Abstract
The viStaMPS software is a collaborative scientific project that was created with three major purposes: (1) facilitate the usage by users non familiar with the specificities of the programming language that supports StaMPS; (2) implement several visualization tasks not available in the StaMPS standard approach (avoiding that each user develop its own code for visualization and interpretation purposes) and (3) create a collaborative research project, continuously under development counting on the dynamism of its users to improve and/or add new features.

Abstract
The aim of this paper is twofold. Firstly, to present a survey of the actual and most advanced methods for man-made structures monitoring, more specifically dams and bridges. Theoretical and technical aspects of these methodologies are presented and discussed focusing on innovative inspection methods and on the opportunities that could deliver. Secondly, to identify the opportunities that could potentially improve the inspections and maintenance processes, being the satellite-based monitoring, using radar imagery, recognized as viable source of independent information products that may be used to remotely monitor the health of these specific man-made structures. By applying Multi-temporal InSAR processing techniques to a series of radar images over the same region, it is possible to detect vertical movements of structure systems on the ground in the millimeter range, and therefore, identify abnormal or excessive movement indicating potential problems requiring detailed ground investigation. In this paper it is clearly demonstrated that with the new high-resolution synthetic aperture radar satellites scenes, InSAR technology may be particular useful as hot spot indicator of relative deformations structures over large areas, making possible to develop interferometric based methodologies for structural health monitoring. From a technological standpoint, this approach represents a substantial evolution over the current state-of-the-art. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract
In the last decade, Synthetic Aperture Radar Interferometry (InSAR) has become operational as a technique that allows remote detection of deformation at the Earth's surface. Analysis of time series of SAR images extends the area where InSAR can be successfully applied and also permits detection of smaller displacements through the reduction of error sources. Stanford Method for Persistent Scatterers (StaMPS) InSAR implementation, which is based on the processing of multi-temporal SAR data, is widely used for ground deformation monitoring. This is due mainly to its proven reliability and freeware distribution among the scientific community. However, some issues can make the interpretation of the results a difficult task: StaMPS supports data processing based on command prompt, which increases the difficulty of usage by users not familiar with the specific programming language that supports StaMPS. Moreover, several visualization tasks are not implemented in the standard approach requiring that each user develop its own code for visualization and interpretation purposes. In this paper, we present viStaMPS, a new visual application developed to enhance the visualization, manipulation and exportation of StaMPS results. The programmed application is developed in Matlab through the Graphical User Interface (GUI) and no coding is required for running it, which avoids any programming language knowledge for standard uses. The included graphical interface is very versatile allowing the user to choose among several features: visualization, manipulation and exportation of data which are not available in the original StaMPS.

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
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.