Determination of Elevations for Excavation Operations Using Drone Technologies

Using deep learning technology to rapidly estimate depth information from a single image has been studied in many situations, but it is new in construction site elevation determinations, and challenges are not limited to the lack of datasets. This dissertation presents the research results of utilizing drone ortho-imaging and deep learning to estimate construction site elevations for excavation operations. It provides two flexible options of fast elevation determination including a low-high-ortho-image-pair-based method and a single-frame-ortho-image-based method. The success of this research project advanced the ortho-imaging utilization in construction surveying, strengthened CNNs (convolutional neural networks) to work with large scale images, and contributed dense image pixel matching with different scales.This research project has three major tasks. First, the high-resolution ortho-image and elevation-map datasets were acquired using the low-high ortho-image pair-based 3D-reconstruction method. In detail, a vertical drone path is designed first to capture a 2:1 scale ortho-image pair of a construction site at two different altitudes. Then, to simultaneously match the pixel pairs and determine elevations, the developed pixel matching and virtual elevation algorithm provides the candidate pixel pairs in each virtual plane for matching, and the four-scaling patch feature descriptors are used to match them. Experimental results show that 92% of pixels in the pixel grid were strongly matched, where the accuracy of elevations was within ±5 cm.Second, the acquired high-resolution datasets were applied to train and test the ortho-image encoder and elevation-map decoder, where the max-pooling and up-sampling layers link the ortho-image and elevation-map in the same pixel coordinate. This convolutional encoder-decoder was supplemented with an input ortho-image overlapping disassembling and output elevation-map assembling algorithm to crop the high-resolution datasets into multiple small-patch datasets for model training and testing. Experimental results indicated 128×128-pixel small-patch had the best elevation estimation performance, where 21.22% of the selected points were exactly matched with “ground truth,” 31.21% points were accurately matched within ±5 cm. Finally, vegetation was identified in high-resolution ortho-images and removed from corresponding elevation-maps using the developed CNN-based image classification model and the vegetation removing algorithm. Experimental results concluded that the developed CNN model using 32×32-pixel ortho-image and class-label small-patch datasets had 93% accuracy in identifying objects and localizing objects’ edges

Fotogrametría UAV en apoyo del levantamiento de un sitio arqueológico. modelos 3D en la Hierápolis de Frigia (Turquía)

[EN] Unmanned aerial vehicle (UAV) photogrammetry has shown a very rapid development in many fields, especially in archaeological excavation areas and architectural complexes, where it offers a detailed generation of three-dimensional (3D) data including the possibility of updating over time. It also proves to be a very flexible tool applicable to many types of complex areas with a variety of different features. The use of aerial acquisition provides highly effective results, adding to both rapid capture and lower costs. In fact, today in the field of archaeological research, great efforts are invested in the generation of very large-scale models and orthophotos, and the technology seems to promise further future developments, not only from the terrestrial (orthogonal) point of view, but also from the nadiral direction from a low altitude, as a preferential and often optimal point of view. Here an effective workflow for photogrammetric product generation is presented for selected case studies in some monumental areas of ancient Hierapolis in Phrygia (Turkey), in which the Italian Archaeological Mission of Hierapolis (MAIER) has been working since the 1960s. The recent experiences achieved by UAV photogrammetry are quite innovative. The variety and complexity of the buildings, as well as the height of their ruins, offer numerous challenges, which are interesting to deal with. The 3D aerial survey was performed for multiple purposes with the eBee system by Sensefly. Specific attention was paid to the digital surface model (DSM) and aerial orthoimages of three test areas: the Plutonium area; the Thermal Bath-Church; and the Necropolis. Starting from the same technical approach, a comparative assesment among the three sites was carried out, taking into account the specific goals, the type of the structure and the terrain conformation.[ES] La fotogrametría con vehículos aéresos no tripulados (Unmanned Aerial vehicle, UAV) ha mostrado un desarrollo muy rápido en muchos campos, especialmente en áreas de excavación arqueológica y complejos arquitectónicos, donde ofrece una detallada generación de datos tridimensionales (3D), junto con su actualización en el tiempo. También demuestra ser una herramienta muy flexible aplicable en muchos tipos de áreas complejas, con diferentes características formales. El uso de la toma aérea proporciona hoy resultados altamente efectivos, lo que aumenta la rapidez de adquisición y menores costes. De hecho, hoy en día en el campo de la investigación arqueológica, se invierten grandes esfuerzos a la hora de generar modelos y ortoimágenes a grandes escalas, y parece prometer más desarrollos futuros, no sólo desde el punto de vista terrestre (ortogonal), sino también con dirección nadiral, desde baja altitud, como punto de vista preferencial y óptimo. Aquí se presenta un flujo de trabajo eficaz que permite generar productos fotogramétricos en varios casos de estudio en áreas monumentales de la antigua Hierápolis de Frigia (Turquía), donde la Misión Arqueológica Italiana de Hierápolis (MAIER) ha estado funcionando desde los años 1960. Estas experiencias logradas con la fotogrametría UAV son bastante innovadoras. La variedad y la complejidad de los edificios, así como la altura de sus ruinas ofrecen numerosos puntos problemáticos que son interesantes de tratar. El levantamiento aéreo 3D se realizó con múltiples propósitos por medio del sistema eBee de Sensefly. Se prestó especial atención al Modelo Digital de Superficie (MDS) y a las ortoimágenes aéreas en tres áreas de prueba: el área de Plutonio; la Iglesia-Baño Termal; y la Necrópolis. Partiendo del mismo enfoque técnico, se ofreció una evaluación comparativa de los tres sitios, teniendo en cuenta los objetivos específicos, el tipo de estructura y la configuración del terreno.Chiabrando, F.; D'andria, F.; Sammartano, G.; Spanò, A. (2018). UAV photogrammetry for archaeological site survey. 3D models at the Hierapolis in Phrygia (Turkey). Virtual Archaeology Review. 9(18):28-43. doi:10.4995/var.2018.5958SWORD284391

Photogrammetry as a New Scientific Tool in Archaeology: Worldwide Research Trends

Archaeology has made significant advances in the last 20 years. This can be seen by the remarkable increase in specialised literature on all archaeology-related disciplines. These advances have made it a science with links to many other sciences, both in the field of experimental sciences and in the use of techniques from other disciplines such as engineering. Within this last issue it is important to highlight the great advance that the use of photogrammetry has brought for archaeology. In this research, through a systematic study with bibliometric techniques, the main institutions and countries that are carrying them out and the main interests of the scientific community in archaeology related to photogrammetry have been identified. The main increase in this field has been observed since 2010, especially the contribution of UAVs that have reduced the cost of photogrammetric flights for reduced areas. The main lines of research in photogrammetry applied to archaeology are close-range photogrammetry, aerial photogrammetry (UAV), cultural heritage, excavation, cameras, GPS, laser scan, and virtual reconstruction including 3D printing

Multi-temporal images and 3D dense models for archaeological site monitoring in Hierapolis of Phrygia (TK)

and range-based measurement systems have become increasingly interesting in excavation processes for monitoring purposes and large scale mapping, both from a terrestrial and aerial point of view. The paper will focus on the great challenge of monitoring sites over time, integrating and conforming multiple data coming from previous metric survey projects and image data collected in the past for different purposes. The test-site was the complex archaeological landscape of the ancient city of Hierapolis in Phrygia on which the MAIER – Italian Archaeological Mission of Hierapolis has operated since the 1960s and where the Politecnico di Torino conducted several survey campaigns. A set of multi-temporal datasets acquired in a series of campaigns in 1997, 2002, 2007, 2012, 2015 are presented, as well as their 3D multi-sensor models; the older dense models generated with archival images are intended to be compared and integrated with newer models generated by the LiDAR scans in 2012 and the UAV systems employed in the last mission in 2015. In particular, the case study was the massive complex of the ancient Bath-Church in the northern part of the city below the Northern Necropolis, and Building A of the Apollo Sanctuary, in the central Sacred Area near the Ancient Theatre. In these sites, many different sensors have been experimented with over the years and preliminary multi-temporal data integration has been tested in order to up-date and improve older archival records based on collected images and related to newer and updated documentation projects

A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this paper proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.Comment: 8 pages, 8 figure

MULTI-TEMPORAL IMAGES AND 3D DENSE MODELS FOR ARCHAEOLOGICAL SITE MONITORING IN HIERAPOLIS OF PHRYGIA (TR)

The archaeological areas are one of the fields in which the contribution of image-base and range-based Geomatics techniques were employed since long time and are now getting popular (Hadjimitsis et al. 2009; Campana 2017). In recent times, Remotely Piloted Aircraft Systems (RPAS), together with Terrestrial Laser Scanning (TLS) survey systems become more and more interesting to be studied in excavations sites for monitoring purposes and solving high detail data and large scale and comprehensive mapping matters both from terrestrial and aerial point of view. 3D information derived from different acquisition campaign and different sensors too, belonging to the same spatial system, can be integrated and create a multi-temporal and multi-scale database (Remondino, Rizzi 2010; Kersten, Lindstaedt 2012; Moussa, Abdel-Wahab, Fritsch 2012; Chiabrando et al. 2016; Farella et al. 2016). The contribution of multi-sensor acquisitions, as it is known from increasing scientific experiences, offers by now to archaeological studies the possibility to obtain a multi-temporal view of the site with restricted time windows, but it is interesting considering also the possibility of a valuable integration and contribution of older image-based documentation, already stored in archives. In a complex archaeological area as the one of Hierapolis of Phrygia in Pamukkale (TR), a very interesting site investigated since several tens of years by the MAIER – Italian Archaeological Mission of Hierapolis, the impressive excavations have required extensive and accurate large-scale survey and documentation projects. This paper will also show the evolution over time of 3D survey methods. In particular, the presented experiences regarding the documentation campaigns by Politecnico di Torino and Geomatics group concern many multi-temporal datasets that have been acquired by different subsequent campaigns in 1997, 1998, 2007, 2012, 2015, by employing various sensors following the evolution of the acquisition techniques offers by geomatics in the archaeological field surveys

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

Exploring heritage through time and space : Supporting community reflection on the highland clearances

On the two hundredth anniversary of the Kildonan clearances, when people were forcibly removed from their homes, the Timespan Heritage centre has created a program of community centred work aimed at challenging pre conceptions and encouraging reflection on this important historical process. This paper explores the innovative ways in which virtual world technology has facilitated community engagement, enhanced visualisation and encouraged reflection as part of this program. An installation where users navigate through a reconstruction of pre clearance Caen township is controlled through natural gestures and presented on a 300 inch six megapixel screen. This environment allows users to experience the past in new ways. The platform has value as an effective way for an educator, artist or hobbyist to create large scale virtual environments using off the shelf hardware and open source software. The result is an exhibit that also serves as a platform for experimentation into innovative ways of community co-creation and co-curation.Postprin

Three-Dimensional Reconstruction and Modeling Using Low-Precision Vision Sensors for Automation and Robotics Applications in Construction

Automation and robotics in construction (ARC) has the potential to assist in the performance of several mundane, repetitive, or dangerous construction tasks autonomously or under the supervision of human workers, and perform effective site and resource monitoring to stimulate productivity growth and facilitate safety management. When using ARC technologies, three-dimensional (3D) reconstruction is a primary requirement for perceiving and modeling the environment to generate 3D workplace models for various applications. Previous work in ARC has predominantly utilized 3D data captured from high-fidelity and expensive laser scanners for data collection and processing while paying little attention of 3D reconstruction and modeling using low-precision vision sensors, particularly for indoor ARC applications. This dissertation explores 3D reconstruction and modeling for ARC applications using low-precision vision sensors for both outdoor and indoor applications. First, to handle occlusion for cluttered environments, a joint point cloud completion and surface relation inference framework using red-green-blue and depth (RGB-D) sensors (e.g., Microsoft® Kinect) is proposed to obtain complete 3D models and the surface relations. Then, to explore the integration of prior domain knowledge, a user-guided dimensional analysis method using RGB-D sensors is designed to interactively obtain dimensional information for indoor building environments. In order to allow deployed ARC systems to be aware of or monitor humans in the environment, a real-time human tracking method using a single RGB-D sensor is designed to track specific individuals under various illumination conditions in work environments. Finally, this research also investigates the utilization of aerially collected video images for modeling ongoing excavations and automated geotechnical hazards detection and monitoring. The efficacy of the researched methods has been evaluated and validated through several experiments. Specifically, the joint point cloud completion and surface relation inference method is demonstrated to be able to recover all surface connectivity relations, double the point cloud size by adding points of which more than 87% are correct, and thus create high-quality complete 3D models of the work environment. The user-guided dimensional analysis method can provide legitimate user guidance for obtaining dimensions of interest. The average relative errors for the example scenes are less than 7% while the absolute errors less than 36mm. The designed human worker tracking method can successfully track a specific individual in real-time with high detection accuracy. The excavation slope stability monitoring framework allows convenient data collection and efficient data processing for real-time job site monitoring. The designed geotechnical hazard detection and mapping methods enable automated identification of landslides using only aerial video images collected using drones.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138626/1/yongxiao_1.pd