Search

Historical terrestrial images are the only visual sources documenting alpine environments shortly after the end of the Little Ice Age. Despite their unique value, they are largely unused for quantifying environmental changes because of the difficult and time-consuming estimation of the unknown camera parameters. For most images large parts of the captured scenery have vastly changed over time, making automatic feature point matching infeasible. In contrast, the visible image horizon seems to remain stable over time and hence, appears to be a suitable feature for image orientation. Since the focal length is unknown for historical terrestrial images, existing methods, focusing solely on estimating the exterior orientation of recent imagery, can not be applied. Accordingly, it was investigated if the horizon is suitable to estimate both the interior and exterior orientation of historical terrestrial images, with an accuracy comparable to manually oriented images. In a first step, the whole horizon was used to approximate the unknown camera parameters, reducing the potential search space. In the subsequent spatial resection these approximations were further refined using salient points along the horizon. We evaluated our approach using 204 manually oriented reference images. With the proposed method the accuracy of the estimated exterior orientation could be significantly improved compared to previous works. Additionally, the unknown focal length was estimated within 5% of the true focal length for 75% of the images. As historical terrestrial images are commonly used for monoplotting, the accuracy for 2400 manually selected checkpoints was evaluated. This analysis showed that for 63% of the images the same accuracy as with manually oriented images was achieved. For additional 22% the estimated camera parameters were still accurate enough to serve as initial estimates for a subsequent manual orientation. In 15% of the images our method completely failed. Due to the vastly changing scenery and oblique viewing geometry, finding the initial camera parameters, in our experience, is often the most challenging and time consuming step during manual orientation of historical images. Hence, in 85% of the images this initial step can be replaced with our method, leading to a significantly reduced effort for orienting whole collections of historical terrestrial images.

Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation.

Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices.

The increased availability of satellite SAR data coupled with improved InSAR processing algorithms has led to higher accuracy of InSAR-derived displacements. However, obtaining millimeter-level accuracy still remains a challenge due to the inability to accurately model the tropospheric delay, which is sometimes larger than the actual deformation. We propose to estimate the relative daily SAR tropospheric delay by double differencing interferograms that share a common image by leveraging Sentinel-1’s short revisit time and the interchanging role of the common image. The resulting double-differenced interferograms consist of tropospheric delay and processing noise. This combination leads to an inverse problem as a result of the differential nature of the interferograms. The unknown SAR tropopsheric delay is solved by Tikhonov regularization and the processing errors are accounted for by covariance modelling. In cases of rapid localized displacement, the deforming region is masked and the tropospheric delay for the deforming region is determined through kriging. Validation is performed using simulated SAR data and GNSS tropospheric delays. We find a good correlation between estimated SAR tropospheric delays and GNSS tropospheric delays, with an average root mean square error (RMSE) of 1.93 radians across six GNSS locations. In addition, we examine the performance of the correction on interferograms through variogram modelling, which indicates improved correction both for short-wavelength and long-wavelength tropospheric noise. SAR tropospheric delay estimates are integrated into a multitemporal InSAR workflow through either interferometric subtraction or stochastic modelling. Validation using GNSS measurements indicates that SAR tropospheric delay estimates significantly improve the accuracy of the InSAR-derived time-series displacement.

Geometric features such as cylinders and planes are important objects of interest in terrestrial laser scanner surveys of complex scenes. The quality of the objects modelled from the laser scanner data is a function of many variables and geometric network design plays a key role in maximizing precision. The expected precision can be predicted at the planning stage from simulations of the environment to be scanned. However, this practice can incur a high computational load, even if performed in 2D rather than in 3D. In this paper, a closed-form solution to estimate geometric object precision is proposed as an efficient first order network design tool. It models the laser scanner measurement process with an observation distribution function that is introduced into the least-squares normal equations. Parameter precision is evaluated directly by solving a few (three to six) integrals and inverting the normal equations matrix. The method is presented for two cases of a circle lying in the horizontal plane and a 2D line scanned from a single location. Both a simplified circle model and a more general circle model are explored. The method is then extended using the summation of normals method to allow precision estimation from the combination of multiple scans from different locations. Results from many real datasets, 95 circles and 30 lines, show that the distributions of the range observations and derived Cartesian coordinates follow model predictions. Moreover, results demonstrate that the method can predict circle parameter standard deviations within 4%–6% of the experimental values. The agreement is at the 10% level for a very specific case due to inherent high parameter correlation. The agreement of line parameter standard deviations is much greater, approximately 0.1%. The results show the method can be a valuable tool to predict feature quality with minimal computational requirements. The method is beneficial to not only laser scanner network design but could also be to instantaneous 2D map construction performed for SLAM-based surveys.

Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed.

Timber stand inventories are critical for making management decisions in both commercial and conservation related forestry applications. When taking these inventories, the most critical characteristics to record are diameter at breast height (DBH), stand density, tree height and tree species. As discovered in prior research, a relatively inexpensive device, which hereby will be referred to as AutoTSI (short for Automatic Timber Stand Inventory), can be assembled and deployed in the field to take accurate measurements of diameter at breast height (DBH) for timber stand sample plots in a short amount of time compared to the traditional method of manually measuring these trees. The specified range and angle of resolution of the sensor allows for estimation of stand density and basal area. Each element of both the hardware and software is modular by design allowing the user to customize according to whatever sensors may be readily available to the user. In silviculture research, methodical collection, and analysis of geometric measurements of trees is essential for developing land management plans that optimize biomass production, biodiversity, and other desired characteristics in a forest environment making this device useful to both tree farmers and researchers.

Background: Armenia has trained physicians to practice family medicine (FM) for over 20 years. The pediatric population comprises a significant proportion of patients seen by FM practices, yet to date, there have been no studies assessing the knowledge and self-efficacy of FM physicians regarding pediatric care. As the first step is needs assessment to improve the quality of care, this study aims to assess the self-efficacy and knowledge of FM physicians regarding the care of pediatric patients. Materials and Methods: We distributed a survey to attendees at an FM conference in Lori Province, Armenia. The survey instrument assessed demographics and experience, self-efficacy in providing pediatric care, and pediatric knowledge via questions adapted from the American Board of Family Medicine (ABFM). Results: Eighty-seven percent of participants were female. Roughly half (45%) had trained through an FM residency program, while the remainder had retrained to become FM physicians following a residency in another field. Almost all (97%) practiced outside of the capital city, Yerevan. About half believed that their didactic (51%) and clinical education (48%) prepared them either "extremely" or "very" well. Overall, there was no clear relationship between participants' reported self-efficacy in a given area of pediatrics and their score in that area on the knowledge portion of the survey. Conclusions: Our findings reveal opportunities for improvement in knowledge related to pediatric care in FM physicians in Armenia, as well as a lack of relationship between reported self-efficacy and knowledge. Thus, future programs should not rely solely on self-reported gaps to identify or prioritize areas of focus. Further study is recommended in other specialties in Armenia and internationally to improve future programs. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors' better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations. [ABSTRACT FROM AUTHOR]

The value of archival documents quite often extends beyond their original purpose, as evidence contained within these artefacts, whether written or drawn, can provide veracity for new lines of heritage inquiry. Many settlements in the ‘new world’ were set out by land surveyors whose drawings charted the accurate placement and alignment of new streets and block perimeters laid upon drawings of the extant topographical landscape features. The paper discusses three settlement maps of Melbourne, Australia, through the lens of Michel de Certeau’s idea that maps are an instrument of power are not just about recording; maps are actually about appropriating and producing regimes of place. In the Australian context, the settlement drawings, prepared under the direction of the colonial administration, inadvertently depicts Country that had been under the custodial care of the First Nations peoples for millennia, and through the intentions of the settlement maps about to be irrevocably disturbed, altered or destroyed. We raise the prospect that urban and landscape design can reflect on the ‘lost landscapes’ of cultural significance, and discuss new ways of interpreting representation through an approach of design reconciliation.

An unprecedented effort to improve regional coordination and land use governance has been underway in California since 2008, when the state passed the Sustainable Communities and Climate Protection Act (Senate Bill 375). The law complements earlier state policy (Assembly Bill 32) to reduce statewide greenhouse gas emissions across an array of sectors. SB 375 specifically encourages regional land use planning that, when coupled with supportive transportation investments, would help to reduce automobile dependent patterns of land use and sprawl. Implementation of these new regional land use visions and the GHG reductions they promise depend largely on local government land use and development actions. This report explores the responses of California cities and counties to this experiment in order to understand what may make local governments more or less likely to collaborate with regionally oriented policies. It reports on a survey of California local governments administered in early 2017 and explores two main questions: (1) to what extent are California local governments adopting local land use policy and development decisions that reflect the MPO’s regional land use vision, and (2) what factors make some local governments more likely to cooperate with regional land use visions, and what factors make others less likely to do so?View the NCST Project Webpage

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spatial autocorrelation between training and validation data. Independent of the analytical method, such spatial dependence will inevitably inflate the estimated model performance. This problem is ignored in most CNN-related studies and suggests a flaw in their validation procedure. Here, we demonstrate how neglecting spatial autocorrelation during cross-validation leads to an optimistic model performance assessment, using the example of a tree species segmentation problem in multiple, spatially distributed drone image acquisitions. We evaluated CNN-based predictions with test data sampled from 1) randomly sampled hold-outs and 2) spatially blocked hold-outs. Assuming that a block cross-validation provides a realistic model performance, a validation with randomly sampled holdouts overestimated the model performance by up to 28%. Smaller training sample size increased this optimism. Spatial autocorrelation among observations was significantly higher within than between different remote sensing acquisitions. Thus, model performance should be tested with spatial cross-validation strategies and multiple independent remote sensing acquisitions. Otherwise, the estimated performance of any geospatial deep learning method is likely to be overestimated.

Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results.

Automated change detection based on urban mobile laser scanning data is the foundation for a whole range of applications such as building model updates, map generation for autonomous driving and natural disaster assessment. The challenge with mobile LiDAR data is that various sources of error, such as localization errors, lead to uncertainties and contradictions in the derived information. This paper presents an approach to automatic change detection using a new category of generic evidence grids that addresses the above problems. Said technique, referred to as fuzzy spatial reasoning, solves common problems of state-of-the-art evidence grids and also provides a method of inference utilizing fuzzy Boolean reasoning. Based on this, logical operations are used to determine changes and combine them with semantic information. A quantitative evaluation based on a hand-annotated version of the TUM-MLS data set shows that the proposed method is able to identify confirmed and changed elements of the environment with F1-scores of 0.93 and 0.89.

Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications.

The article analyses the land structure of the peasants of Hlukhiv, Konotop and Krolevets counties of Chernihiv province as a result of the reform of 1861 in the middle of the 19th — early 20th centuries. Certain regional features of the process of land allotment of peasants of different categories are indicated. The common features and peculiarities of the realization of the Little Russian and Great Russian positions, drafting and implementing of charters and land surveying are determined. The counties that used the Little Russian and Great Russian positions are shown (or both at the same time). The sizes of peasant plots and duties for them are shown. The examples illustrate the protection of the interests of landlords by the authorities when endowing peasants of different categories of land. The principles of selection of peace mediators and the level of efficiency of their work, as well as approaches to the creation of peace sites are highlighted. The pace of the surveying process and its consequences for different categories of peasants are analysed.

This paper proposes citizen heritage science as an effective method to gather reliable data for monitoring and documenting heritage sites. For large heritage organisations the monitoring and documentation of sites in their care presents considerable challenges; continual monitoring of smaller, unstaffed, and more remote sites is often not practical. However, heritage sites are often popular destinations that receive high levels of visitors who carry increasingly sophisticated mobile phones. It seems logical that heritage organisations capitalise on using visitors' images to record and monitor remote heritage sites. We compare two methods for data collection: a 'guided' approach, in which on-site signage prompts visitor submissions; and an 'open' approach, in which the public is asked to send any photographs they have of the site in question. We analyse the different results in collected data from these two approaches and hope to encourage heritage institutions to set up similar projects. [ABSTRACT FROM AUTHOR]

The exploitation of the underground is currently undergoing a major transformation process. Besides mining, alternative ways of utilization are playing an increasingly important role. These alternative utilizations include the storage of gases, the disposal of radioactive wastes, and the implementation of geothermal storage facilities. In addition, more and more mining operations are being shut down, like the German hard coal mining industry in 2018. But the integrity of closed mines must be ensured in the post-mining phase. In order to capture such large and complex structures efficiently and reliably, new surveying techniques are required. A promising approach is the use of photogrammetric methods to generate three-dimensional models of the cavity. Photogrammetry is currently known mainly from aerial photography. It is conceivable to use such methods for underground applications. In recent years, significant progress has been made in the algorithm-based orientation of unstructured image sets. More powerful computer systems and the growing range of unmanned aerial vehicles could enable partially automated capture. However, a number of problems have to be solved for underground imaging techniques. Unfavorable exposure conditions complicate image alignment. Furthermore, the models must be integrated into existing surveying systems with a sufficiently high degree of accuracy. In order to consider the numerous technical aspects, the development of an integrated capture and evaluation approach is necessary. This could make an important contribution to the monitoring of critical cavities of different commodities. Such an approach would also fit into the current digitization trend in the mining industry and provide new opportunities for ground control in particular.