highlights the shortcomings of current knowledge on erosion of shallow landslides

AbstractWidespread shallow landslides typically result in the loss of productive soils and grasslands. Deposits of landslide tails have received much attention, while the severity of rill erosion on the landslide scar is generally neglected or underestimated. Thirty‐five rainfall‐induced shallow landslides were selected on Lishi (finer texture) and Malan loess (coarser texture) on the hilly and gully Loess Plateau, China. The rill erosion intensity and rill morphological parameters of these landslide scars were measured by on‐site investigation 3 years after the landslides. Landslide characteristics were investigated by unmanned aerial vehicle photogrammetry, and soil properties were also evaluated. We found that landslide scars suffered rill erosion for more than 8 years after landslides. Rill erosion intensities of the Lishi (22.91 × 103 t km−2) and Malan landslides (16.90 × 103 t km−2) in 3 years occupied 11.43% of the landslide deposit amounts. The structural equation model demonstrated that catchment area, soil bulk density, soil organic matter, water stable aggregate content (WSA), and sand content explained 87% of the variance in rill intensity through paths of rill depth, width, and distribution. Additionally, the negative effect of rill distribution on rill depth led to the unusual result that coarse soils with low WSA had low rill erosion intensities. In summary, the rill erosion of shallow landslide scars reached a non‐negligible value. The effect of initial rill distribution masked the effect of soil properties on rill intensity. Therefore, this study casts fresh insight into the sediment source of landslides, increasing theoretical support for landslide control strategies.

Stepping into Oligocene. A reassessment of the early Oligocene mammal tracks from Saignon (SE France)

ABSTRACT The Saignon ichnosite, located in the Apt syncline, yielded a high density of footprints with hundreds of mammal tracks preserved. It was discovered and first studied in the 1970s, and never re-examined since. Here we present a new analysis of the ichnosite with updates to ichnotaxonomy. The surface of the limestone bed bearing the prints was digitised by aerial photogrammetry (UAV). Best preserved tracks and trackways were digitised by short-range photogrammetric techniques. 3D data served as the basis for all measurements, including precise depth measurements. Of the hundreds of tracks present, this study focuses on the 20 best-preserved perissodactyl trackways. A review of the large perissodactyl morphotype is provided and Rhinoceripeda voconcense comb. nov. is defined. We also present possible evidence of social behaviour in early rhinocerotids. Based on fewer tracks a second smaller perissodactyl ichnotaxon, Lophiopus isp. is identified. Artiodactyl prints are attributed, when possible, to Bifidipes velox. The Saignon tracksite, dated as early Rupelian (MP21), is at an important faunal turnover, following the Grande Coupure. Its ichnofauna is characteristic of a post-Grande Coupure fauna with the presence of newcomer rhinocerotids that reached Europe from Asia and the persistence of endemic pre-Grande Coupure palaeotheriids.

Flood Affected Building Footprints 3D Model from LiDAR and Aerial Photogrammetry (Case Study: Pandugo Street, Surabaya)

Frequent rainfall events in Surabaya occurred due to tall silt within the channel, which prevent water flow and at that point flooding onto the street. Information about the events, including the simulation and prediction are needing accurate visualization to be major consideration for infrastructure and public facilities decision-making. 3D modelling are accurate enough as flood disaster risk management for buildings, using LiDAR and aerial photogrammetry that can provide strong visual representations accurately. DSM and DTM derived from aerial photogrammetry survey are then used to get DSM LiDAR. DSM&DTM from LiDAR was taken in 2016 and DSM&DTM from aerial photogrammetry was taken in 2022. The only objects that are updated are buildings from DSM aerial photogrammetry then applied to DSM LiDAR. Buildings footprint, road network, nDSM, and flood inundation data are then processed to get the 3D Model of buildings and floods. The model are then evaluated by comparing the total of affected buildings with manual methods. Validation on buildings height also performed and results in 0.86233 RMSe that achieved the LOD2 standard. Potential further studies could focus on improving the LOD and considering other parameters such as rainfall data or gutter data.

Combination of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Heritage Building Information Modeling: A Case Study of Tarsus St. Paul Church

Cultural heritage building information modeling (HBIM) is an emerging process allowing us to reconstruct built heritage virtually. The data of a digitally documented cultural heritage building offers significant advantages as it is accessible and modifiable by all professionals involved in the same or different projects. The most important factor affecting the accuracy and precision of the HBIM model is the ability to collect complete and accurate information about the physical structure. Combining terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry point clouds is one of the most efficient ways to capture accurate digital data on the building. This study provides the foundation for creating an HBIM model for cultural heritage the coupling of spatial data with TLS and UAV. This paper aims to generate synergy between TLS and UAV point cloud data and ensure that the spatial database contains sufficient data to model historical objects with HBIM tendencies.

Polis Sites and Sightlines: Using Digital Techniques to Trace the Experience of the Built Environment of Hellenistic Athens

ABSTRACT Utilizing unmanned aerial vehicle (UAV) photography and photogrammetry, this project created a digital elevation model of the Agora and Acropolis of Athens in order to understand the evolution of the city’s built environment in the Hellenistic and early Roman period (ca. 300 BCE–50 CE). Populated with 3D block models, the digital elevation model enabled advanced viewshed analyses that clarify which monuments were visible to—and interacted with—one another. This article demonstrates the capabilities and analytical potential of such modeling by examining the monuments dedicated to and by the Attalid dynasty of Pergamon and how these constructions affected the experience of the city’s built environment.

3D Semantic Segmentation of Aerial Photogrammetry Models Based on Orthographic Projection

Semantic segmentation of 3D scenes is one of the most important tasks in the field of computer vision and has attracted much attention. In this paper, we propose a novel framework for 3D semantic segmentation of aerial photogrammetry models, which uses orthographic projection to improve efficiency while still ensuring high precision, and can also be applied to multiple types of models (i.e., textured mesh or colored point cloud). In our pipeline, we first obtain RGB images and elevation images from the 3D scene through orthographic projection, then use the image semantic segmentation network to segment these images to obtain pixel-wise semantic predictions, and finally back-project the segmentation results to the 3D model for fusion. Specifically, for the image semantic segmentation model, we design a cross-modality feature aggregation module and a context guidance module based on category features, which assist the network in learning more discriminative features between different objects. For the 2D-3D semantic fusion, we combine the segmentation results of the 2D images with the geometric consistency of the 3D models for joint optimization, which further improves the accuracy of the 3D semantic segmentation. Extensive experiments on two large-scale urban scenes demonstrate the efficiency and feasibility of our algorithm and surpass the current mainstream 3D deep learning methods.

A Low-Cost and Fast Operational Procedure to Identify Potential Slope Instabilities in Cultural Heritage Sites

Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top of ancient ruins, to protect urban agglomerations, goods and people. The geographical locations of these centers allowed them to maintain their original conformation over time, but, at the same time, exposed them to a high risk of landslides. In this context, this research aimed to present an integrated and low-cost approach to study the potential landslide phenomena affecting two medieval towns. Field surveys and mapping were carried out through the use of innovative digital mapping tools to create a digital database directly on the field. Data gathered during field surveys were integrated with GIS analyses for an improved interpretation of the geological and geomorphological features. Due to the inaccessibility of the cliffs surrounding the two villages, a more detailed analysis of these areas was performed through the use of unmanned aerial vehicle-based photogrammetry, while advanced differential synthetic aperture radar interferometry (A-DInSAR) interpretation was undertaken to verify the stability of the buildings in proximity to the cliffs and other potential active failures. The results of the study highlighted the similar geometry and structural settings of the two areas. Kinematically, the intersection of three main joint sets tends to detach blocks (sometimes in high volumes) from the cliffs. The A-DInSAR analysis demonstrated the presence of a landslide failure along the northwest side of the Monte San Martino town. The buildings in proximity to the cliffs did not show evidence of movements. More generally, this research gives insights into the pro and cons of different survey and analysis approaches and into the benefits of their procedural integration in space and in time. Overall, the procedure developed here may be applied in similar contexts in order to understand the structural features driving slopes’ instabilities and create digital databases of geological/monitoring data.

Laser scanner and UAV digital photogrammetry as support tools for cosmic-ray muon radiography applications: an archaeological case study from Italy

The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach.

Image Segmentation and Filtering of Anaerobic Lagoon Floating Cover in Digital Elevation Model and Orthomosaics Using Unsupervised k-Means Clustering for Scum Association Analysis

In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance.

Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers

In recent years, advanced digital technologies have driven an outstanding paradigm shift in the field of architectural heritage, particularly for building modelling, historical documentation and touristic promotion. Nonetheless, they show great potentialities in the field of assessment and control of the state of conservation of heritage buildings. In particular, close-range and aerial photogrammetry have increasingly relied on low-cost and user-friendly tools and procedures, with a high degree of automation that makes them accessible to specialists who are foremost involved in architectural diagnosis and conservation, rather than in remote sensing sciences. In this framework, this paper provides a scoping review of 117 publications, based on the PRISMA protocol, from Scopus and Web of Science databases, related to the employment of photogrammetric models and methods, with specific focus on the targets and purposes of the diagnostic process, including decay mapping, structural monitoring and modelling, non-destructive investigation and multi-source documentation. In detail, the results point out that current studies mainly support robust processing of large amounts of information from direct observation of surface alterations, systematic correlation between materials, construction characteristics, visible anomalies and experimental measurements, as well as multi-disciplinary collaborative workflows through remote inspection and harmonized data management. Further improvements were identified, including standardization of acquisition procedures, automatization of elaboration pipelines, integration of real-time data, validation of diagnosis decision-making support tools and scalability to networks of assets.

Lava flow daily monitoring: the case of the 19 September–5 October 2022 eruption at Piton de la Fournaise

Taking prompt and effective actions to mitigate risks associated with an effusive eruption greatly depends on the monitoring of lava flow emplacement. Here we report on the monitoring of the lava flow emplaced during the 19 September to 05 October 2022 eruption at Piton de la Fournaise (La Réunion) that involves an unprecedentedly large data set acquired using multiple techniques and sensors. These include aerial photogrammetry to construct digital surface models and define lava flow geometry during and after emplacement. Complementary use of multiple satellite sensors (visible, infrared, and radar) allowed lava flow field contour definition and an estimation of time-averaged discharge rate with daily frequency. Evolution of the eruptive cone morphology was also monitored through aerial photogrammetry and radar multi-viewing angle imagery. This combination of data obtained from several institutes and using various techniques—from ground, air and space—allowed detailed tracking of lava flow advance and serves as an example for future eruptions at Piton de La Fournaise and at other active volcanic sites.

Seasonal morphological evolution and migration of granule ripples in the Sanlongsha Dune Field, northern Kumtagh Sand Sea, China

Granule ripples are aeolian landforms that are armored against erosion by coarse grains at their crest. Their bimodal grain-size distributions and large dimensions distinguish them from normal wind ripples. The morphodynamics of granule ripples have attracted considerable attention because they provide a good example of coarse-grained aeolian bedforms. Previous studies have mainly focused on the morphological characteristics and formation process of granule ripples, but their evolution has been less studied. Using unpiloted aerial vehicle photogrammetry and real-time kinematic positioning, we conducted five field observations in the Sanlongsha Dune Field of northern China's Kumtagh Sand Sea. We analyzed the seasonal morphological evolution and migration of granule ripples, and the corresponding wind regimes. The morphometric parameters and migration rate showed distinctly different changes in different periods. The lowest height, the fastest migration, and most of the disintegration of granule ripples occurred in the season with the strongest wind. Although the proportions of wind speeds that exceeded the impact and fluid threshold velocities of coarse grains were low, these wind events significantly influenced the morphodynamics of granule ripples. Our findings and theoretical analysis demonstrate that the evolution of granule ripples is closely related to bimodal and unimodal transport events. To enhance our understanding of the morphodynamics of granule ripples, we must focus future research on the reptation and saltation of coarse grains.

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

Quantifying nutrient and sediment erosion at riverbank cattle access points using fine-scale geo-spatial data

Unrestricted cattle access to the riparian zone can exacerbate riverbank erosion in grazed grassland catchments. Knowledge gaps include the magnitude of erosion and other environmental pressures at cattle access points. This study aimed to address this by using two high resolution geo-spatial methods; 1) aerial photogrammetry and 2) terrestrial laser scanning to measure cumulative, seasonal, and annual erosion rates at nine unmitigated cattle access points in Northern Ireland. Total, fine sediment and total phosphorus exports were determined through bulk density and deep soil core sampling campaigns of exposed bank faces. Accumulated erosion was estimated using method 1) at 1.0 – 49.5 t and 0.51 – 16.64 kg for total sediment and total phosphorus, respectively. Using method 2) median annual export coefficients of 0.19 – 0.21 t m−1 and 0.065 – 0.087 kg m−1 (normalised to streambank length) were determined for total sediment and total phosphorus transfers respectively and these mostly occurred during the grazing season (median 84% for both sediment and total phosphorus). In terms of livestock pressures, these annual exports equate to 0.34 – 0.40 t LU-1 yr−1 and 0.103 – 0.111 kg LU-1 yr−1 for total sediment and total phosphorus, respectively (1.19–1.89 LU ha−1). The conventional measure of protective fencing is likely to prevent such transfers to rivers. Scaling a nationwide agri-environment scheme over six years which installed 2,493 km of riparian fencing (and assuming from this study that 1.9 % of all riparian field boundaries had cattle access impact), this measure potentially saved 9,047–9,999 t yr−1 and 3,095 – 4,143 kg yr−1 of total sediment and total phosphorus, respectively, from entering water courses.

Aerial photogrammetry to characterise and numerically model an ice jam in Southern Quebec

Abstract Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.

UAV based Topographical mapping and accuracy assessment of Orthophoto using GCP

For smaller locations, the traditional aerial photogrammetry techniques utilizing helicopters or airplanes are expensive and difficult. A new competitive strategy is necessary for quick spatial data collecting at a low cost and in a short amount of time for a developing nation like Nepal where geospatial data is in great demand. Currently, the UAV (Unmanned Aerial Vehicle) has become an alternative for different engineering applications, especially in surveying, one of these applications is for making a topographical map. This study demonstrates how this can be achieved using one of the evolving remote sensing technologies, Unmanned Aerial Vehicles (UAVs). Besides, this study also involves image processing and topographic map production using Pix4D and GIS environments. For this study,DJI Mavic Air-2 Advanced quadcopter collected about 207 images at a flying height of 80 m above the Kathmandu University area. An orthophoto of 2.4 cm GSD covering 127064 sq. Meter of area was produced. The RMSE of 5.37 cm in X and 3.32 cm in Y and 4.05cm in Z was achieved With appropriate checkpoints.The measurements in the orthophoto replicated the field measurements to an error of less than 0.5% of the actual dimensions.