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About

Emanuel Soares Peres Correia is an assistant professor at Trás-os-Montes e Alto Douro University (UTAD) in Portugal. He has been lecturing in areas such as web development, electronics, power electronics, telecommunications and computer networks since 2003. He is an integrated researcher at the Centre for Robotics in Industry and Intelligent Systems (CRIIS) in Technology and Science Associate Laboratory (INESC-TEC) and his research focuses on combining sensing networks, in-field processing units and mesh communication networks to develop data acquisition systems which enable decision support tools for precision agriculture. Human–Machine interaction, namely augmented reality systems and new interfaces, in areas such as education, agriculture and tourism, is also an area of interest. His research has been presented at international conferences such as CENTERIS, EDUCON, CISTI, CSEDU and SPIE and he has been published in the e.g. Journal of Computers and Electronics in Agriculture, Journal of Remote Sensing, International Journal of Remote Sensing, Journal of Theoretical and Applied Electronic Commerce Research, Journal of Applied Logic and Procedia Technology. He is an Editor-in-Chief of the International Journal of Web Portals (IJWP) since November 2016. As a member of UTAD’s Urban Eco-Efficiency Unit, he is currently involved in several research projects mainly related with the application of UAV in agriculture and forestry.

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Details

Details

Publications

2019

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

Authors
Marques, P; Padua, L; Adao, T; Hruska, J; Peres, E; Sousa, A; Sousa, JJ;

Publication
Remote Sensing

Abstract
Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameters—such as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R2 = 0.86), and the crown diameter (RMSE of 0.44 m and R2 = 0.96)—were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

2019

IMPLEMENTATION OF E-LEARNING AT THE UNIVERSITY OF TRÁS-OS-MONTES E ALTO DOURO: STUDENTS' PERSPECTIVES

Authors
Vaz, C; Borges, J; Peres, E; Sousa, J; Reis, M;

Publication
INTED2019 Proceedings

Abstract

2019

mySense: A comprehensive data management environment to improve precision agriculture practices

Authors
Morais, R; Silva, N; Mendes, J; Adao, T; Padua, L; Lopez Riquelme, J; Pavon Pulido, N; Sousa, JJ; Peres, E;

Publication
Computers and Electronics in Agriculture

Abstract

2019

Procedural Modeling of Buildings Composed of Arbitrarily-Shaped Floor-Plans: Background, Progress, Contributions and Challenges of a Methodology Oriented to Cultural Heritage

Authors
Adao, T; Padua, L; Marques, P; Sousa, JJ; Peres, E; Magalhaes, L;

Publication
Computers

Abstract
Virtual models' production is of high pertinence in research and business fields such as architecture, archeology, or video games, whose requirements might range between expeditious virtual building generation for extensively populating computer-based synthesized environments and hypothesis testing through digital reconstructions. There are some known approaches to achieve the production/reconstruction of virtual models, namely digital settlements and buildings. Manual modeling requires highly-skilled manpower and a considerable amount of time to achieve the desired digital contents, in a process composed by many stages that are typically repeated over time. Both image-based and range scanning approaches are more suitable for digital preservation of well-conserved structures. However, they usually require trained human resources to prepare field operations and manipulate expensive equipment (e.g., 3D scanners) and advanced software tools (e.g., photogrammetric applications). To tackle the issues presented by previous approaches, a class of cost-effective, efficient, and scarce-data-tolerant techniques/methods, known as procedural modeling, has been developed aiming at the semi- or fully-automatic production of virtual environments composed of hollow buildings exclusively represented by outer façades or traversable buildings with interiors, either for expeditious generation or reconstruction. Despite the many achievements of the existing procedural modeling approaches, the production of virtual buildings with both interiors and exteriors composed by non-rectangular shapes (convex or concave n-gons) at the floor-plan level is still seldomly addressed. Therefore, a methodology (and respective system) capable of semi-automatically producing ontology-based traversable buildings composed of arbitrarily-shaped floor-plans has been proposed and continuously developed, and is under analysis in this paper, along with its contributions towards the accomplishment of other virtual reality (VR) and augmented reality (AR) projects/works oriented to digital applications for cultural heritage. Recent roof production-related enhancements resorting to the well-established straight skeleton approach are also addressed, as well as forthcoming challenges. The aim is to consolidate this procedural modeling methodology as a valuable computer graphics work and discuss its future~directions.

2019

USING VIRTUAL SCENARIOS TO PRODUCE MACHINE LEARNABLE ENVIRONMENTS FOR WILDFIRE DETECTION AND SEGMENTATION

Authors
Adão, T; Pinho, TM; Pádua, L; Santos, N; Sousa, A; Sousa, JJ; Peres, E;

Publication
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Abstract
<p><strong>Abstract.</strong> Today’s climatic proneness to extreme conditions together with human activity have been triggering a series of wildfire-related events that put at risk ecosystems, as well as animal and vegetal patrimony, while threatening dwellers nearby rural or urban areas. When intervention teams - firefighters, civil protection, police - acknowledge these events, usually they have already escalated to proportions hardly controllable mainly due wind gusts, fuel-like solo conditions, among other conditions that propitiate fire spreading.</p> <p>Currently, there is a wide range of camera-capable sensing systems that can be complemented with useful location data - for example, unmanned aerial systems (UAS) integrated cameras and IMU/GPS sensors, stationary surveillance systems - and processing components capable of fostering wildfire events detection and monitoring, thus providing accurate and faithful data for decision support. Precisely in what concerns to detection and monitoring, Deep Learning (DL) has been successfully applied to perform tasks involving classification and/or segmentation of objects of interest in several fields, such as Agriculture, Forestry and other similar areas. Usually, for an effective DL application, more specifically, based on imagery, datasets must rely on heavy and burdensome logistics to gather a representative problem formulation. What if putting together a dataset could be supported in customizable virtual environments, representing faithful situations to train machines, as it already occurs for human training in what regards some particular tasks (rescue operations, surgeries, industry assembling, etc.)?</p> <p>This work intends to propose not only a system to produce faithful virtual environments to complement and/or even supplant the need for dataset gathering logistics while eventually dealing with hypothetical proposals considering climate change events, but also to create tools for synthesizing wildfire environments for DL application. It will therefore enable to extend existing fire datasets with new data generated by human interaction and supervision, viable for training a computational entity. To that end, a study is presented to assess at which extent data virtually generated data can contribute to an effective DL system aiming to identify and segment fire, bearing in mind future developments of active monitoring systems to timely detect fire events and hopefully provide decision support systems to operational teams.</p>

Supervised
thesis

2017

Tecnicas e tecnologias para desenvolvimento de Front-End na Empresa PontoPR-Inovação Digital

Author
Moisés Silva de Paiva

Institution
UTAD

2017

Plataformas interativas de vídeo: uma proposta de implementação

Author
Miguel Ângelo Ferreira Fonseca

Institution
UTAD

2017

Estudo de técnicas e de tecnologias para o desenvolvimento de Frontend de aplicações web

Author
Alexis José Rodrigues da Silva

Institution
UTAD

2016

E-learning na Universidade de Trás-os-Montes e Alto Douro

Author
Carlos Manuel Rodrigues Soares Vaz

Institution
UTAD

2016

Fotografia panorâmica 3D

Author
Ruben Tiago da Silva Bento Craveiro

Institution
UTAD