Abstract
The evaluation of the presented procedural modelling methodology will be addressed in this chapter. Thus, a set of tests made to demonstrate the capabilities of this methodology in producing buildings compliant with a real subset of architectural rules (RGEU [1]) and others with distinct formats and different architectonic structures will be presented. Moreover, the effectiveness of the treemap approach in subdividing random layouts is shown, along with a generic stochastic process for automatic building generation and also some computational performance measurements that point out to the methodology expeditiousness. © The Author(s) 2016.

Abstract
This chapter provides an overview of the procedural modelling methodology that is addressed in this book. With the purpose of pointing out its need, the current issues in Procedural Modelling will be highlighted. In addition, the justification of some strategic decisions made during the development activities will be presented along with a brief enlightenment of the aforementioned methodology. © The Author(s) 2016.

Abstract

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.

Abstract
Vineyard parcels delimitation is a preliminary but important task to support zoning activities, which can be burdensome and time-consuming when manually performed. In spite of being desirable to overcome such issue, the implementation of a semi-/fully automatic delimitation approach can meet serious development challenges when dealing with vineyards like the ones that prevail in Douro Region (north-east of Portugal), mainly due to the great diversity of parcel/row formats and several factors that can hamper detection as, for example, interrupted rows and inter-row vegetation. Thereby, with the aim of addressing vineyard parcels detection and delimitation in Douro Region, a preliminary method based on segmentation and morphological operations upon high-resolution aerial imagery is proposed. This method was tested in a data set collected from vineyards located at the University of Trás-os-Montes and Alto Douro(Vila Real, Portugal). The presence of some of the previously mentioned challenging conditions - namely interrupted rows and inter-row grassing - in a few parcels contributed to lower the overall detection accuracy, pointing out the need for future improvements. Notwithstanding, encouraging preliminary results were achieved. © 2018 Association for Computing Machinery.

Abstract
Augmented Reality (AR) has been widely used in areas such as medicine, education, entertainment and cultural heritage to enhance activities that include (but are not limited to) teaching, training and amusement, through the completion of the real world with viewable and usually interactive virtual data (e.g. 3D models, geo-markers and labels). Despite the already confirmed AR benefits in the referred areas, many of the existing AR systems rely on heavy and obsolete hardware bundles composed of several devices and numerous cables that usually culminate in considerably expensive solutions. This issue is about to be tackled through the recent technological developments which currently enable the production of small-sized boards with remarkable capabilities - such as processing, visualization and storage - at relatively low prices. Following this line of reasoning, this paper proposes and compares five different multi-purpose AR mobile units, running Windows or Android operating systems, having in mind low-cost and lightweight requirements and different levels of immersion: a laptop computer, two tablets, a smartphone and smartglasses. A set of tests was carried out to evaluate the proposed unit performance. Moreover, a set of users' assessments was also conducted, highlighting an overall acceptance regarding the use of the proposed units in AR applications. This paper is an extension of a previous work (Pádua et al., 2015) in which a conceptual architecture for mobile units - complying with AR requirements (including visualization, processing, location and communication) for indoor or outdoor utilization - was presented, along with a shorter set of lightweight and cost-effective AR mobile units and respective performance tests. © 2018, IGI Global.