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Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun's illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun's illumination, and the second where both the sun's illumination and the sensor's viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor's geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this. [ABSTRACT FROM AUTHOR]

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun’s illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun’s illumination, and the second where both the sun’s illumination and the sensor’s viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor’s geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this.

The use of Unmanned Aerial Systems (UAS) in remote sensing applications, specifically using the Trimble UX5 HP platform for aerial data collection over Karakol city in Kyrgyzstan. Photogrammetric technique used to identify and match common features in the overlapping aerial images to create a sparse point cloud, which were further processed to create a Digital Surface Model (DSM). A slope-based filtering algorithm was applied to the DSM data for generating a Digital Terrain Model (DTM). The normalized Digital Surface Model (nDSM) was derived from the DSM by subtracting the DTM. Object-based image analysis applied to UAS datasets for the extraction of building footprints in an urban area. The results indicate that extracted building footprints have been generated accurately with an overall completeness of 92.4% and correctness of 95,2%. [ABSTRACT FROM AUTHOR]

The medieval wall of Jaén is a historical monument that has suffered from the apathy of institutions in recent years, causing its calamitous current status. This study focuses on the previous geomatic tasks developed to obtain a 3D documentation of this site in order to manage future restoration works. The methodology included the integration of data obtained using several geomatic techniques, such as LiDAR and photogrammetry at three scale levels, from the general to the particular. Therefore, data fusion is the main aspect of this methodology, developed in an attempt to take advantage of the benefits of each technique. The application demonstrated the feasibility of using the methodology in order to obtain a complete documentation, including 3D models, orthoimages and DEMs, at several scales with different resolutions and point densities. In addition, we also considered the accuracy of data and products with respect to the project requirements. Another aspect to highlight is the reduction in acquisition time by minimizing the necessity of conducting classic surveying to obtain georeference data. The results show reliable products for supporting restoration tasks and allowing the development of a BIM application to manage them, but also for the dissemination of knowledge regarding this unknown monument.

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it. [ABSTRACT FROM AUTHOR]

The medieval wall of Jaén is a historical monument that has suffered from the apathy of institutions in recent years, causing its calamitous current status. This study focuses on the previous geomatic tasks developed to obtain a 3D documentation of this site in order to manage future restoration works. The methodology included the integration of data obtained using several geomatic techniques, such as LiDAR and photogrammetry at three scale levels, from the general to the particular. Therefore, data fusion is the main aspect of this methodology, developed in an attempt to take advantage of the benefits of each technique. The application demonstrated the feasibility of using the methodology in order to obtain a complete documentation, including 3D models, orthoimages and DEMs, at several scales with different resolutions and point densities. In addition, we also considered the accuracy of data and products with respect to the project requirements. Another aspect to highlight is the reduction in acquisition time by minimizing the necessity of conducting classic surveying to obtain georeference data. The results show reliable products for supporting restoration tasks and allowing the development of a BIM application to manage them, but also for the dissemination of knowledge regarding this unknown monument. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAV) in photogrammetric mapping/surveying facilities has increased recently due to the developments on photogrammetric instruments and algorithms that enhance high-quality final products (orthoimages, digital surface model-DSM, etc.) in fast, accurate, and economical way. The aim of this study was to assess the accuracy of a UAV-based post-processing kinematic (PPK) solution. To do that, two methods were implemented with PPK solution and georeferencing with ground control points (GCPs). According to the statistical results, root mean square error (RMSE) values obtained from the GCPs and PPK solutions in the horizontal component are 6.5 cm and 5.4 cm, respectively. The RMSE values in the vertical component (ellipsoidal heights) were obtained as 4.8 cm (GCPs) and 5.2 cm (PPK), respectively. The results show that UAV-PPK method can also be used to produce photogrammetric products where high accuracy (≤ 10 cm) is required without GCPs. In addition, the results obtained regarding the use of this method clearly show that it can be applied in many different fields such as agriculture, forestry, natural disasters, and geomatics. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are modern tool for various scientific research areas such as agriculture, disaster management, mining and surveying. Among them, remote sensing is utilizing UAVs to acquire high-resolution digital images of ground surface. In remote sensing and photogrammetric operations, the geometric quality of the imagery basically depends on the relation between pixel size and the map scale, contrast information, atmosphere and the sun elevation, the printing technology, screen resolution and the visual acuity (ISPRS Int J Geo-Inf, Liu et al. Liu et al., ISPRS International Journal of Geo-Information 7:333, 2018; Eur Assoc Geosci Eng 1–5, Zatserkovnyi et al. Zatserkovnyi et al., European Association of Geoscientists and Engineers 25:1–5, 2020). In this study, accuracy has been assessed and compared of true digital orthoimages generated by two different software with same configuration. Three datasets of different places called dense urban, urban slum and forest mountains have been collected from field with area covering 0.061, 0.130 and 2.040 km2 and ground sampling distance 1.79, 2.19 and 11.86 cm, respectively. The orthoimages were generated using Pix4D and AgiSoft Photoscan software and then randomly distributed checkpoints, and visual angular distortion on the orthoimage was used to verify its precision. It was observed that in dense urban and urban slum Pix4D accuracy is more than AgiSoft Photoscan, but in forest mountain, it is vice versa. However, the process and mapping accuracy to generate TDOI require further improvement. [ABSTRACT FROM AUTHOR]

Mine development requires continuous management because it causes rapid topographic changes and environmental damage. Drones can be used to produce three-dimensional spatial information by quickly and accurately photographing areas that are difficult or dangerous for humans to approach. In this study, we investigated the possibility of using drone photogrammetry for determining changes and recovery in mines. The accuracy of the drone photogrammetry results was analyzed using checkpoints, and the earthwork volume was calculated and compared with that obtained through a field survey. We determined whether the results were consistent with the mountain recovery plan using drone images. The RMSE was 0.085–0.091 m in the plane and 0.121–0.128 m along the elevation, as determined by analyzing the checkpoint accuracy by creating an orthoimage and a digital surface model based on the drone images; these results satisfy the tolerance range of the 1/1000 digital map descriptions. The drone photogrammetry generated an average error of 11.9% using the conventional measurement method. The possibility of use was proved by confirming the vegetation and rock prevention nets using photographed images. The usability of drone photogrammetry in mines is expected to increase if substantial spatial information is produced and analyzed in the future. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2% for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.

Three-dimensional (3D) surveying of a construction site using an image-based method may produce incorrect ground elevation results at vegetation-covered regions, because the light rays are reflected on the surface of vegetation in front of the "truth" ground. This paper presents a convolutional neural network (CNN) method to identify and locate static vegetation using drone-based high-resolution orthoimages. The developed CNN-based image classification models are supplemented with an overlapping disassembling algorithm to generate 8×8-pixel , 16×16-pixel , 32×32-pixel , or 64×64-pixel small-patches as model inputs. The training datasets are 10 pairs of 1,536×1,536-pixel orthoimage and label-image dataset. Experimental results show that cropping a high-resolution image into 9,025 overlapped 32×32-pixel small-patches (with a site size of 17.28×17.28 cm2) for image classification, and assembling the small-patch label-image predictions to a patch-wise label-image prediction, has the average pixel accuracy of 92.6% in identifying objects on the experimental site. In addition, a vegetation-removing algorithm is designed to divide the label-image prediction into 36,864 nonoverlapping 8×8-pixel patches and traverse them in 192 row-loops and 191 column-loops. The testing results show vegetation in label-images are modified with the "truth" ground elevation and verified with two datasets obtained on different dates. In addition, the measured elevation differentials are close to the measured vegetation heights on the experimental site. This research has advanced the drone-based orthoimaging method in construction site surveying, which can automatically identify the static obstacles and determine the ground elevations more accurately. Furthermore, an approach of using a CNN model to segment a construction site has been proven feasible. [ABSTRACT FROM AUTHOR]

Using deep learning to recover depth information from a single image has been studied in many situations, but there are no published articles related to the determination of construction site elevations. This paper presents the research results of developing and testing a deep learning model for estimating construction site elevations using a drone-based orthoimage. The proposed method includes an orthoimage-based convolutional neural network (CNN) encoder, an elevation map CNN decoder, and an overlapping orthoimage disassembling and elevation map assembling algorithm. In the convolutional encoder-decoder network model, the max pooling and up-sampling layers link the orthoimage pixel and elevation map pixel in the same coordinate. The experiment data sets are eight orthoimage and elevation map pairs (1,536×1,536 pixels), which are cropped into 64,800 patch pairs (128×128 pixels). Experimental results indicated that the 128×128-pixel patch had the best model prediction performance. After 100 training epochs, 21.22% of the selected 2,304 points from the testing data set were exactly matched with their ground truth elevation values; and 52.43% points were accurately matched in ±5 cm and 66.15% points in ±10 cm , less than 10% points exceeded ±25 cm. This research project advanced drone applications in construction, evaluated CNNs' effectiveness in site surveying, and strengthened CNNs to work with large-scale construction site images. [ABSTRACT FROM AUTHOR]

Orthoimages are differentially rectified images that are corrected for the distortions caused especially by image tilt and topographic relief. The orientation, digital elevation model (DEM) and off-nadir angle plays an important role in orthoimage accuracy. The orientation error mostly occurs due to the quality and distribution of the ground control points. In this study, an attempt has been made to model the remaining errors by keeping the orientation error constant. To model the accuracy, orthoimages are produced with eight DEMs having different accuracies and are assessed using 50 check points. As the theoretical model cannot reflect the real world exactly, an empirical model is used for estimating the orthoimage accuracy. This proposed model was validated by another dataset. It is concluded that statistically there is no significant difference between the calculated model and real planimetric errors. The proposed model can be used in predicting orthoimage accuracy provided that the DEM accuracy and off-nadir angles of the points are known. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The distributions and characteristics of coseismic surface ruptures are the key to understand fault behaviors. However, traditional field investigations could be time consuming while toppled buildings and infrastructures may be subject to repairs due to rescue missions and transportation recovery within a few days after the earthquake, leading to urgent needs for rapid, extensive surveys before surface breakages are modified. Therefore, we take advantage of UAS photogrammetry to collect aerial images and to map surface fractures for the Mw 6.4 Hualien, eastern Taiwan, earthquake on 6 February 2018. The earthquake struck Hualien City and generated dense surface breakages over a distance of ~8 km. Together with control surveys for ground control points by using RTK-GPS, UAS photogrammetry uses the methods of the Structure from Motion and Multi-View Stereo to produce high-resolution orthoimages and digital surface models. Our mapping results show that surface ruptures follow the trace of the Milun fault and northern Linding fault. Mapped surface ruptures are usually in en échelon arrays or distributed fractures rather than a through-going fault. Estimated sinistral offset based on orthoimages is more than 60 cm in Chihsingtan and decreases toward south. The fault behavior of the 2018 Hualien earthquake shows along-strike variations and is somewhat different from the October 1951 earthquake. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A new method allows distortion free vegetation cover imagery to be collected. • 4 m2 area plots are rapidly imaged using only a handheld camera. • Utilising object based image analysis a classification accuracy of 81% is achieved. • Inclusion of a vegetation height image band improves overall classification accuracy. Abstract Percent vegetation cover is important variable used in understanding ecosystem processes, vegetation health and productivity. Downward looking images captured using a handheld camera have been demonstrated as a viable option for rapidly capturing in situ information to assess vegetation cover. This technique, however, is prone to perspective distortions biasing cover estimates towards taller vegetation elements. In this paper we present a new approach to generate imagery for use in vegetation cover estimation utilising multiple overlapping photographs and structure from motion algorithms to produce a 3D point cloud representation of the target plot. This point cloud is then converted into an orthoimage consisting of four bands—red, green, blue and vegetation height—which is free from perspective distortions. The approach is trialled in two Eucalypt forests in South Eastern Australia to produce an estimate of change in cover of all vegetation elements following a prescribed burn. Orthoimages are generated with 2.5 mm resolution and classified using object-based image analysis and random forests into broad vegetation and fuel classes. Utilising this approach an overall classification accuracy of 81% is achieved with the resulting estimates of cover agreeing with visual point based interpretation to within 6% across all classes. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) modelling of archaeological sites has recently undergone a great development due to the implementation of new acquisition devices and techniques and new processing algorithms. One of the most noteworthy of these is the use of unmanned aerial systems for lifting acquisition devices such as digital cameras to model sites based on photogrammetric techniques. Despite the development and wide use of these systems there are some situations where they are not allowed to fly. In this context we propose a method based on masts which elevate the camera in order to perform photogrammetric surveys of large archeological sites. In this study we analysed the photograph acquisition parameters and the surveying procedure in order to balance in‐field the efficiency and the complete coverage of photographs in the study zone, taking into account the requirements of the photogrammetric processing technique to be implemented. The proposed method has been applied to a large and complex archaeological site located in Aswan (Egypt). The results obtained (photogrammetric products and the 3D model) have demonstrated the viability of the method in these cases and the possible implementation of these types of studies for documentation, planning, virtual visualization, etc. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a fully automatic framework for the generation of so-called LiDAR orthoimages (i.e. 2D raster maps of the reflectance and height LiDAR samples) from ground-level LiDAR scans. Beyond the Digital Surface Model (DSM or heightmap) provided by the height orthoimage, the proposed method cost-effectively generates a reflectance channel that is easily interpretable by human operators without relying on any optical acquisition, calibration and registration. Moreover, it commonly achieves very high resolutions (1cm 2 per pixel), thanks to the typical sampling density of static or mobile LiDAR scans. Compared to orthoimages generated from aerial datasets, the proposed LiDAR orthoimages are acquired from the ground level and thus do not suffer occlusions from hovering objects (trees, tunnels and bridges), enabling their use in a number of urban applications such as road network monitoring and management, as well as precise mapping of the public space e.g. for accessibility applications or management of underground networks. Its generation and usability however faces two issues : (i) the inhomogeneous sampling density of LiDAR point clouds and (ii) the presence of masked areas (holes) behind occluders, which include, in a urban context, cars, tree trunks, poles or pedestrians (i) is addressed by first projecting the point cloud on a 2D-pixel grid so as to generate sparse and noisy reflectance and height images from which dense images estimated using a joint anisotropic diffusion of the height and reflectance channels. (ii) LiDAR shadow areas are detected by analyzing the diffusion results so that they can be inpainted using an examplar-based method, guided by an alignment prior. Results on real mobile and static acquisition data demonstrate the effectiveness of the proposed pipeline in generating a very high resolution LiDAR orthoimage of reflectance and height while filling holes of various sizes in a visually satisfying way. Highlights • Fully automatic pipeline for Orthoimage and Digital Surface Model generation from LiDAR point clouds. • Joint reflectance and height diffusion and inpainting methodology. • Generated orthoimages do not suffer occlusions from hovering objects and reach subcentimetric accuracy with modern MMS. [ABSTRACT FROM AUTHOR]

Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2% for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes. [ABSTRACT FROM AUTHOR]

Cartosat-1 is a global, high resolution stereographic imaging mission to support enhanced applications in several areas of terrain mapping, natural resources management, disaster management, infrastructure and development planning. A collaborative project of generating statewide Digital Elevation Model (DEMs) and mosaic of Ortho-image for all the states and union territories in India has completed under the project namely Space based Information Support for Decentralized Planning (SIS-DP) using Photogrammetric techniques with Cartosat-1 stereo data. Approximately 11000 stereo pairs of Cartosat-1 data were used in this process. Photogrammetric blocks for each state were processed using existing reference tiles and accordingly ortho-images were generated. The paper outlines the methodology for generating state-wide Digital Elevation Models (DEMs) and ortho-images. The guidelines that govern the quality of the output were discussed. Dissemination mechanism via public accessible web platform was described.

The geometric accuracy of 2008 AVHRR orthoimages from Metop-A, NOAA-17 and NOAA-18 satellites over Switzerland have been investigated here. The methods employed in the study are fully automated, with an accuracy of 0.1–0.2 pixels, however, blunders do occur and this requests a careful blunder detection approach. The investigations include analysis of relative, absolute and band-to-band registration (BBR) accuracy. Regarding relative accuracy, thousands of points are matched between Metop-A, NOAA-17 and NOAA-18 images of the same day. The accuracy is quite high with mean shifts between 0.2 and 0.4 pixels. Systematic stripes have been observed when NOAA-18 images are involved in matching. In spite of many efforts to find the source of this error, no explanation could be found. In addition, large shifts up to 2.9 pixels on some days between September and December 2008 were observed. Regarding absolute accuracy, digitized lakes as reference polygons have been used and a subpixel lake matching method has been applied. The mean shifts generally fulfilled EUMETSAT and GCOS specifications, although some partial results exceed them, especially for Metop-A. Regarding BBR accuracy, six multispectral bands have been compared, also with point matching. The EUMETSAT specification is 0.1 km, however, this specification refers to original images, not orthoimages. Taking also into account the matching errors of 0.1 km, the EUMETSAT specifications are in principle fulfilled in all cases except matching of Metop-A and NOAA-17 Band-2 images with Bands 4 and 5. The overall work showed that although, in general, accuracies are high and fulfill specifications, errors exceeding the specifications can occur and vary depending on the satellite used, time and location. Such errors influence subsequent geometric or thematic processing; thus, an automated and permanent quality control of such images should be executed.

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water authorities are required to have a survey of large woody debris (LWD) in river channels and to manage this aspect of the stream habitat, making decisions on removing, positioning or leaving LWD in a natural state. The main objective of this study is to develop a new methodology that assists in decision making for sustainable management of river channels by using generated low-cost, geomatic products to detect LWD. The use of low-cost photogrammetry based on the use of economical, conventional, non-metric digital cameras mounted on low-cost aircrafts, together with the use of the latest computational vision techniques and open-source geomatic tools, provides useful geomatic products. The proposed methodology, compared with conventional photogrammetry or other traditional methods, led to a cost savings of up to 45%. This work presents several contributions for the area of free and open source software related to Geographic Information System (FOSSGIS) applications to LWD management in streams, while developing a QGIS [1] plugin that characterizes the risk from the automatic calculation of geometrical parameters.

This article presents a methodology to process information from a Terrestrial Laser Scanner (TLS) from three dimensions (3D) to two dimensions (2D), and to two dimensions with a color value (2.5D), as a tool to document and analyze heritage buildings. Principally focused on the loss of material in stone, this study aims at creating an evaluation method for loss control, taking into account the state of conservation of a building in terms of restoration, from studying the pathologies, to their identification and delimitation. A case study on the Cathedral of the Seu Vella de Lleida was completed, examining the details of the stone surfaces. This cathedral was affected by military use, periods of abandonment, and periodic restorations. [ABSTRACT FROM AUTHOR]

In this paper, a developed low-cost system is described, which aims to facilitate 3D documentation fast and reliably by acquiring the necessary data in outdoor environment for the 3D documentation of façades especially in the case of very narrow streets. In particular, it provides a viable solution for buildings up to 8-10m high and streets as narrow as 2m or even less. In cases like that, it is practically impossible or highly time-consuming to acquire images in a conventional way. This practice would lead to a huge number of images and long processing times. The developed system was tested in the narrow streets of a medieval village on the Greek island of Chios. There, in order to by-pass the problem of short taking distances, it was thought to use high definition action cameras together with a 360° camera, which are usually provided with very wide-angle lenses and are capable of acquiring images, of high definition, are rather cheap and, most importantly, extremely light. Results suggest that the system can perform fast 3D data acquisition adequate for deliverables of high quality. [ABSTRACT FROM AUTHOR]

The wide availability of high-resolution satellite stereo images has created a surging demand for effective stereo image retrieval methods. Recently, few retrieval methods have been designed specifically for stereo images having unique characteristics (e.g., viewing number and viewing angles), and often have insufficient retrieval accuracy. A new content-based stereo image retrieval method is achieved with height and planar visual word pairs, which are generated from the stereo extracted digital surface models and orthoimages. Experimental results of the International Society for Photogrammetry and Remote Sensing stereo benchmark test data set show that our method outperforms the state-of-the-art methods in terms of accuracy and stability. Our method achieves a high retrieval precision of 0.9, and has a high efficiency. Our method is stable for two stereo pairs, covering the same scene from different sensors, which usually have a small ranking difference in the returned ranking list. Our method is helpful to quickly and accurately locate desired stereo images from large quantities of multisensor stereo images. [ABSTRACT FROM PUBLISHER]

Since the new millennium, living in historic cities has become extremely popular in the Netherlands. As a consequence, historic environments are being adapted to meet modern living standards. Houses are constantly subjected to development, restoration and renovation. Although most projects are carried out with great care and strive to preserve and respect as much historic material as possible, nevertheless a significant amount of historical fabric disappears. This puts enormous pressure on building archaeologists that struggle to rapidly and accurately capture in situ authentic material and historical evidence in the midst of construction works. In Leiden, a medieval city that flourished during the seventeenth century and that today counts over 3,000 listed monuments, a solution to the problem has been found with the implementation of advanced recording techniques. Since 2014, building archaeologists of the city council have experienced first-hand that new recording techniques, such as laser scanning and photogrammetry, have dramatically decreased time spent on site with documentation. Time they now use to uncover, analyse and interpret the recovered historical data. Nevertheless, within building archaeology education, a strong case is made for hand drawing as a method for understanding a building, emphasising the importance of close observation and physical contact with the subject. In this paper, the use of advanced recording techniques in building archaeology is being advocated, confronting traditional educational theory with practise and research tradition with the rapid rise of new recording technologies. [ABSTRACT FROM AUTHOR]

In Sahel-Doukkala, which is characterized by lands of a relatively low relief, global DEMs and DEMs generated from digitizing topographic maps, have been the primary source of several multidisciplinary researches. Although these products present a great value of the conducted research, the level of the given accuracy is not sufficient enough for detailed geospatial analysis. These requirements led us to generate a high-resolution DEM as an alternative of available global DEMs or/and DEMs generated from digitizing topographic maps. In this study, we present a workflow to extract high-resolution DEM at 5 m resolution and derived orthoimages from ALOS-PRISM data over Sahel-Doukkala, through photogrammetric techniques, using a variation of GCPs obtained from topographic maps at scale 1:25,000. The accuracy of the generated products is reported according to NSSDA standards. Using ten GCPs, a PRISM-DEM with 3.88 m vertical accuracy and 11.60 m horizontal accuracy, both at 95% confidence level is obtained. This DEM will serve as base dataset for further detailed geospatial analysis and mapping applications in order to identify the relationship between surface parameters and groundwater, and also to assess and understand all factors influencing the development of karst landscapes and consequently subsurface stability in the investigated area. [ABSTRACT FROM AUTHOR]

It is important nowadays to underline some relevant topics concerning the effective contribution of 3D high detailed products derived from innovation and integration of Geomatics technologies, allowing a remarkable development in descriptive metric capabilities, supporting and improving the material recording, representation, analysis and characterization about alteration of the constructive systems. Considering the relevance of the complex interdisciplinary research of these issues that move around the Cultural Heritage safeguard and due to its extreme vulnerability, these models must give a response to different problems. Primarily they has to provide complete models on which to pursue accurate morpho-dimensional documentation, and to base structural assessment, decay investigations, and consequently to underpin restoration practices and support operational workflow in CH assets monitoring. Some peculiarities of new methods for semiautomatic processing algorithms are thus evidenced, advantaging their proficiency to behave as tools for a more sustainable approach in the general process of preservation and protection. Specifically about the ancient masonries documentation, the chance of using digital products derived from very high scale models, as the detailed orthoimages projection and surfaces development offers many opportunities. Here, a late-medieval stratified dovecote tower in Verolengo (TO) with a particular trunk-conical shape had been analysed in order to reconstruct an identity and a historical and architectural framework, de facto not recognized yet. A 3D reconstruction by dense matching techniques will be presented, in the complex context that are the vertical high buildings, presenting one of the highest level of vulnerability. The importance of the 3D model availability, closely connected to dense radiometric information, has been particularly expressed in two main direction for the diagnosis both of volumetric structure assessment and the material characterization of the mixed masonries walls. [ABSTRACT FROM AUTHOR]

The main aim of this research is to investigate the impact of vertical accuracy of digital elevation models (DEMs) on the orthoimage production process. The study took place on a QuickBird image by using four different DEMs with different vertical accuracies, nine ground control points (GCPs), and three sensor models. The four DEMs are global positioning system (GPS)-based DEM, topographic map-based DEM, Shuttle Radar Topography Mission-based DEM, and advanced spaceborne thermal emission and reflection-based DEM. The used sensor models are the rigorous sensor model, rational functional model (RFM), and refined RFM. The results show that the REM without GCPs gives inaccurate results especially in case of high spatial resolution satellite images. The research recommends using the rigorous model and the refined RFM for orthorectifying images accurately as the results showed that these two models give accurate, precise, and stable results. As well, their results do not contain systematic errors. [ABSTRACT FROM AUTHOR]

Use of digital images is expanding as a tool for glacier monitoring, and small-format time-lapse cameras are increasingly being used for glacier monitoring of fast-flowing glaciers. Stereoscopic imagery is preferable since it yields direct displacement results but stereo photogrammetry has more requirements regarding geometry in set-up and control points, as well as the additional cost of another complete camera system. We investigate a combination of methods to achieve satisfactory control of accuracy with resulting significant day-to-day velocity variations ranging from 1.5–4 m day−1 made at a distance of 2 km. Validation of results was made by comparing different methods, partly using the same image material, but also in combination with aerial and satellite images. Monoscopic results can also be used to gain continuity in a stereo data set when geometry or visibility is poor. We also explore the use of ordinary photographs taken from airliners for compilation of orthoimages as a potential low cost method for detection of sudden changes. The method, showing some tens of metres accuracy, was verified for monitoring velocities and front positions during a glacier surge and was also used to validate monoscopic time-lapse images.

To generate an orthoimage product, mosaicking is a necessary process, and seam-based mosaicking of orthoimages is popular. However, many of these methods only focus on the generation of seamlines between two adjacent orthoimages, so the final generated mosaicking image depends on the order of compositing. To address this shortcoming, this paper presents an initial seamline network generation method based on improved seeded region growing. The basis of this method is the use of raster data rather than vector calculation, which is used with the area Voronoi diagrams with overlap (AVDO)-based method. First, the effective area of each image and overlap regions between adjacent images are determined. Then, the improved seeded region growing algorithm obtains the seamlines of each overlap region. The main improvement is that the boundary lines of overlap regions, rather than individual points, are chosen as seeds of the seeded region growing algorithm. These seeds grow simultaneously until growing regions overlap. The generated separatrix of growing regions is regarded as the seamline in the overlap region. At the same time, the cut result of the image’s effective area is obtained. After that, these generated cut images are intersected to generate the effective mosaic polygon (EMP) of the image. Finally, all generated EMPs are vectorized to form the initial seamline network. In this way, the proposed method can process any kind of overlap region, and the final generated seamline network has no relation to the order of the image compositing. The experimental results demonstrate that the presented method is feasible and can achieve higher accuracy than the previous AVDO-based method.

In the article, the application of stereopairs of panchromatic and multispectral SPOT satellite imagery in photogrammetric applications is presented. Theoretical basis of the generation of digital elevation models and orthoimages and a description of the practical processing of orthoimages are given. Examined are the possibilities of the application of the middle-resolution images from mountainousregions. The accuracy of the orthoimages has been determined by comparing with orthophotos DOF 5. The results are unexpectedly good and show the potential of SPOT imagery. The accuracy of the resultshas been, in addition to the input data, affected by the restrictions of the digital, automatic processing techniques in the software applied.

This paper evaluates the geometric effects of pansharpening algorithms on automatically generated DSMs and thus on the resulting orthoimagery through a quantitative assessment of the accuracy on the end products. The main motivation was based on the fact that for automatically generated Digital Surface Models, an image correlation step is employed for extracting correspondences between the overlapping images. Thus their accuracy and reliability is strictly related to image quality, while pansharpening may result into lower image quality which may affect the DSM generation and the resulting orthoimage accuracy. To this direction, an iterative methodology was applied in order to combine the process described by Agrafiotis and Georgopoulos (2015) with different pansharpening algorithms and check the accuracy of orthoimagery resulting from pansharpened data. Results are thoroughly examined and statistically analysed. The overall evaluation indicated that the pansharpening process didn't affect the geometric accuracy of the resulting DSM with a 10m interval, as well as the resulting orthoimagery. Although some residuals in the orthoimages were observed, their magnitude cannot adversely affect the accuracy of the final orthoimagery. [ABSTRACT FROM AUTHOR]

An orthoimage is one of the basic photogrammetric products used for architectural documentation of historical objects; recently, it has become a standard in such work. Considering the increasing popularity of photogrammetric techniques applied in the cultural heritage domain, this research examines the two most popular measuring technologies: terrestrial laser scanning, and automatic processing of digital photographs. The basic objective of the performed works presented in this paper was to optimize the quality of generated high-resolution orthoimages using integration of data acquired by a Z+F 5006 terrestrial laser scanner and a Canon EOS 5D Mark II digital camera. The subject was one of the walls of the "Blue Chamber" of the Museum of King Jan III's Palace at Wilanów (Warsaw, Poland). The high-resolution images resulting from integration of the point clouds acquired by the different methods were analysed in detail with respect to geometric and radiometric correctness. [ABSTRACT FROM AUTHOR]

The detection of orthoimage mosaicking seamlines by means of wavelet transform was examined. Radiometric alignment was omitted, giving priority to the issue of seamlines which bypass locations where there is a parallax between orthoimages. The importance of this issue is particularly relevant for images with very high resolution. In order to create a barrier image between orthoimages, the redundant wavelet transform variant known as MODWT-MRA was used. While more computationally complex than the frequently used DWT, it enables very good multiresolution edge detection. An IT prototype was developed on the basis of the described concept, and several cases of seamline detection were tested on the basis of data with a resolution of 10 cm to 1 m. The correct seamline location was obtained for each test case. This result opens the door to future expansion of the radiometric alignment method, which is also based on wavelets. [ABSTRACT FROM AUTHOR]

Textured three dimensional models are currently the one of the standard methods of representing the results of photogrammetric works. A realistic 3D model combines the geometrical relations between the structure's elements with realistic textures of each of its elements. Data used to create 3D models of structures can be derived from many different sources. The most commonly used tool for documentation purposes, is a digital camera and nowadays terrestrial laser scanning (TLS). Integration of data acquired from different sources allows modelling and visualization of 3D models historical structures. Additional aspect of data integration is possibility of complementing of missing points for example in point clouds. The paper shows the possibility of integrating data from terrestrial laser scanning with digital imagery and an analysis of the accuracy of the presented methods. The paper describes results obtained from raw data consisting of a point cloud measured using terrestrial laser scanning acquired from a Leica ScanStation2 and digital imagery taken using a Kodak DCS Pro 14N camera. The studied structure is the ruins of the Ilza castle in Poland. [ABSTRACT FROM AUTHOR]

The paper gives an overview on the problems occurring during commercial digital orthophoto (DOP) production. There is a wide variety in requirements on orthophotos. It is no problem to produce good orthophotos in small areas, whereas production of good and homogeneous orthophotos in large areas remains a challenge. Therefore, we discuss large area orthophotos based on regular areal flight with regular flight parameters and nadir images, not on large numbers of images creating orthophotos based on various oblique images. The focus of the paper is to optimize the process of line production of orthophotos to achieve the best results under minimum usage of resources. Beside improvements in commercial software, the improvement in process design can help to achieve this objectives. It is necessary the attach importance to the different quality aspects to invest the limited resources at the most profitable process step. The requirements on orthophotos can be grouped into three categories: geometric, radiometric and aesthetic requirements. The main intention is to generate correct and well interpretable orthoimages, putting attention to geometry and radiometry. Many requirements cause enormous expenditures in flight time, number of images and amount of interactive editing work. Among the geometric aspects, the accuracy is the prominent factor. The geometric quality of the underlying digital terrain model (DTM) is of outstanding importance. Depending on the used focal length during acquisition and overlap, geometric errors in the DTM are directly represented in the orthophoto. Particularly at manmade surface break lines, even horizontal DTM errors occur striking in the orthophotos. A special consideration is to put on objects outside the DTM surface. While buildings may be represented in a good manner in a digital surface models (DSM) to generate so called "true orthophotos", there is no way to represent vegetation, cables, poles etc. in an appropriate manner in any DSM. Seam lines have to disappear in the mosaics. There should be no indication for the detection of the used seam lines nether in geometric nor in radiometric edges. The radiometric aspect include a good distribution of the color values in the histogram, good contrasts should become quantified. It is impossible to measure the aesthetic quality, but is has to be homogeneous over the whole project area without eye-catching artefacts. In the conclusion, we want to give to the producers of orthophotos and their clients a list of criteria and quality figures to be agreed on before the production starts to avoid later discussions. [ABSTRACT FROM AUTHOR]

For several years there has been an increasing interest in the use of unmanned aerial vehicles in acquiring image data from a low altitude. Considering the cost-effectiveness of the flight time of UAVs vs. conventional airplanes, the use of the former is advantageous when generating large scale accurate ortophotos. Through the development of UAV imagery, we can update largescale basic maps. These maps are cartographic products which are used for registration, economic, and strategic planning. On the basis of these maps other cartographic maps are produced, for example maps used building planning. The article presents an assessesment of the usefulness of orthophotos based on UAV imagery to upgrade the basic map. In the research a compact, nonmetric camera, mounted on a fixed wing powered by an electric motor was used. The tested area covered flat, agricultural and woodland terrains. The processing and analysis of orthorectification were carried out with the INPHO UASMaster programme. Due to the effect of UAV instability on low-altitude imagery, the use of non-metric digital cameras and the low-accuracy GPS-INS sensors, the geometry of images is visibly lower were compared to conventional digital aerial photos (large values of phi and kappa angles). Therefore, typically, low-altitude images require large along- and across-track direction overlap - usually above 70 %. As a result of the research orthoimages were obtained with a resolution of 0.06 meters and a horizontal accuracy of 0.10m. Digitized basic maps were used as the reference data. The accuracy of orthoimages vs. basic maps was estimated based on the study and on the available reference sources. As a result, it was found that the geometric accuracy and interpretative advantages of the final orthoimages allow the updating of basic maps. It is estimated that such an update of basic maps based on UAV imagery reduces processing time by approx. 40%. [ABSTRACT FROM AUTHOR]

The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts. [ABSTRACT FROM AUTHOR]

This article presents a methodology to process information from a Terrestrial Laser Scanner (TLS) from three dimensions (3D) to two dimensions (2D), and to two dimensions with a color value (2.5D), as a tool to document and analyze heritage buildings. Principally focused on the loss of material in stone, this study aims at creating an evaluation method for loss control, taking into account the state of conservation of a building in terms of restoration, from studying the pathologies, to their identification and delimitation. A case study on the Cathedral of the Seu Vella de Lleida was completed, examining the details of the stone surfaces. This cathedral was affected by military use, periods of abandonment, and periodic restorations.