Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification.

Parking space management systems help organize and optimize available parking spaces for consumers, making the process of finding and using parking spaces more efficient. Current parking space management systems include manual recognition, the employment of magnetic and ultrasonic sensors, and, recently, computer vision (CV). One relatively new region-based convolutional neural network (R-CNN) model, Mask R-CNN, has shown promise in its ability to detect objects and has demonstrated superior performance over many other popular CV methods. Building on Mask R-CNN, an updated version, Rotated Mask R-CNN, which can generate bounding boxes the axes of which are rotated with respect to the image’s axis, was proposed to address the limitation of Mask R-CNN. Albeit with the documented theoretical benefits, the application of the rotated version is rare because of its recent invention. To this end, the study aims to detect vehicle instances in one parking lot using various Rotated Mask R-CNN models based on unmanned aircraft system collected images. Both average precision and average recall were utilized to assess the performance of the alternative models with different backbone and head networks. The results reveal the high accuracy level associated with Rotated Mask R-CNN in real-time detection of vehicles. In addition, the results indicate that the inference speed and total loss are highly correlated with head networks and training schedules.

Throughout the past few decades, advancements in global navigation satellite systems (GNSS), such as the Global Positioning System (GPS), have resulted in real-time planimetric accuracy at the centimetre level, whether achieved using Real-Time Kinematic (RTK) or Real-Time Network (RTN) approaches. This study examines and characterizes the performance of RTK and RTN solutions at three test sites in Los Angeles County, California, in the United States of America. The solutions were characterized to examine the dispersion of vertical measurements in the context of different environments. The results from this study suggest that both methods have advantages and drawbacks; in particular, the data showed that the distribution and density of the network stations, cellular network coverage and environmental dynamics have significant impacts on RTN vertical precision, where the vertical precision for the RTN measurements were observed to be 2-4 times lower in comparison to the RTK measurements.

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs.

Roadway crack detection is essential for ensuring a safe and comfortable driving environment. However, given the irregular shape, small area size, and occasionally very large number, of the pavement cracking objects, it is often laborious to label the cracking instances during the training process under the fully supervised algorithm. To address this issue, the study strives to apply semi-supervised learning for crack detection that claims to reduce the cost associated with the labeling process, while possibly maintaining or even improving the learning accuracy in some situations. The research features three distinct backbones of Mask R-CNN models, Unmanned Aerial System imagery of two resolutions, three levels of pseudo-labeled data, eleven threshold values and two types of assessment (that is, in-resolution and out-of-resolution). The results demonstrate that semi-supervised Mask R-CNN models are effective in detecting roadway cracks. Nonetheless, the sensitive analysis is recommended in the future research to identify the optimal pseudo ratio that could generate the highest prediction accuracy.

Surface distress is an indication of poor or unfavorable pavement performance or signs of impending failure that can be classified into a fracture, distortion, or disintegration. To mitigate the risk of failing roadways, effective methods to detect road distress are needed. Recent studies associated with the detection of road distress using object detection algorithms are encouraging. Although current methodologies are favorable, some of them seem to be inefficient, time-consuming, and costly. For these reasons, the present study presents a methodology based on the mask regions with convolutional neural network model, which is coupled with the new object detection framework Detectron2 to train the model that utilizes roadway imagery acquired from an unmanned aerial system (UAS). For a comprehensive understanding of the performance of the proposed model, different settings are tested in the study. First, the deep learning models are trained based on both high- and low-resolution datasets. Second, three different backbone models are explored. Finally, a set of threshold values are tested. The corresponding experimental results suggest that the proposed methodology and UAS imagery can be used as efficient tools to detect road distress with an average precision score up to 95%.

ABSTRACT The ability to accurately estimate the amount of stockpile material for construction projects can have a substantial impact on project budgets and schedules as well as public safety. Recent proliferation of small Unmanned Aircraft System (sUAS) platforms has made it possible to estimate these stockpiles using inexpensive aerial imagery and photogrammetry at a fraction of the cost and time compared to traditional methods. This study examines the quality of height and volume estimates from sUAS photogrammetry processed through Agisoft Metashape, Bentley ContextCapture, and PixElement, and compared with Global Navigation Satellite System (GNSS) based surveying and Terrestrial Laser Scanning (TLS) measurements. Measurements made on three co-located stockpiles occupying in total 345 m2 and a volume of 487.11 m3 showed that all three software produced results within 3% of TLS measured height and 2% of TLS volume overall, with greater local variation. Moreover, photogrammetry provided complete coverage while the TLS data had voids due to scanner obstructions. Considering that results could be further improved with refinements in technique, this study joins others in demonstrating that sUAS photogrammetry provides a viable alternative to conventional methods for estimating stockpiles.

Landslides are natural disasters that cause extensive environmental, infrastructure and socioeconomic damage worldwide. Since they are difficult to identify, it is imperative to evaluate innovative approaches to detect early-warning signs and assess their susceptibility, hazard and risk. The increasing availability of airborne laser-scanning data provides an opportunity for modern landslide mapping techniques to analyze topographic signature patterns of landslide, landslide-prone and landslide scarred areas over large swaths of terrain. In this study, a methodology based on several feature extractors and unsupervised classification, specifically k-means clustering and the Gaussian mixture model (GMM) were tested at the Carlyon Beach Peninsula in the state of Washington to map slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, the unsupervised methods correctly classify up to 87% of the terrain in the study area. These results suggest that (1) landslide scars associated with past deep-seated landslides may be identified using digital elevation models (DEMs) with unsupervised classification models; (2) feature extractors allow for individual analysis of specific topographic signatures; (3) unsupervised classification can be performed on each topographic signature using multiple number of clusters; (4) comparison of documented landslide prone regions to algorithm mapped regions show that algorithmic classification can accurately identify areas where deep-seated landslides have occurred. The conclusions of this study can be summarized by stating that unsupervised classification mapping methods and airborne light detection and ranging (LiDAR)-derived DEMs can offer important surface information that can be used as effective tools for digital terrain analysis to support landslide detection.

Interest in small unmanned aircraft systems (sUAS) for topographic mapping has significantly grown in recent years, driven in part by technological advancements that have made it possible to survey small- to medium-sized areas quickly and at low cost using sUAS aerial photography and digital photogrammetry. Although this approach can produce dense point clouds of topographic measurements, they have not been tested extensively to provide insights on accuracy levels for topographic mapping. This case study examines the accuracy of a sUAS-derived point cloud of a parking lot located at the Citizens Bank Arena (CBA) in Ontario, California, by comparing it to ground control points (GCPs) measured using global navigation satellite system (GNSS) data corrected with real-time kinematic (RTK) and to data from a terrestrial laser scanning (TLS) survey. We intentionally chose a flat surface due to the prevalence of flat scenes in sUAS mapping and the challenges they pose for accurately deriving vertical measurements. When the GNSS-RTK survey was compared to the sUAS point cloud, the residuals were found to be on average 18 mm and −20 mm for the horizontal and vertical components. Furthermore, when the sUAS point cloud was compared to the TLS point cloud, the average difference observed in the vertical component was 2 mm with a standard deviation of 31 mm. These results indicate that sUAS imagery can produce point clouds comparable to traditional topographic mapping methods and support other studies showing that sUAS photogrammetry provides a cost-effective, safe, efficient, and accurate solution for topographic mapping.

In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m(2) to 27,000 m(2) in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslideprone areas. In this study, a methodology based on k-means clustering and Gaussian Mixture Model (GMM) tested several feature extractors and employed an unsupervised classifier at the Carlyon Beach Peninsula in the state of Washington to attempt to distinguish between slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, our algorithms correctly classify up to 87% of the terrain in our study area. These results suggest that the proposed methods and LiDAR-derived DEMs can provide important surface information and be used as efficient tools for digital terrain analysis to create accurate landslide maps.

Landslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement, and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation.We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a lidar-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, Ohio, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne lidar data can be used as efficient tools for small landslide susceptibility and hazard mapping.

Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

A probabilistic approach is proposed to aid landslide susceptibility mapping. The objective of the proposed approach is to identify and predict areas that may develop into landslides and quantify the growth of existing landslides with high probability. Change detection was applied to repeat airborne Light Detection and Ranging (LiDAR) surveys acquired in December of 2008 and April of 2012. The study area was along the transportation corridor of Muskingum State Route 666 in Zanesville, Ohio, an area characterized by high vegetation densities, stream and river channeling, and some residential development. In the investigation, changes between LiDAR-derived Digital Elevation Models (DEM) were computed by analyzing, cell-by-cell, the vertical differences and, consequently, generating a DEM of Difference (DoD) map. Then, a parametric z-test was used to evaluate probabilistically if single-cell differences were real as compared to noise. Next, a non-parametric signed rank test was used to assess local neighborhoods and compute the probability that the median of the samples surpassed a desired threshold. Finally, high-probability neighborhoods (clusters) comprised of a minimum area and desired probabilities were mapped as “landslide susceptible”. The initial results, obtained by comparison to a reference landslide map, were as expected, indicating that segments of the mapped landslides experienced changes, while others did not. It was also observed that some unmapped areas also experienced changes, indicating that they may be developing landslides. This study demonstrates that the monitoring of existing and identification of newly developing landslides is feasible from multi-temporal airborne LiDAR data.

La red de vaporductos del campo geotermico de Cerro Prieto esta compuesta po r un conjunto de 184 pozos, de los cuales 162 son pozos integrados, interconectados en tre si a traves de una red de tuberias. P o r m e dio de esta red se alim entan 13 unidades ge neradoras de electricid ad con una capacidad tota l instalada de 720 MW e. La red tiene una longitud aproxim a da de 120 kilom e tr os y esta co m puesta por tuberias de diferentes diam etros, ram a les, interconexion es, etc. La complejidad y extension d e l sis t em a de vaporductos hace m u y dificil el analisis del tran sporte y su m i nistro de vapor a las plantas genera doras. Lo anterior creo la necesid ad de contar con una herram ienta que ayudara en el an alisis del sistem a con el fin de determ inar el com porta m i ento global de la red y verificar la direccion y cantidades de flujo en cada uno de las interconexiones, colectores, ram a les y sub-ram a les. En este trabajo s e p r esenta un modelo hidraulico de la red de vaporductos del cam po geoterm i co de Cerro Prie to , el cu al perm ite determ inar el com portam i ento global d e la red m e diante la cu antificacion d e las caidas de presion, flu j os y perd idas de calor a traves de lo s com ponentes del sistema. Ade m as, el m odelo p e rm ite el analisis del im pacto de cambios en las condiciones de operacion, variaciones en la pro duccion de vapor, actividades de m a nt enim iento y cam bios en el diseno, com o es la integracion de nuevos pozos. El m o d e lo se desarrollo utilizando PIPEP HASE 9.0, el cual es un sim u lador num e rico de f l ujo m u ltif asico en es tado estacio n ario con tr ansf erencia de calor, q u e perm ite modelar sistem as de tuberias y redes para el transporte de vapor y condensado

Se presentan los resultados de la simulacion de la red de vaporductos del campo geotermico de Cerro Prieto, B.C. (CGCP). La red esta com puesta por dos redes parale las de Alta y Ba ja Pre s i on debido a que en el cam po existe separacion prim aria y secundaria de vapor, excepto en Cerro Prieto Uno (CPU), donde solo hay separacion prim aria de vapor. A su vez, la red de Alta Presion se dividio en dos partes: el Bloque Norte, for m ado por los Ra m a les 1 y 2 de Cerro Prieto Tres (C PT), el cam po de Cerro Prieto Cuatro (CPC), los pozos de Cerro Prieto Dos (CPD) q u e envian vapor a la Interconex i on CPD-CPT (Cerro Prieto Tres), y las Interconexio n es B y C h acia CPU; y el Bloqu e S u r form ado por los Ramales 1 y 2 de CPD y el resto de los pozos de CPU. Por su pa rte, la red de Baja Presion se subdividio en tres bloques: el Bloque 1 que incluye 16 pozos de C P C y el Ram a l 2 de CPT; el Bloque 2 que incluye 17 pozos de CPC, el Ram a l 1 de CPT y la Interconexion CPD-CPT, y el Bloque 3 que incluye 44 po zos de CPD. Se enco ntro que las diferencias relativas prom edio de las presiones y flujos m e didos y si m u lados son m e no res para la red de Alta Presion que para la red de Baja Presion. Para el Bloque Norte de la red de Alta Presion, la diferencia relativa prom edio entre presiones m e didas y sim u ladas de los pozos vari a entre -3.8% y +6.6%, con valores m e d i os de 1.2 a 4.3%, m i entras que para los puntos de entrega-recepci on de vapor en las plan tas las diferencias entre presiones y gastos m e didos y sim u l a dos son m e nores de 5.2 % y 1 % , res p ectiv am ente, y la calid ad es m a yor de 98.5%. Para el Bloque Sur de la red de Alta Pres ion, la diferencia relativa prom edio entre presiones m e didas y s i m u ladas de los pozos varia entre -8.2% y +4 .2%, con valores m e dios de -3.7 a 2.3%, m i entras que para los puntos de entrega-recepcion de vapor