Abstract
Advances in Unmanned Aerial Systems (UAS) allowed them to become both flexible and cost-effective. When combined with computer vision data processing techniques they are a good way to obtain high-resolution imagery and 3D information. As such, UAS can be advantageous both for agriculture and forestry areas, where the need for data acquisition at specific times and within a specific time frame is crucial, enabling the extraction of several measurements from different crop types. In this study a low-cost UAS was used to survey an area mainly composed by chestnut trees (Castanea sativa Mill.). Flights were performed at different heights (ranging from 30 to 120 m), in single and double grid flight patterns, and photogrammetric processing was then applied. The obtained information consists of orthophoto mosaics and digital elevation models which enable the measurement of individual tree’s parameters such as tree crown diameter and tree height. Results demonstrate that despite its lower spatial resolution, data from single grid flights carried out at higher heights provided more reliable results than data acquired at lower flight heights. Higher number of images acquired in double grid flights also improved the results. Overall, the obtained results are encouraging, presenting a R2 higher than 0.9 and an overall root mean square error of 44 cm. © 2018 Association for Computing Machinery.

Abstract
The cost-effectiveness of unmanned aerial systems (UAS) makes them suitable platforms to survey cultural heritage sites. Developments in photogrammetry provide methods capable to generate accurate 3D models out of 2D aerial images. Considering the involved technologies, the purpose of this paper is to document the Chapel of Espiríto Santo: a very relevant monument for Vila Real (Portugal) that is currently located at the campus of the University of Trás-os-Montes and Alto Douro. The UAS-based aerial imagery survey approach is presented along with photogrammetric process to build chapel’s 3D model. Moreover, two photogrammetric software were compared – Pix4Dmapper Pro and Agisoft Photoscan – in terms of modelling accuracy and functionalities ease of use. © 2018 Association for Computing Machinery.

Abstract
To differentiate between canopy and vegetation cover is particularly challenging. Nonetheless, it is pivotal in obtaining the exact crops' vegetation when using remote-sensing data. In this article, a method to automatically estimate and extract vineyards' canopy is proposed. It combines vegetation indices and digital elevation models - derived from high-resolution images, acquired using unmanned aerial vehicles - to differentiate between vines' canopy and inter-row vegetation cover. This enables the extraction of relevant information from a specific vineyard plot. The proposed method was applied to data acquired from some vineyards located in Portugal's north-eastern region, and the resulting parameters were validated. It proved to be an effective method when applied with consumer-grade sensors, carried by unmanned aerial vehicles. Moreover, it also proved to be a fast and efficient way to extract vineyard information, enabling vineyard plots mapping for precision viticulture management tasks.

Abstract
While the popularity of virtual reality (VR) grows in a wide range of application contexts - e.g. entertainment, training, cultural heritage and medicine -, its economic impact is expected to reach around 15bn USD, by the year of 2020. Within VR field, 360video has been sparking the interest of development and research communities. However, editing tools supporting 360panoramas are usually expensive and/or demand programming skills and/or advanced user knowledge. Besides, application approaches to quickly and intuitively set up such 360video-based VR environments complemented with diverse types of parameterizable virtual assets and multimedia elements are still hard to find. Thereby, this paper aims to propose a system specification to simply and rapidly configure immersive VR environments composed of surrounding 360video spheres that can be complemented with parameterizable multimedia contents - namely 3D models, text and spatial sound -, whose behavior can be either time-range or user-interaction dependent. Moreover, a preliminary prototype that follows a substantial part of the previously mentioned specification and implements the enhancement of 360videos with time-range dependent virtual assets is presented. Preliminary tests evaluating usability and user satisfaction were also carried out with 30 participants, from which encouraging results were achieved. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.

Abstract
This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.