Showing papers in "The international archives of the photogrammetry, remote sensing and spatial information sciences in 2022"

Integrated processing of photogrammetric and laser scanning data for frescoes restoration

Abstract: Abstract. The integration of photogrammetry and Terrestrial Laser Scanner (TLS) techniques is often desirable for Cultural Heritage digitization, especially when high metric and radiometric accuracy is required, as for the documentation and restoration of frescoed spaces. Despite the many technological and methodological advances in both techniques, their full integration is still not straightforward. The paper investigates a methodology where TLS and photogrammetric data are processed together through an image matching process between RGB panoramas acquired by the scanner’s integrated camera and frame imagery acquired through photographic equipment. The co-registration is performed without any Ground Control Point (GCP) but using the automatically extracted tie points and the known Exterior Orientation parameters of the panoramas (gathered from TLS data original registration) to set the ground reference. The procedure allowed for effective integrated processing with the possibility of take benefit from TLS and photogrammetry pros and demonstrated to be reliable even with low overlap between photogrammetric images.

An image-based deep learning workflow for 3d heritage point cloud semantic segmentation

Abstract: Abstract. The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.

Application of uav swarm semi-autonomous system for the linear photogrammetric survey

Abstract: Abstract. Unmanned Aerial Vehicles (UAVs) are used as stand-alone systems for a variety of purposes from agriculture, and environmental monitoring through architecture, and construction to humanitarian missions. The advantage of UAV is high spatial and temporal resolution but on the other hand, the disadvantage is the small area of cover and time demanding data collection. As a result of technological advancements, the complexity of systems to unprecedented levels these disadvantages can be solved by UAV Swarm systems. A UAV Swarm system is defined as the utilization of more than one UAV that are cooperating together in a semi-autonomous or autonomous manner to achieve a common goal. There are numerous factors in play while designing a system as advanced as a UAV Swarm. In our experiment, we focused on the semi-autonomous concept of creating and deploying a UAV Swarm with three Small UAVs in master-slave architecture for high-resolution fine-scale mapping. We demonstrate the implementation of collective behaviour of UAV swarm for river bed mapping that considers all on-board systems, including high resolution georeferenced aerial photography and navigation using high accuracy GPS. The testing field for this study was a 13.3 ha linear area of Solani River in the Haridwar district of the state of Uttarakhand, India. Images were captured by all three UAVs (one leader and two followers) and 5 ground control points (GCP) were used for geo-referencing. Aerial Triangulation and Bundle Block adjustment were processed by photogrammetric software Pix4DMapper. This UAV swarm mapping concept generates standard accurate geospatial results of 1.24 cm GSD and RMS Error 0.023 meter. Assessing the proposed system's efficiency and accuracy after such processes are taken into account reduces the time and cost manifolds of the UAV surveying.

Handheld fisheye multicamera system: surveying meandering architectonic spaces in open-loop mode – accuracy assessment

Abstract: Abstract. The task of digitalizing meandering complex spaces in 3D is a challenging one even with the most advanced instrumentation like lightweight terrestrial laser scanner or portable/wearable Mobile Mapping Systems (MMSs). The complexity and extension of architectonic spaces such as staircases, corridors and passages are such that the acquisition time using static devices becomes prohibitive and the accuracy using mobile devices gets affected by drift error leading to warped models or requiring abundant control measurements. This paper presents a photogrammetric portable fisheye multicamera solution for the 3D survey of complex areas that aims at being both handy and fast in the acquisition as well as more reliable ad accurate than common MMSs. The paper showcases a stress test conducted on five complex reconstruction trajectories selected from the meandering connection passages of Milan’s Cathedral. The tests are constructed as worst-case scenario to evaluate the accuracy and drift error amount of the proposed system in open-ended unconstrained paths. The results, though still suffering from moderate drift error, highlights the potential of the solution, especially in retaining the overall shape and orthogonality of the architectonic elements acquired.

Handling critical aspects in massive photogrammetric digitization of museum assets

Abstract: Abstract. In recent years, a growing interest in the 3D digitisation of museum assets has been pushed by the evident advantages of digital copies in supporting and advancing the knowledge, preservation and promotion of historical artefacts. Realising photo-realistic and precise digital twins of medium and small-sized movable objects implies several operations, still hiring open research problems and hampering the complete automation and derivation of satisfactory results while limiting processing time. The work examines some recurrent issues and potential solutions, summing up several experiences of photogrammetric-based massive digitisation projects. In particular, the article presents some insights into three crucial aspects of the photogrammetric pipeline. The first experiments tackle the Depth of Field (DoF) problem, especially when digitising small artefacts with macro-lenses. On the processing side, two decisive and time-consuming tasks are instead investigated: background masking and point cloud editing, exploring and proposing automatic solutions for speeding up the reconstruction process.

Geometric primitive extraction from semantically enriched point clouds

Abstract: Abstract. 3D point clouds are robust representations of real-world objects and usually contain information about the shape, size, position and radiometry of the scene. However, unstructured point clouds do not directly exploit the full potential of such information and thus, further analysis is commonly required. Especially when dealing with cultural heritage objects which are, typically, described by complex 3D geometries, semantic segmentation is a fundamental step for the automatic identification of shapes, erosions, etc. This paper focuses on the efficient extraction of semantic classes that would support the generation of geometric primitives such as planes, spheres, cylinders, etc. Our semantic segmentation approach relies on supervised learning using a Random Forest algorithm, while the geometric shapes are identified and extracted with the RANSAC model fitting algorithm. In this way the parametric modelling procedure in a HBIM environment is easily enabled. Our experiments show the efficient label transferability of our 3D semantic segmentation approach across different Doric Greek temples, with qualitatively and quantitatively evaluations.

Image orientation with a hybrid pipeline robust to rotations and wide-baselines

Abstract: Abstract. The extraction of reliable and repeatable interest points among images is a fundamental step for automatic image orientation (Structure-From-Motion). Despite recent progresses, open issues in challenging conditions - such as wide baselines and strong light variations - are still present. Over the years, traditional hand-crafted methods have been paired by learning-based approaches, progressively updating the state-of-the-art according to recent benchmarks. Notwithstanding these advancements, learning-based methods are often not suitable for real photogrammetric surveys due to their lack of rotation invariance, a fundamental requirement for these specific applications. This paper proposes a novel hybrid image matching pipeline which employs both hand-crafted and deep-based components, to extract reliable rotational invariant keypoints optimized for wide-baseline scenarios. The proposed hybrid pipeline was compared with other hand-crafted and learning-based state-of-the-art approaches on some photogrammetric datasets using metric ground-truth data. Results show that the proposed hybrid matching pipeline has high accuracy and appeared to be the only method among the evaluated ones able to register images in the most challenging wide-baseline scenarios.

A procedure to import a complex geometry model of a heritage building into bim for advanced architectural representations

Abstract: Abstract. The aim of this communication is to present the advantages and disadvantages of a Scan-to-BIM process applied to a heritage building in order to obtain advanced technical drawings to be used in the analysis and illustration of the project. The whole process described includes: the survey planification and data acquisition with a Terrestrial Laser Scanner; the processing and cleaning of the point cloud; the 3D mathematical modelling; a proposal for semi-automatic modelling of organic elements; and the import of the final model into a BIM environment. Rhinoceros (McNeel) and Revit (Autodesk) are the main programs used. The crucial aspect of this workflow is found at the moment of importing the geometrical model into Revit, having to accommodate the criteria of this program in terms of tolerances, geometric structure of the solids, incompatibilities with NURBS libraries, etc. The result is a BIM model divided into families and subcategories where visual attributes can be assigned per element, parameterized and other visual information can be added (orthophotographs, wireframe analysis drawings, etc.). In other words, a 3D model from which highly configurable advanced representations (plans, vertical sections, perspectives, isometric exploded view, etc.) can be obtained and with which to generate analyses from the field of Architectural Graphic Expression.

Comparison of leica blk360 and leica blk2go on chosen test objects

Abstract: Abstract. The contribution deals with the comparison of two laser scanners manufactured by Leica company. In BIM modelling, there is a need for fast and accurate gathering of spatial data, e.g. point clouds. Those data can be gathered by photogrammetry or laser scanning. Last years on the market, there occurred some light and easy-to-use alternatives to classic laser scanners. There were chosen two scanners that belong to the easy-to-use category. The first scanner is stationary Leica BLK360 and the second scanner is Leica BLK2GO which is a handheld scanner based on SLAM technology. Both laser scanners were tested on three different test objects. The first object is an administrative building, the second object is a historical administrative building and the third object is the vaults of the church. In all cases, only the indoor side of the objects was measured. The point clouds were compared to each other and the comparison was discussed. The parameters derived from the point clouds were also compared to the parameters read in the original documentation of the object. The comparison of the parameters may show, how those point clouds are usable for the final BIM modelling.

Integration of uav-lidar and uav-photogrammetry for infrastructure monitoring and bridge assessment

Abstract: Abstract. The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but time-consuming model, the UAV-LiDAR resulting point cloud’s georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5–10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.

3d digitization of transparent and glass surfaces: state of the art and analysis of some methods

Abstract: Abstract. In the field of industrial metrology, there is a rising need for 3D information at a very high resolution for micro-measurements and quality control of transparent objects such as glass bottles (beer, wine, cola, cosmetics, etc.). However, such objects are particularly challenging for optical-based 3D reconstruction methods and systems such as photogrammetry, photometric stereo, structured light scanning, laser scanning, typically resulting in poor metrological performances. Indeed, these methods require the surface of the object to diffusely reflect the incoming light, which is not the case with the glass material where refraction and absorption phenomena do not permit their use. Over the years, various methods have been investigated and developed to avoid the coating (or powdering) treatment often used to make transparent objects opaque and diffusely reflecting. Most of the approaches require either some a priori knowledge of the transparent object or assumptions about how light interacts with the surface. This paper provides a general overview of state-of-the-art 3D digitization methods for optically non-cooperative surfaces featuring absorption, scattering, and refraction. The paper reviews research works summarizing them into four categories including shape-from-X, direct ray measurements, hybrid, and learning-based approaches. Moreover, we provided some 3D results to better highlight the advantages and disadvantages of each method in practice when dealing with transparent objects.

Real-time positioning and tracking for vision-based unmanned underwater vehicles

Abstract: Abstract. Unmanned underwater vehicle (UUV) is a key technology for marine resource exploration and ecological monitoring. How to use vision-based active positioning and three-dimensional perception to realize UUV underwater autonomous navigation and positioning is the basis for UUV's underwater operations. The complexity and unstructured characteristics of seawater bring new challenges to vision-based underwater high-precision positioning. Traditional visual localization algorithms mainly include geometric-based visual localization algorithms (such as ORB-SLAM2) and deep learning-based visual localization algorithms (such as DXSLAM). In this paper, based on the typical marine environment (low brightness, dynamic fish interference, underwater light spot, high turbidity), the experimental analysis and comparison of different visual positioning methods of UUV is carried out, which provides a reference for realizing the real-time localization of UUV, and further provides a better solution for UUV underwater measurement and monitoring operations.

3d mapping of indoor and outdoor environments using apple smart devices

Abstract: Abstract. Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.

3d modelling of a historic windmill: ppk-aided terrestrial photogrammetry vs smartphone app

Abstract: Abstract. Cultural heritage (CH), what we inherited from the past generations, is a precious asset connecting the past to the present. It has many demonstrable benefits to nations around the world. For many countries, it has been a part of national identity as well as a key driver of the economy. However, CH is under constant threat of demolition due to wars, natural and human-induced hazards, and negligence. Therefore, documentation of CH has become very essential. Recent advancements in remote sensing technology have improved upon approaches for the surveying and structural modelling of the CH. This paper examines two close-range photogrammetry approaches in modelling a historic windmill. In the first approach, to generate a 3D model of the windmill, the images were obtained with a PPK-aided system and then processed through the Structure-from-Motion (SfM) method in Agisoft Metashape software. The second approach utilized a smartphone app both to capture the images and then generate the 3D model of the windmill with SfM. The 3D models of windmills, generated with two different methods, were compared in CloudCompare software using the cloud-to-mesh distance (C2M) tool. Two models were aligned with point pairs-picking for registration and the result showed that the models are quite similar and distance between the two models ranged from −5cm to +5cm.

The eurosdr time benchmark for historical aerial images

Abstract: Abstract. Automatic photogrammetric processing of historical (or archival) aerial photos is still a challenging task, particularly in cases of missing ancillary information, low radiometric and image quality, limited stereo coverage or large temporal span. However, with recent advances in photogrammetry and Artificial Intelligence (AI) algorithms for image processing and interpretation, an increasing number of applications are now feasible. The article presents the TIME (hisTorical aerIal iMagEs) benchmark (https://time.fbk.eu/), promoted by EuroSDR to explore the potential of historical aerial images. Realized in collaboration with various European NMCAs, the benchmark has garnered aerial image blocks and time series imagery captured since the 1950s. To support the photogrammetric processing of the digitized photos, ancillary data are supplied with available information about flight missions, taking cameras, and ground control points (GCPs). Several diverse investigations have been undertaken with the benchmark datasets, all captured over historical urban areas or landscapes. The paper describes the benchmark datasets and some potential research topics, presenting several tests and analyses realized with the collated and shared data.

3dcd: a new dataset for 2d and 3d change detection using deep learning techniques

Abstract: Abstract. Change detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on the use of deep learning algorithms. These kinds of algorithms are generally focused on generating two-dimensional (2D) change maps, thus they are only able to detect horizontal changes in land use/land cover, not considering nor returning any information on the corresponding elevation changes. Our work proposes a step forward, creating and sharing a dataset where two optical images acquired in different epochs are provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes. Particularly, our aim is to provide a dataset useful to address and possibly solve the change detection task in 3D. Indeed, the proposed dataset, on the one hand, can empower a further development of 2D change detection algorithms, and, on the other hand, can allow to develop algorithms able to provide 3D change detection maps from two optical images captured in different epochs, without the need to rely directly on elevation data as input. The proposed dataset is publicly available at the following link: https://bit.ly/3wDdo41.

Reliability of dtms obtained with mobile fast surveys techniques

Abstract: Abstract. The contribution addresses the issue of the integrated survey aimed at three-dimensional modeling for the documentation of different types of terrain through the analysis of two case studies located in the province of Pavia - Italy. The techniques of aerial photogrammetric acquisition SfM (UAVs), Terrestrial Laser Scanner (TLS) and Mobile (MLS) are now consolidated and widely used, managing to meet the needs of documentation of land levelling, monitoring, and analysis of landslide volumes. The two case studies present difficulties due to a strong inclination of the land and extensive presence of vegetation in the first case and to a strong presence of agricultural canalizations in the second case. The data processing phase focused on the comparison between MLS and close-range photogrammetry, while the acquisitions from TLS were used as control data. This acquisition method allows avoiding the process of approximation and reconstruction of the DTM under the vegetation, ensuring the correctness of the data relating to the ground course. The database allows the generation of highly reliable DTMs using specific point cloud modeling and processing software. Fast survey instruments are ideal in large areas or in hilly areas where sub-vertical sections and covered by vegetation are often present, difficult to detect only with close-range photogrammetry.

Deep learning for 3d building reconstruction: a review

Abstract: Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS). Nowadays 3D city models have become an essential part of 3D GIS environments and they can be used in many applications and analyses in urban areas. The conventional 3D building reconstruction methods depend heavily on the data quality and source; and manual efforts are still needed for generating the object models. Several tasks in photogrammetry and remote sensing have been revolutionized by using deep learning (DL) methods, such as image segmentation, classification, and 3D reconstruction. In this study, we provide a review on the state-of-the-art machine learning and in particular the DL methods for 3D building reconstruction for the purpose of city modelling using EO data. This is the first review with a focus on object model generation based on the DL methods and EO data. A brief overview of the recent building reconstruction studies with DL is also given. We have investigated the different DL architectures, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and the combinations of conventional approaches with DL in this paper and reported their advantages and disadvantages. An outlook on the future developments of 3D building modelling based on DL is also presented.

Deep learning: new approach for detecting scholar exams fraud

Abstract: Abstract. Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set.

Cloudtran: cloud removal from multitemporal satellite images using axial transformer networks

Abstract: Abstract. We present a method for cloud-removal from satellite images using axial transformer networks. The method considers a set of multitemporal images in a given region of interest together with the corresponding cloud masks, and delivers a cloud-free image for a specific day of the year. We propose the combination of an encoder-decoder model employing axial attention layers for the estimation of the low-resolution cloud-free image, together with a fully parallel upsampler that reconstructs the image at full resolution. The method is compared with various baselines and state-of-the-art methods on two Sentinel-2 datasets, showing significant improvements across multiple standard metrics used for image quality assessment.

Uav-based river plastic detection with a multispectral camera

Abstract: Abstract. Plastic is the third world’s most produced material by industry (after concrete and steel), but people recycle only 9% of plastic that they have used. The other parts are either burned or accumulated in landfills and in the environment, the latter being the cause of many serious consequences, in particular when considering a long-term scenario. A significant part the plastic waste is dispersed in the aquatic environment, having a dramatic impact on the aquatic flora and fauna. This motivated several works aiming at the development of methodologies and automatic or semi-automatic tools for the plastic pollution detection, in order to enable and facilitate its recovery. This paper deals with the problem of plastic waste automatic detection in the fluvial and aquatic environment. The goal is that of exploiting the well-recognized potential of machine learning tools in object detection applications. A machine learning tool, based on random forest classifiers, has been developed to properly detect plastic objects in multi-spectral imagery collected by an unmanned aerial vehicle (UAV). In the developed approach, the outcome is determined by the combination of two random forest classifiers and of an area-based selection criterion. The approach is tested on 154 images collected by a multi-spectral proximity sensor, namely the MAIA-S2 camera, in a fluvial environment, on the Arno river (Italy), where an artificial controlled scenario was created by introducing plastic samples anchored to the ground. The obtained results are quite satisfactory in terms of object detection accuracy and recall (both higher than 98%), while presenting a remarkably lower performance in terms of precision and quality. The overall performance appears also to be dependent on the UAV flight altitude, being worse at higher altitudes, as expected.

Comparison of photogrammetry tools considering rebar progress recognition

Abstract: Abstract. Construction progress monitoring is an important process throughout the project timeline towards its successful completion. Among imaging techniques, photogrammetry is considered as economical and effective method. However, few studies can be found on construction progress monitoring via photogrammetry; thus, not much guideline is available for this domain. This study evaluated the photogrammetry tools for the progress assessment of the rebar grid framework. Photogrammetry tools were evaluated and analysed following defined criteria, and Agisoft Metashape, and 3DF Zephyr were identified as better options. This study aims to provide a guideline to construction industry professionals and stakeholders towards the adoption of photogrammetric progress assessment for construction activities.

Structural line feature selection for improving indoor visual slam

Abstract: Abstract. Nowadays, Visual SLAM has gained ample successes in various scenarios. For feature-based system, it is still limited when running in an indoor room, as the indoor scene is often with few and simple texture which result in less and unevenly distributed point features. To solve this limitation, line features which are quite rich in an indoor scene are extracted and used. However, not all features can geometrically contribute to pose estimation, specifically, line features that are consistent to the motion direction provide only weak geometric constraint for solving pose parameters. Therefore, this paper proposes a selection method for reasonable line features, in particular, based on the Manhattan World Assumption (MWA), structural line features are firstly extracted instead of normal line features. Then, the structural line features are selected according to the direction information of vanishing points and selected for a stronger geometric constraint on pose estimation. In general, the selected structural lines require that the intersection angle between the corresponding principal direction and the camera motion direction is higher than a threshold, which is extensively investigated in the experiments. The experimental results show that, compared to the original ORB-SLAM2, the localization accuracy after using the proposed method can be improved by around 15%-40% on various public datasets, and the real-time performance can be basically guaranteed even including the extra time spent on the selection procedure.

Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban lulc features

Abstract: Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.

The recognition of heritage qualities from feature-based digital procedures in the analysis of historical urban contexts

Abstract: Abstract. The documentation of historical architectural heritage in urban contexts involves the consideration of planning adaptations of settlements and landscape, related to the identification of formal and semantic qualities. In particular, the identification of cultural significance of Heritage building units can find correspondence in geometrical features that are documented within the urban asset. In this way, urban monitoring, in an increasingly automated way, can support the identification and characterization of semantic elements also regarding Heritage objects, observing the invariance and conservation of formal constants in urban dynamic assets.Considering the experimental case study of Solikamsk historical center, belonging to Upper Kama route (Russia), a multi-instrumental strategy of spatial survey is applied, evaluating data coverages and resolutions. This analysis defines a preliminary framework to develop further processes of 3D triangulation and reality-based meshing. The morpho-metric detail of final models constitutes the basis for the computing test of feature-based procedures, including regions recognition and mesh segmentation, which can be calibrated for shape qualities and scales, reaching a preliminary modeling classification of Heritage and urban building units.

Determination of chlorophyll content in selected grass communities of krkonoše mts. tundra based on laboratory spectroscopy and aerial hyperspectral data

Abstract: Abstract. The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R2 for all indices was 0.52 for all species together at leaf level and median R2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

<i>IFCALIGNMENT</i> FOR RASTER-TO-VECTOR GIS RAILWAY CENTRELINE: A CASE STUDY IN THE SOUTH OF ITALY

Abstract: Abstract. Built environment Asset Management (AM) is evolving and renewing itself through the development of new technologies. Building Information Modelling (BIM) is the main methodology for the digitisation process of existing data and information. Although BIM was originally intended for buildings, in the last few years Infrastructure Building Information Modelling (I-BIM) and Civil Information Modelling (CIM) are emerging to manage civil infrastructure. The interaction of infrastructure with the surrounding environment is a fundamental aspect and it requires data-sharing between different sources and systems. Geographic Information Systems (GIS) is used to store and elaborate Earth’s surface information, and it is, therefore, necessary to achieve a complete BIM/GIS interoperability. This paper aims to test the popular BIM open-standard Industry Foundation Classes (IFC) capabilities and potentialities in storing GIS data. A case study of a disused railway in the south of Italy was used to test the methodology presented: rail-centreline (alignment) extraction from GIS raster data, and a conversion of the alignment to an IFCAlignment element. The possibility to export a rail alignment in IFC was confirmed.

Uav photogrammetry for metric evaluation of concrete bridge cracks

Abstract: Abstract. Monitoring cracks opening on concrete bridges is a key aspect for structural health assessment. Digital image processing, combined with Unmanned Aerial Vehicles (UAVs) and photogrammetry, allows for non-contact 3D reconstruction of cracks, reducing costs and potential unsafe factors involved in manual inspections. This paper presents a flexible procedure based on UAV photogrammetry for accurate evaluation of cracks geometry, that can be implemented for periodic structural monitoring. Stereo-pair of images, acquired with UAVs close to the cracked surface, are used to build a scaled photogrammetric model through Structure-from-Motion. Cracks are detected on images by image binarization and digital image processing techniques. Thereafter, one single image is used to reconstruct crack 3D geometry, by back-projecting crack image coordinates on a 3D model of the object. This can be built from the current stereo-pair of images, or based on an existing photogrammetric model, in the case of a periodic monitoring set-up. Crack width is accurately estimated in 3D world. The procedure is tested and evaluated in a case study, obtaining millimetric accurate results, which is in line with the average ground sample distance of the images employed. Results highlight the potentials of UAVs and photogrammetry not only for bridge inspections and damages localization, but also for accurately evaluating cracks geometry and helping structural engineers to assess structure health conditions.

Mapping vegetation and measuring the performance of machine learning algorithm in lulc classification in the large area using sentinel-2 and landsat-8 datasets of dehradun as a test case

Abstract: Abstract. In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s “Sentinel” and American Earth observation satellite” landsat” missions have been used in various remote sensing applications.Google Earth Engine (GEE) is such a tool that publicly allow the use of these available datasets, there is a large amount of available data in GEE, which are being used for computing and analysing purpose. In this article, we compare the classification performance of four supervised machine learning algorithms: Classification and Regression Tree (CART), Random forests (RF), Gradient tree boosting (GTB), Support vector machines (SVM). The study area is located at 30.3165° N, 78.0322° E near the Himalayan foothills, with four land-use land-cover (LULC) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 and LANDSAT-8 covering spring, summer, autumn, and winter conditions. Here we collected a total of 2084 sample points in which 536, 506, 505, 540 points belong to urban, water, forest and agriculture points respectively. which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, for accuracy measurement we use confusion and Cohen’s kappa calculation method.We have calculated CART (Accuracy 93.52% and Kappa coefficient 91.36%), Random Forest (Accuracy 95.86% and Kappa coefficient 94.48%),Gradient Tree Boost (Accuracy 95.33% and Kappa coefficient 93.37%),Support Vector Machine (Accuracy 73.54% and Kappa coefficient 76.28%) for Landsat 8 data sets and CART (Accuracy 89.24% and Kappa coefficient 85.64%), Random Forest (Accuracy 91.45% and Kappa coefficient 88.59%),Gradient Tree Boost (Accuracy 87.71% and Kappa coefficient 83.58%),Support Vector Machine (Accuracy 84.96% and Kappa coefficient 79.99%) for Sentinel2 data sets. Further analysis for accuracy and machine learning algorithm are discussed in result section.

Project indigo – document, disseminate & analyse a graffiti-scape

Abstract: Abstract. Graffiti is a short-lived form of heritage balancing between tangible and intangible, offensive and pleasant. Graffiti makes people laugh, wonder, angry, think. These conflicting traits are all present along Vienna's Donaukanal (Eng. Danube Canal), a recreational hotspot – located in the city's heart – famous for its endless display of graffiti. The graffiti-focused heritage science project INDIGO aims to build the basis to systematically document, monitor, and analyse circa 13 km of Donaukanal graffiti in the next decade. The first part of this paper details INDIGO's goals and overarching methodological framework, simultaneously placing it into the broader landscape of graffiti research. The second part of the text concentrates on INDIGO's graffiti documentation activities. Given the project's aim to create a spatially, spectrally, and temporally accurate record of all possible mark-makings attached in (il)legal ways to the public urban surfaces of the Donaukanal, it seems appropriate to provide insights on the photographic plus image-based modelling activities that form the foundation of INDIGO's graffiti recording strategy. The text ends with some envisioned strategies to streamline image acquisition and process the anticipated hundreds of thousands of images.