MAPPING BUILDING INTERIORS WITH LIDAR: CLASSIFYING THE POINT CLOUD WITH ARCGIS

Accurate maps of building interiors are needed to support location-based services, plan for emergencies, and manage facilities. However, suitable maps to meet these needs are not available for many buildings. Handheld LiDAR scanners provide an effective tool to collect data for indoor mapping but there are no well-established methods for classifying features in indoor point clouds. The goal of this research was to develop an efficient manual procedure for classifying indoor point clouds to represent features-of-interest. We used Paracosm’s PX-80 handheld LiDAR scanner to collect point cloud and image data for 11 buildings, which encompassed a variety of architectures. ESRI’s ArcGIS Desktop was used to digitize features that were easily identified in the point cloud and Paracosm’s Retrace was used to digitize features for which imagery was needed for efficient identification. We developed several tools in Python to facilitate the process. We focused on classifying 29 features-of-interest to public safety personnel including walls, doors, windows, fire alarms, smoke detectors, and sprinklers. The method we developed was efficient, accurate, and allowed successful mapping of features as small as a sprinkler head. Point cloud classification for a 14,000 m2 building took 20–40 hours, depending on building characteristics. Although the method is based on manual digitization, it provides a practical solution for indoor mapping using LiDAR. The methods can be applied in mapping a wide variety of features in indoor or outdoor environments

ROADSIDE FOREST MODELING USING DASHCAM VIDEOS AND CONVOLUTIONAL NEURAL NETS

Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to execute roadside vegetation management practices, at the landscape level, without proper monitoring of roadside forests’ physical structure and health condition. Remote sensing images and LiDAR are widely used to characterize the forest edge; however, the limitation on the temporal and spatial resolution for most of that dataset is a big challenge. Also, there is a need for a ground-level dataset that provides the vertical profile of the forest trees so that we can more accurately characterize the forest structure and health and recommend the optimal management strategies according to the local forest conditions. For the first time, we introduced Dashcam videos as an alternative to the existing aerial remote sensing data sources to characterize the roadside forest condition using the deep learning (DL) convolutional neural net (CNN) algorithms. In this study, we used dashcam videos taken during the leaf-on and leaf-off conditions and various weather conditions along the roadside. We trained a DLCNN model based on the U-Net and YOLO v5 architectures to classify the multilayer vegetation and detect utility poles and tree trunks alongside the road. Our experiment results suggest that a dashcam can be a viable alternative and complementary way to characterize the roadside vegetation and can be used in the management of roadside forests as a cost-effective data acquisition mechanism for utility companies

The evolution of ice-wedge polygon networks in tundra fire scars

Abstract In response to increasing temperatures and precipitation in the Arctic, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowland landscapes, polygonal ice wedges are especially vulnerable, and their melting induces widespread subsidence triggering the transition from low-centered (LCP) to high-centered polygons (HCP) by forming degrading troughs. This process has an important impact on surface hydrology, as the connectivity of such trough networks determines the rate of drainage of an entire landscape (Liljedahl et al., 2016). While scientists have observed this degradation trend throughout large domains in the polygonal patterned Arctic landscape over timescales of multiple decades, it is especially evident in disturbed areas such as fire scars (Jones et al., 2015). Here, wildfires removed the insulating organic soil layer. We can therefore observe the LCP-to-HCP transition within only several years. Until now, studies on quantifying trough connectivity have been limited to local field studies and sparse time series only. With high-resolution Earth observation data, a more comprehensive analysis is possible. However, when considering the vast and ever-growing volumes of data generated, highly automated and scalable methods are needed that allow scientists to extract information on the geomorphic state and on changes over time of ice-wedge trough networks. In this study, we combine very-high-resolution (VHR) aerial imagery and comprehensive databases of segmented polygons derived from VHR optical satellite imagery (Witharana et al., 2018) to investigate the changing polygonal ground landscapes and their environmental implications in fire scars in Northern and Western Alaska. Leveraging the automated and scalable nature of our recently introduced approach (Rettelbach et al., 2021), we represent the polygon networks as graphs (a concept from computer science to describe complex networks) and use graph metrics to describe the state of these (hydrological) trough networks. Due to a lack of historical data, we cannot investigate a dense time series of a single representative study area on the evolution of the network, but rather leverage the possibilities of a space-for-time substitution. Thus, we focus on data from multiple fire scars of different ages (up to 120 years between date of disturbance and date of acquisition). With our approach, we might infer past and future states of degradation from the currently prevailing spatial patterns showing how this type of disturbed landscape evolves over space and time. It further allows scientists to gain insights into the complex geomorphology, hydrology, and ecology of landscapes, thus helping to quantify how they interact with climate change

DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS

Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to a multitude of factors including harsh lighting conditions, the presence of thin clouds, the context of surrounding pixels, and complex spatial patterns. In recent studies, deep convolutional neural networks (CNNs) have shown outstanding results in the computer vision domain. These methods are practiced for better capturing the texture, shape as well as context of images. In this study, we propose a deep learning CNN approach to detect cloud pixels from medium-resolution satellite imagery. The proposed CNN accounts for both the low-level features, such as color and texture information as well as high-level features extracted from successive convolutions of the input image. We prepared a cloud-pixel dataset of approximately 7273 randomly sampled 320 by 320 pixels image patches taken from a total of 121 Landsat-8 (30m) and Sentinel-2 (20m) image scenes. These satellite images come with cloud masks. From the available data channels, only blue, green, red, and NIR bands are fed into the model. The CNN model was trained on 5300 image patches and validated on 1973 independent image patches. As the final output from our model, we extract a binary mask of cloud pixels and non-cloud pixels. The results are benchmarked against established cloud detection methods using standard accuracy metrics

GEOSPATIAL MODELING OF ROADSIDE VEGETATION RISK ON DISTRIBUTION POWER LINES IN CONNECTICUT

Roadside trees cause almost 90% of the power outages in the forested Northeastern US. Management of roadside vegetation risk on electrical infrastructure demands timely and accurate information on forest conditions. Tasking conventional ground-based scouting methods along thousands of kilometers of powerlines in a repeated fashion are labor-/cost-/time-intense. Geospatial and earth observation (EO) technologies serve as cost-effective tools in monitoring, inspecting, and managing utility corridors. EO technologies, from drones, aircraft, to satellites can efficiently acquire information over large areas at regular intervals while probing forest physical structure and health conditions. LiDAR is a useful data stream for modeling terrain conditions and estimation of multiple forest inventory variables that explain the physical structure of the forest. Various EO imagery provides information on bio-physical characteristics of trees that affect forest health at finer granularity. The goal of this study is to combine multiple environmental variables to develop a spatially-explicit vegetation risk model using machine learning algorithms. Some of the key inputs used in our analysis include LiDAR-derived tree-related variables (e.g., tree height, proximity pixels, canopy cover), LiDAR-derived terrain data (slope, aspect, topographic index), soil characteristics, vegetation management data (tree trimming methods), infrastructure data (wire type), and power outages reported from 2005 to 2017 in Connecticut. Findings of this research will be vital in informing vegetation management decision-making processes, which eventually reduce power outages and the cost of utility corridor maintenance

Prehabilitation in elective patients undergoing cardiac surgery: a randomised control trial (THE PrEPS TRIAL) – a study protocol

Introduction: Prehabilitation prior to surgery has been shown to reduce postoperative complications, reduce length of hospital stay and improve quality of life after cancer and limb reconstruction surgery. However, there are minimal data on the impact of prehabilitation in patients undergoing cardiac surgery, despite the fact these patients are generally older and have more comorbidities and frailty. This trial will assess the feasibility and impact of a prehabilitation intervention consisting of exercise and inspiratory muscle training on preoperative functional exercise capacity in adult patients awaiting elective cardiac surgery, and determine any impact on clinical outcomes after surgery. Methods and analysis: PrEPS is a randomised controlled single-centre trial recruiting 180 participants undergoing elective cardiac surgery. Participants will be randomised in a 1:1 ratio to standard presurgical care or standard care plus a prehabilitation intervention. The primary outcome will be change in functional exercise capacity measured as change in the 6 min walk test distance from baseline. Secondary outcomes will evaluate the impact of prehabilitation on preoperative and postoperative outcomes including; respiratory function, health-related quality of life, anxiety and depression, frailty, and postoperative complications and resource use. This trial will evaluate if a prehabilitation intervention can improve preoperative physical function, inspiratory muscle function, frailty and quality of life prior to surgery in elective patients awaiting cardiac surgery, and impact postoperative outcomes. Ethics and dissemination: A favourable opinion was given by the Sheffield Research Ethics Committee in 2019. Trial findings will be disseminated to patients, clinicians, commissioning groups and through peer-reviewed publication

High-affinity RNA binding by a hyperthermophilic single-stranded DNA-binding protein

Single-stranded DNA-binding proteins (SSBs), including replication protein A (RPA) in eukaryotes, play a central role in DNA replication, recombination, and repair. SSBs utilise an oligonucleotide/oligosaccharide-binding (OB) fold domain to bind DNA, and typically oligomerise in solution to bring multiple OB fold domains together in the functional SSB. SSBs from hyperthermophilic crenarchaea, such as Sulfolobus solfataricus, have an unusual structure with a single OB fold coupled to a flexible C-terminal tail. The OB fold resembles those in RPA, whilst the tail is reminiscent of bacterial SSBs and mediates interaction with other proteins. One paradigm in the field is that SSBs bind specifically to ssDNA and much less strongly to RNA, ensuring that their functions are restricted to DNA metabolism. Here, we use a combination of biochemical and biophysical approaches to demonstrate that the binding properties of S. solfataricus SSB are essentially identical for ssDNA and ssRNA. These features may represent an adaptation to a hyperthermophilic lifestyle, where DNA and RNA damage is a more frequent event.Publisher PDFPeer reviewe

TWELVE DATA FUSION ALGORITHMS FOR USE IN RAPID DAMAGE MAPPING WORKFLOWS: AN EVALUATION

ABSTRACT Fused images form the basis for manual, semi-, and fully-automated classification steps in the disaster information retrieval chain. Many fusion algorithms have been developed and tested for different remote sensing applications; however, they are weakly assessed in the context of rapid mapping workflows. We examined how well different fusion algorithms would perform when applied to very high spatial resolution (VHSR) satellite images that encompass post-disaster scenes. The evaluation entailed twelve fusion algorithms: Brovey transform, colour normalization spectral sharpening (CN) algorithm, Ehlers fusion algorithm, Gram-Schmidt fusion algorithm, high-pass filter (HPF) fusion algorithm, local mean matching algorithm, local mean variance matching (LMVM) algorithm, modified intensity-hue-saturation (HIS) fusion algorithm, principal component analysis (PCA) fusion algorithm, subtractive resolution merge (SRM) fusion algorithm, University of New Brunswick (UNB) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to GeoEye-1 satellite images taken over two geographical settings: the 2010 earthquake-damaged sites in Haiti and the 2010 flood-impacted sites in Pakistan. Fused images were assessed for spectral and spatial fidelity using sixteen quality indicators and visual inspection methods. Under each metric, fusion algorithms were ranked and best competitors were identified. Ehlers, WV-PCA, and HPF had the best scores for the majority of spectral quality indices. UNB and Gram-Schmidt algorithms had the best scores for spatial metrics. HPF emerged as the overall best performing fusion algorithm