**Preface to "UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments"**

Dear Colleagues,

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data have enabled a rapid uptake of UAVs and drones across several industries and application domains.

The scope of this book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single and multiple UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS enabled and more broadly Global Navigation Satellite System (GNSS) enabled and GPS /GNSS denied environments.

Contributions include:

UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;

UAV sensor applications, spatial ecology, pest detection, reef, forestry, volcanology, precision agriculture wildlife species tracking, search and rescue, target tracking, the monitoring of the atmosphere, chemical, biological, and natural disaster phenomena, fire prevention, flood prevention, volcanic monitoring, pollution monitoring, microclimates, and land use;

Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;

UAV-based change detection;

Sense and avoid;

UAV navigation in cluttered environments;

Single and multiple UAVs or swarms in GPS /GNSS denied environments;

On-board decision making;

Real-time georeferencing for UAV-based imaging;

Radiometric and spectral calibration of UAV-based sensors;

UAV onboard data storage, transmission, and retrieval;

Collaborative strategies and architectures to control multiple UAVs and sensor networks for the purpose of remote sensing.

#### **Felipe Gonzalez Toro, Antonios Tsourdos**

*Editors*


**Changing Skills in Changing Environments: Skills Needed in Virtual Construction Teams**

Willy Sher1, Sue Sherratt1, Anthony Williams1 and Rod Gameson2

*1The University of Newcastle 2University of Salford 1Australia 2United Kingdom*

#### **1. Introduction**

30 Applications of Virtual Reality

Zhao Ji et al.(2000).On Implementing Virtual Production Line. *Journal of Northwestern Polytechnical University*, Vol.18,No.4(April 2000),pp.518-521,ISSN 1007-5429 Zhang Longxiang.(2007)*UML and System Analysis and Design(2nd edition)*. Post & Telecom

Press, BeiJing, ISBN<PHONE_NUMBER>-52-6

This book focuses on virtual reality. In the context of design, virtual reality is an emerging technology that not only allows designers and other stakeholders to gain a threedimensional appreciation of the artifact being designed, it also has the potential to significantly alter the manner in which design occurs. Internet-based technologies have made it possible for designers in different locations to collaborate in developing and refining their designs. Virtual reality has contributed to this environment (Maher, 2005) by allowing designers in geographically-dispersed locations to interact with each other. Software applications have been developed to assist and facilitate these collaborative activities (including Shyamsundar and Gadh (2001) and Lau, Mak and Lu (2003)) but comparatively speaking, little research has been conducted into the people-related issues of collaboration via the Internet. Some of these are the issues addressed in this chapter.

Recent developments in virtual communication technologies have the potential to dramatically improve collaboration in the construction industry (Gameson & Sher, 2002). Furthermore, virtual teams "hold significant promise for organizations that implement them because they enable unprecedented levels of flexibility and responsiveness" (Powell, Piccoli, & Ives, 2004, p. 6). Some authors observe that virtual teams are here to stay (Bell & Kozlowski, 2002) and that organisations will be forced to "embrace virtual collaboration to enhance their competitiveness" (Abuelmaatti & Rezgui, 2008, p. 351). Indeed, current research proposes that "(g)lobally disbursed project teams are the new norm in every industry today" (Daim et al., 2012). However, the skills required to work productively in virtual environments have been theoretically defined but not assessed in the real world. Indeed, many of the studies that have been conducted (e.g. Hatem, Kwan and Miles (2011) and Rezgui (2007)) into virtual teamwork have involved tertiary-level students. Abelmaatti and Rezgui (2008) consider that the challenges of virtual teamwork in the real world substantially outweigh the relative ease with which academics can research and develop virtual team solutions. Furthermore, the differences between virtual and face-to-face teamwork means that an overt and explicit effort is needed to design new work processes to make it successful (Nunamaker, Reinig, & Briggs, 2009).

Changing Skills in Changing Environments: Skills Needed in Virtual Construction Teams 33

variables contributing to effective virtual teamwork (Gaudes, Hamilton-Bogart, Marsh, & Robinson, 2007; Peña-mora, Vadhavkar, & Aziz, 2009). Virtual teams may differ significantly depending upon these aspects and Dubé and Paré suggest that this table could also be used as a diagnostic tool to help assess the level of complexity in a virtual team.

There is still much discussion about the core set of knowledge, skills and attitudes that constitute teamwork (Salas, et al., 2000). We sought to contribute to this debate by identifying the skills that transferred from a traditional face to face (F2F) environment and the ones that required refining for virtual environments. Furthermore, we wished to identify if virtual teamworkers needed any new skills. As a starting point, we investigated the generic skills workers acquire and use on a daily basis. Generic skills are defined by Salas et al (2000: p, 344) as "the knowledge, skills and attitudes that a team member possesses when completing a task or communicating with fellow members, whether in a co-located or virtual environment". Generic skills influence both individuals and teams; they are skills which are "…transportable and applicable across teams" (Salas, et al., 2000, p. 344). A review of generic skills (Cannon-Bowers, Tannenbaum, Salas, & Volpe, 1995) was used to identify those which are used by design team members and is summarised in Table 2.

To examine the skills designers use it is necessary to understand the content of their interactions. A number of techniques facilitate such insights including Protocol Analysis and Content Analysis. Protocol Analysis attempts to infer cognitive processes by examining verbal interactions (Ericsson & Simon, 1993) but has been found to be a limited means of identifying non-verbal design cognition. Even where some comparisons are discovered, a large degree of interpretation is required (Cross, Christiaans, & K., 1996). The subjectivity of analysis and the length of time required to complete analysis also call into question the

Content Analysis, according to (Wallace, 1987), involves coding transcripts of communications in terms of frequency analyses because the underlying assumption is that "the verbal content produced by the individual is representative of the thought processes at

Several content analysis techniques were used to identify and interpret these thought processes and thereby to investigate the generic skills our participants used. We explored micro-level communication processes because these "can provide valuable insights to managers and researchers alike about how to 'read' the health of teams" (Kanawattanachai & Yoo, 2002: p. 210). We identified quantitative content analysis as an effective means of identifying the generic skills of designers. This necessitated the development of a framework by which our data could be coded. Behavioural marker studies (Klampfer, et al., 2001, Carthey, de Leval, Wright, Farewell, & Reason, 2003) provided a template for our generic skills coding framework. Behavioural markers are observable non-technical "aspects of individual and team performance" (Carthey et al, 2003: p. 411) which are related to the effectiveness of an individual and team. The methods for creating behavioural markers informed the development of our framework. In accordance with Klampfer et al's (2001) recommendations, we devised a system that provided simple, clear markers, used appropriate professional terminology, and emphasised observable behaviours rather than

**3. Generic skills**

appropriateness of this method.

work in his or her mind" (p. 121).

Our studies were part of a project which examined the use of information and computer technologies (ICTs) to facilitate design / construction team interactions. They were funded by the Australian Cooperative Research Centre for Construction Innovation (Maher, 2002) and focused on the early stages of design / construction collaboration where designs for a building are created, developed and revised. Three aspects of collaboration in virtual environments were investigated: (i) the technological processes that enable effective collaboration using these technologies; (ii) the models that allow disciplines to share their views in a synchronous virtual environment; (iii) the generic skills used by individuals and teams when engaging with high bandwidth ICT. The last strand of these investigations was investigated by the authors and is reported on here. Details of the other strands of this project may be found at the project website (Maher, 2002) and other publications (Bellamy, Williams, Sher, Sherratt, & Gameson, (2005) and Sherratt, Sher, Williams, & Gameson, 2010).


**2. Related work**

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

portion size from food images remains uncertainty.

Network (CNN) and Recurrent Neural Network (RNN).

*Limitation of integrating the purchase transaction over different database.*

tool to enrich our life. It can be defined as a system that will recommend items to the

Accordingly, to achieve better food recommendation, it would be useful to analyze foods that people are actually eating in daily life. POS (Point of Sale) is a large-scale transaction data relevant to the customer's purchase tendency [6]. The data is used only by individual store and not open for public. Therefore we cannot analyze the food purchase data among different stores, restaurants, canteens, and so on. *Amazon Go* is a smart store where a purchase transaction can be detected by a camera. Comparing with the POS system, the *Amazon Go* provides an automatic management of information about the foods that people bought, including the items that associated with those products and the appearance of each item with individual preference [7]. In addition, the system can predict the expectation of the market. Note that this kind of *Amazon-Go-like* system has similar constraint for collecting big data. The obtained database from different sources thus is varied. Therefore we can say that there is a limitation of integrating the purchase transaction over different database as shown in

From the diagram, it seems to be meaningful to create a system that analyzes the big data of food-images from various communities including companies, restaurants, and groups in social network system for extracting the people's preference of food combination, food design, and food appearance by applying the image recognition technology. In the field of learning representation, there are many established models such as Artificial Neural Network (ANN), Convolutional Neural

ANN is a broad term that encompasses any form of deep learning model. It can be either shallow or deep depending on the number of hidden layers. CNNs are designed specifically for computer vision. They are different from standard layers of ANNs as they are constructed to receive and process pixel data. RNNs are the "time series version" of ANNs. They are meant to process the sequences of data. They are at the basis of forecasting models and language models. The most common kind of recurrent layers are called LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). They contain a series of small, in-scale ANNs that are able

There was demonstration that digital imaging could estimate food information in many environments and it had many advantages over other methods [4, 5]. However, to derive the food information such as food type, food combination and

users/customers within an environment depending on their past activities.

**74**

**Figure 1.**

**Figure 1**.

Food image recognition is one of the promising applications of visual object application, as it will help estimate food characteristics and analyze people's eating choices for daily life. Many research works represented food recognition more practical by using the convolutional neural network (CNN) model [10–12]. CNN was applied to the tasks of food detection and recognition through parameter optimization. A dataset of the most frequent food items was constructed in a publicly available food-logging system. The CNN showed significantly higher accuracy than a conventional method did. In addition, the color feature is not always helpful for improving the accuracy by comparing the results of two group of controlled trials. It was reported that the achievement of CNN model was at 70–80% on one dataset and 60% on the multi-food dataset. The improvements could be expected by collecting more images and optimizing the network architecture and hyper-parameters.

For example, Deep Convolutional Neural Network (DCNN) was introduced for food recognition based on a combination of CNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning and activation features extracted from the pre-trained CNN [13, 14]. Another approach was based on two main steps: firstly, to produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, secondly, to recognize each of the food types or food-related objects presented in each bounding box [15]. Interestingly, the Max-Pooling function was used for the data and the features extracted from this function were used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 data set was recognized [16]. It was found that the image classification could be extended using prominent features that could categorize food images. Note that the feature-based approach and the multi-level classification approach (hierarchical approach) were highly appreciable to avoid mis-classifications when the number of classes was increased. However, these methodologies consumed high computational time.

#### **2.1 Concept of convolutional neural network (CNN)**

Convolutional neural network is a network that employs a mathematical operation called convolution. There are two main processes in CNN architecture – Learning extraction and Classification [17].

#### **Step-1: Learning extraction**

This process executes feature extraction from images through the following three layers -

a.Convolution layer: this is the first layer to extract features from an input image. There are matrix filters (feature map) that multiplies with image in order to extract some features such as edge, blur, and color.