Low-cost Unmanned Aerial Vehicle Research Articles

Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)

This study reports on a low-cost unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) system called LasUAV, from hardware selection and integration to the generation of three-dimensional point clouds, and an assessment of its performance. Measurement uncertainties were estimated in angular static, angular dynamic, and real flight conditions. The results of these experiments indicate that the point cloud elevation accuracy in the case of angular static acquisition was 3.8 cm, and increased to 3.9 cm in angular dynamic acquisition. In-flight data were acquired over a target surveyed by nine single passages in different flight directions and platform orientations. In this case, the uncertainty of elevation ranged between 5.1 cm and 9.8 cm for each single passage. The combined elevation uncertainty in the case of multiple passages (i.e., the combination of one to nine passages from the set of nine passages) ranged between 5 cm (one passage) and 16 cm (nine passages). The study demonstrates that the positioning device, i.e., the Global Navigation Satellite System real-time kinematic (GNSS RTK) receiver, is the sensor that mostly influences the system performance, followed by the attitude measurement device and the laser sensor. Consequently, strong efforts and greater economic investment should be devoted to GNSS RTK receivers in low-cost custom integrated systems.

Using Drones and 3D Modeling to Survey Tibetan Architectural Heritage: A Case Study with the Multi-Door Stupa

Tibet has an average altitude of 4900 m, a complex terrain, and unique climatic conditions. The technologies used to survey this country’s local architectural heritage must be portable, efficient, and versatile. Low-cost Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) algorithms can help satisfy these demands. Recent studies employing nadir images from low-cost UAVs and SfM algorithms have reported positive metric results (with centimeter-level accuracy) when modeling 2D objects (e.g., land, roofs, and facades). In Tibet, however, forming a complete 3D model of architectural heritage is highly preferable and doing so requires a camera network that can create nadir and oblique images with various baselines. This study compared the accuracies of surveying a Tibetan stupa using the UAV-SfM method compared with Ground Control Points (GCP) and Terrestrial Laser Scanning (TLS). The results indicated that the UAV-derived model is accurate enough for most surveying purposes (RMSE = 2.05 cm; 1/2000 of the stupa’s dimension). The accuracy and completeness of the 3D model allowed Historic Building Information Modeling (HBIM) and structural deformation analysis to also be undertaken. In addition, the stupa was integrated with geographic data (terrain and infrastructure) for visualization, management, and evaluation purposes at a larger scale. Considering the low cost, portability, and completeness offered by UAV and SfM, this tool offers promise for surveying Tibet’s architectural heritage.

Accuracy assessment on low altitude UAV-borne photogrammetry outputs influenced by ground control point at different altitude

Unmanned Aerial Vehicles (UAV) photogrammetry is as precise as conventional survey which able to produce centimetre accuracy. It is considerably quicker and gives brief portrayal of geography. Given ideal regulatory conditions this innovation will supersede current techniques for most mapping and studying applications. UAV can be an important technology to extract useful information of the earth surface in short period of time for example Digital Surface Models (DSM) and other information. The final products are highly dependent on the choice of values on various parameter for the flight planning, and for the processing of the data. The aim of this research is to study the effect of Ground Control Point (GCP) influence on the final accuracy of the output with difference flying altitude. All photogrammetric procedure was applied using Agisoft PhotoScan a Structure from motion (Sfm) software from aligning photos to build Digital Elevation Model (DEM) and georeferenced orthophoto. In this research, we have used total of 16 GCP’s where the coordinates measured with L1 RTK GPS receiver. Analysis was carried out to show the importance of GCP for mapping using low cost UAV. The analysis of both qualitative and quantitative were carried out to verify that the use of GCP is essential to produce a centimetre accuracy photogrammetric output. Utilization of GCP in mapping is significant because the output obtained from photogrammetry comes with actual measurements of the model that we have mapped.

Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

Abstract. Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques’ and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search

In recent years, many radioactive sources have been lost or stolen during use or transportation. When the radioactive source is lost or stolen, it is challenging but imperative to quickly locate the source before it causes damage. Nowadays, source search based on fixed-wing unmanned aerial vehicles (UAVs) can significantly improve search efficiency, but this approach has higher requirements for the activity of the uncontrolled radioactive source and the take-off sites. The aim of this study was to design and demonstrate a platform that uses low-cost multi-rotor UAVs to automatically and efficiently search for uncontrolled radioactive sources even with lower activity. The hardware of this platform consists of a multi-rotor UAV, radiation detection sensor, main control module, gimbal and camera, and ground control station. In the search process, the ground control station and UAV communicate wirelessly in real time. To accommodate different search scenarios, the study proposed three search algorithms with a theoretical comparison. Then, field experiments based on the traversal search algorithm showed that the search system based on multi-rotor UAVs could effectively and accurately conduct contour mapping of a region and locate the radioactive source with an error of 0.32 m. The platform and algorithms enable accurate and efficient searching of radioactive sources, providing an innovative demonstration of future environmental risk assessment and management.

Catching “butterflies” in the morning: A new methodology for rapid detection of aerially deployed plastic land mines from UAVs

It is estimated that there are at least 100 million military munitions and explosives of concern (MEC) devices in the world of various size, shape, and composition. Millions of these are surface plastic land mines with low-pressure detonation regimes, such as the mass-produced Soviet PFM-1. These aerially deployed land mines are concentrated primarily in postconflict developing countries such as Afghanistan and represent a continued humanitarian threat, while also thwarting economic and social development in impacted regions. Identification of this particular type of MEC category poses a significant geophysical challenge, as these mines contain almost no metal (nonferrous aluminum). As a result, standard MEC detection and remediation methodologies based on geophysical principles of magnetometry and electromagnetic induction prove largely ineffective and possibly dangerous. Low-cost unmanned aerial vehicles can rapidly collect large remotely sensed data sets with no risk to MEC technicians. We present results of an experiment focused on remotely assessing thermal signatures of plastic land mines relative to background geology to show that this type of analysis permits rapid detection of randomly dispersed plastic MECs with a high degree of accuracy and low associated costs.

Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms

Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS and hyperspectral scanner to be measured. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This paper presents three calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, this paper starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using ground control points and other using tie features. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for orthorectification of six hyperspectral UAV dataset acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the ground sampling distance of the used scanner (namely, 3–5 cm when flying at 60 m).

A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms.

Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%.

Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach

Structure from Motion (SfM) represents a good low-cost alternative to generate high resolution topography where LiDAR (Light Detection and Ranging) data is scarce or unaffordable. In this work, we demonstrate the advantages of high resolution elevation models (DEM) obtained using the SfM technique to delineate catchment boundaries and the stream network. The SfM-based DEM was compared with LiDAR data, distributed by the Mexican Government, and a previous high resolution topographic map generated by a RTK-GPS system. Aerial images were collected on a forested ecohydrological monitoring site in northwest Mexico using a commercial grade digital camera attached to a tethered helium balloon. Here we applied the SfM method with the removal of the vegetation, similarly to the more advance LiDAR methods. This was achieved by adjusting the point cloud classification parameters (maximum angle, maximum distance and cell size), which to our knowledge, has not has not been reported in the available SfM literature. The SfM terrain model showed minimal differences in ground elevation in the center of the image domain (0-0.5 m) while errors increased on the edges of the domain. The SfM model generated the largest catchment area, main and total channel length (1.07 ha, 106.1 and 223 m, respectively) while LiDAR model obtained the smallest area and main channel length (0.77 ha and 92.9 m, respectively). On the other hand, the SfM model had a better and accurate representation of the river network among all models evaluated due to its closest proximity to the observed GPS-tracked main channel. We concluded that the integration of low cost unmanned aerial vehicles and the SfM method is a good alternative to estimate hydro-morphological attributes in small catchments. Furthermore, we found that high resolution SfM-based terrain models had a fairly good representation of small catchments which is useful in regions with limited data availability. The main findings of this research provide scientific value within the field of hydrological remote sensing in particular in the acquisition of high resolution topography in remote areas without access to more expensive LiDAR or survey techniques. High resolution DEMs allow for a better characterization of catchment area size and stream network delineation which influence hydrological processes (i.e. soil moisture redistribution, runoff, ET).

UAV Capability to Detect and Interpret Solar Radiation as a Potential Replacement Method to Hemispherical Photography

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring–summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R2 = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (−8.06% bias) and highest impact in the modeling of pine regeneration.

Unsupervised discrimination between lodged and non-lodged winter wheat: a case study using a low-cost unmanned aerial vehicle

ABSTRACTWheat lodging significantly reduce the grain yield and quality. Mapping wheat lodging timely and accurately can help farmers get full compensatory damages in time. Therefore, the objective of this study was to employ a low-cost unmanned aerial vehicle (UAV) carrying a Red-Green-Blue camera to discriminate lodged from non-lodged wheat effectively. Low-cost UAV is easier to be accepted by Chinese farmers most of whom manage small-scale farms. After comparing a variety of colour features as well as their texture features, this study found that the texture feature of mean of G/B (digital number ratio of the green band to the blue band) derived from occurrence measures was the optimum discriminator of lodged and non-lodged wheat. This discriminator was still effective although the spatial spectral variations in the study area were much more complex than that in previous studies. With an unsupervised method based on the discriminator, the UAV system was able to discriminate lodged wheat from non-lodged wheat. The resultant of overall accuracy was 89.55% and Kappa coefficient was 0.76. The producer’s accuracies were 81.23% and 93.62%, whereas the user’s accuracies were 86.15% and 91.08% for lodged and non-lodged wheat, respectively. The retrieved wheat lodging distribution map showed a substantial agreement with ground reference data.

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low-cost aerial surveillance and mapping applications in remote regions. There is, however, a key limitation, which is the fact that low-cost UAVs have limited fuel capacity and, hence, require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in the fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in three-dimensional taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach.

Citrus canopy volume estimation using UAV oblique photography

Abstract: The canopy volume estimation method based on UAV oblique photography combined with pointed cloud processing technology was proposed in this study. Navel orange trees with irregular crown and landscape trees with regular crown were used as research objects in the experiment. Based on the multi-angle aerial images acquired by oblique photography with a low-cost four-rotor UAV, the three-dimensional point cloud and True Digital Ortho Map (TDOM) of the target area were generated after 3D reconstruction. Then, processing and classification were taken successively to obtain the Canopy Height Model (CHM) of individual tree. The cone sum algorithm, the convex-hull algorithm and the trapezoid segmentation algorithm were employed to estimate crown volume of individual tree. For orange trees, the R 2 between the cone volume sum algorithm and manual volume measurement was 0.63 of orange trees, and it is higher than that by the other two algorithms. For the landscape tree, the convex-hull algorithm did best, and the R 2 was 0.89. The results show that UAV oblique photography technology could be used to extract the individual canopy parameters of citrus orchard and to meet the requirements of large-scale and low-cost orchard management. Keywords: canopy volume estimation, oblique photography, Unmanned Aerial Vehicles (UAV), Canopy Height Model (CHM), point cloud DOI: 10.33440/j.ijpaa.20210401.157 Citation: Wang T, Li J C, Lin H, Deng L, Zheng Y Q, Yi S L, Xie R J, Lyu Q. Citrus canopy volume estimation using UAV oblique photography. Int J Precis Agric Aviat, 2021; 4(1): 22–28.

The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

Background and Purpose: Unmanned aerial vehicles (UAVs) are flexible to solve various surveying tasks which make them useful in many disciplines, including forestry. The main goal of this research is to evaluate the quality of photogrammetry-based digital surface model (DSM) from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of ground control points - GCPs) conditions. Materials and Methods: The UAV-based DSMs without (DSMP) and with using GCPs (DSMP- GCP) were generated. The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m sampling grid obtained from LiDAR-based DSM (DSML) with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Due to the non-normal distribution of residuals (vertical differences between UAV- and LiDAR-based DSMs), a vertical agreement was assessed by using robust measures: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile. Results: As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. The median, NMAD, 68.3% quantile, 95% quantile and RMSE* (without outliers) values for DSMP are 2.23 m, 3.22 m, 4.34 m, 15.04 m and 5.10 m, respectively, whereas for DSMP-GCP amount to -1.33 m, 2.77 m, 0.11 m, 8.15 m and 3.54 m, respectively. Conclusions: The obtained results confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather and environmental conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used.

Volumetric calculation using low cost unmanned aerial vehicle (UAV) approach

Unmanned Aerial Vehicles (UAV) technology has evolved dramatically in the 21st century. It is used by both military and general public for recreational purposes and mapping work. Operating cost for UAV is much cheaper compared to that of normal aircraft and it does not require a large work space. The UAV systems have similar functions with the LIDAR and satellite images technologies. These systems require a huge cost, labour and time consumption to produce elevation and dimension data. Measurement of difficult objects such as water tank can also be done by using UAV. The purpose of this paper is to show the capability of UAV to compute the volume of water tank based on a different number of images and control points. The results were compared with the actual volume of the tank to validate the measurement. In this study, the image acquisition was done using Phantom 3 Professional, which is a low cost UAV. The analysis in this study is based on different volume computations using two and four control points with variety set of UAV images. The results show that more images will provide a better quality measurement. With 95 images and four GCP, the error percentage to the actual volume is about 5%. Four controls are enough to get good results but more images are needed, estimated about 115 until 220 images. All in all, it can be concluded that the low cost UAV has a potential to be used for volume of water and dimension measurement.

Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images

Soybean has been the main Brazilian agricultural commodity, contributing substantially to the country’s trade balance. However, foliar diseases are the key factor that can undermine the soy production, usually caused by fungi, bacteria, viruses, and nematodes. This letter proposes a computer vision system to track soybean foliar diseases in the field using images captured by the low-cost unmanned aerial vehicle model DJI Phantom 3. The proposed system is based on the segmentation method Simple Linear Iterative Clustering to detect plant leaves in the images and on visual attributes to describe the features of foliar physical properties, such as color, gradient, texture, and shape. Our methodology evaluated the performance of six classifiers for different heights, including 1, 2, 4, 8, and 16 m. Experimental results showed that color and texture attributes lead to higher classification rates, achieving the precision of 98.34% for heights between 1 and 2 m, with a decay of 2% at each meter. Results indicate that our approach can support experts and farmers to monitor diseases in soybean fields.

Acquiring high-resolution topography and performing spatial analysis of loess landslides by using low-cost UAVs

The Loess Plateau is a region in China prone to frequent geological disasters, where thousands of loess landslides can be found. Conventional field survey methods are inadequate for the requirements of fine spatial analysis of landslides. Due to its numerous advantages (fast, efficient, low cost, safe, and able to acquire high-resolution data), structure from motion (SfM) technique to photogrammetric orientation of flights and modeling applied to photographs taken by unmanned aerial vehicles (UAVs) equipped with a camera has become a powerful new tool for the generation of high-resolution topography that has emerged in recent years, which has become a powerful new technique for acquiring high-resolution topographic data. In this study, we conducted nearly two months of field UAV surveys of loess landslides on the Loess Plateau, eventually established 3D digital models for 11 loess landslides, and produced high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs). High-resolution spatial analysis of the loess landslides (mainly including characteristic parameter extraction, topography profile analysis, surface feature analysis, and hydrologic analysis) was performed using Agisoft PhotoScan, ArcGIS 10.2, Global Mapper 17, and Origin Pro 9.0. The UAV technique allows us to further understand the micro-level internal spatial and structural characteristics of loess landslides. Moreover, not only does it allow us to accurately measure the characteristic geometric parameters but also enables us to detect the surface details of loess landslides (e.g., textures, fissures, and micro-landforms). Manifestly, we can also deduce the original structural characteristics and possible inducement mechanism of landslides based on a combination of high-resolution data acquired by UAVs, proper ground surveys, and theoretical knowledge. In summary, the low-cost UAVs are highly and especially suitable for surveys and digital terrain analysis of landslides on the Loess Plateau with sparse vegetation.

LONG CORRIDOR SURVEY FOR HIGH VOLTAGE POWER LINES DESIGN USING UAV

Abstract. The term Unmanned Aerial Vehicle (UAV) is often directly associated with the armed forces due to their widely-criticized use of such vehicles on the modern battlefield. However, with the advancement of UAV technology, the acquisition and operational cost of small civilian UAV have reduced while their functionalities have increased. Therefore, a wide variety of new civilian applications have emerged. Mapping industry has been benefited as affordable UAV can partially replace traditional platforms, such as helicopters and small aircrafts, for low altitude photography acquisition. Although relatively new to the industry, the use of UAV is rapidly commercialized and they are expected to have a sizeable impact on the mapping industry in the coming years. The aim of this work was to test the use of a low-cost UAV for orthophoto production and Digital Surface Model (DSM) creation, to be used for the design of a new 23km high voltage line of Electricity Authority of Cyprus.

ARCHITECTURAL HERITAGE DOCUMENTATION BY USING LOW COST UAV WITH FISHEYE LENS: OTAG-I HUMAYUN IN ISTANBUL AS A CASE STUDY

Abstract. The digital documentation of architectural heritage is important for monitoring, preserving, managing as well as 3B BIM modelling, time-space VR (virtual reality) applications. The unmanned aerial vehicles (UAVs) have been widely used in these application thanks to rapid developments in technology which enable the high resolution images with resolutions in millimeters. Moreover, it has become possible to produce highly accurate 3D point clouds with structure from motion (SfM) and multi-view stereo (MVS), to obtain a surface reconstruction of a realistic 3D architectural heritage model by using high-overlap images and 3D modeling software such as Context capture, Pix4Dmapper, Photoscan. In this study, digital documentation of Otag-i Humayun (The Ottoman Empire Sultan's Summer Palace) located in Davutpaşa, Istanbul/Turkey is aimed using low cost UAV. The data collections have been made with low cost UAS 3DR Solo UAV with GoPro Hero 4 with fisheye lens. The data processing was accomplished by using commercial Pix4D software. The dense point clouds, a true orthophoto and 3D solid model of the Otag-i Humayun were produced results. The quality check of the produced point clouds has been performed. The obtained result from Otag-i Humayun in Istanbul proved that, the low cost UAV with fisheye lens can be successfully used for architectural heritage documentation.

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.