Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazard

Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques in Blue Tarpaulin Inspection

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature v identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize or label the objects, or classify the pixels in the picture. Finally, supervised classification is based on a system in which a user picks sample pixel in an image that are indicative of certain classes and then tells image-processing software to categorize the other pixels in the picture using these training sites as references. To group pixels together, the user also specifies the limits for how similar they must be. The number of classifications into which the image is categorized is likewise determined by the user. The importance of tracking blue roofs is multifaceted. Structures with roof damage from natural disasters face many immediate dangers, such as further water and wind damage. These communities are at a critical moment as responding to the damage efficiently and effectively should occur in the immediate aftermath of a disaster. In part due to strategies such as FEMA and the United States Army Corps of Engineers’ (USACE) Operation Blue Roof, most often blue tarpaulins are installed on structures to prevent further damage caused by wind and rain. From a Unmanned Arial Vehicles (UAV) perspective, these blue tarps stand out amid the downed trees, devastated infrastructure, and other debris that will populate the area. Understanding that recovery can be one of the most important stages of Emergency Management, testing techniques vi for speed, accuracy, and effectiveness will assist in creating more effective Emergency Management (EM) specialists

Investigation of Strategic Deployment Opportunities for Unmanned Aerial Systems (UAS) at INDOT

Unmanned aerial systems (UAS) are increasingly used for a variety of applications related to INDOT’s mission including bridge inspection, traffic management, incident response, construction and roadway mapping. UAS have the potential to reduce costs and increase capabilities. Other state DOTs and transportation agencies have deployed UAS for an increasing number of applications due to technology advances that provide increased capabilities and lower costs, resulting from regulatory changes that simplified operations for small UAS under 55 pounds (aka, sUAS). This document provides an overview of UAS applications that may be appropriate for INDOT, as well as a description of the regulations that affect UAS operation as described in 14 CFR Part 107. The potential applications were prioritized using Quality Function Deployment (QFD), a methodology used in the aerospace industry that clearly communicates qualitative and ambiguous information with a transparent framework for decision making. The factors considered included technical feasibility, ease of adoption and stakeholder acceptance, activities underway at INDOT, and contribution to INDOT mission and goals. Dozens of interviews with INDOT personnel and stakeholders were held to get an accurate and varied perspective of potential for UAVs at INDOT. The initial prioritization was completed in early 2019 and identified three key areas: UAS for bridge inspection safety as a part of regular operations, UAS for construction with deliverables provided via construction contracts, and UAS for emergency management. Descriptions of current practices and opportunities for INDOT are provided for each of these applications. An estimate of the benefits and costs is identified, based on findings from other agencies as well as projections for INDOT. A benefit cost analysis for the application of UAS for bridge inspection safety suggests a benefit cost over one for the analysis period

The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review

The role of digital technologies (DTs) in humanitarian supply chains (HSC) has become an increasingly researched topic in the operations literature. While numerous publications have dealt with this convergence, most studies have focused on examining the implementation of individual DTs within the HSC context, leaving relevant literature, to date, dispersed and fragmented. This study, through a systematic literature review of 110 articles on HSC published between 2015 and 2020, provides a unified overview of the current state-of-the-art DTs adopted in HSC operations. The literature review findings substantiate the growing significance of DTs within HSC, identifying their main objectives and application domains, as well as their deployment with respect to the different HSC phases (i.e., Mitigation, Preparedness, Response, and Recovery). Furthermore, the findings also offer insight into how participant organizations might configure a technological portfolio aimed at overcoming operational difficulties in HSC endeavours. This work is novel as it differs from the existing traditional perspective on the role of individual technologies on HSC research by reviewing multiple DTs within the HSC domain

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable