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Publications

2020

Digital Reconstitution of Road Traffic Accidents: A Flexible Methodology Relying on UAV Surveying and Complementary Strategies to Support Multiple Scenarios

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

Publication
International Journal of Environmental Research and Public Health

Abstract
The reconstitution of road traffic accidents scenes is a contemporary and important issue, addressed both by private and public entities in different countries around the world. However, the task of collecting data on site is not generally focused on with the same orientation and relevance. Addressing this type of accident scenario requires a balance between two fundamental yet competing concerns: (1) information collecting, which is a thorough and lengthy process and (2) the need to allow traffic to flow again as quickly as possible. This technical note proposes a novel methodology that aims to support road traffic authorities/professionals in activities involving the collection of data/evidences of motor vehicle collision scenarios by exploring the potential of using low-cost, small-sized and light-weight unmanned aerial vehicles (UAV). A high number of experimental tests and evaluations were conducted in various working conditions and in cooperation with the Portuguese law enforcement authorities responsible for investigating road traffic accidents. The tests allowed for concluding that the proposed method gathers all the conditions to be adopted as a near future approach for reconstituting road traffic accidents and proved to be: faster, more rigorous and safer than the current manual methodologies used not only in Portugal but also in many countries worldwide.

2020

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

Authors
Padua, L; Adao, T; Sousa, A; Peres, E; Sousa, JJ;

Publication
Remote Sensing

Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

2020

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

Authors
Padua, L; Guimaraes, N; Adao, T; Sousa, A; Peres, E; Sousa, JJ;

Publication
ISPRS International Journal of Geo-Information

Abstract
Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

2019

Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle

Authors
Pádua, L; Guimarães, N; Adão, T; Marques, P; Peres, E; Sousa, AMR; Sousa, JJ;

Publication
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract

2019

Digital Ampelographer: A CNN Based Preliminary Approach

Authors
Adão, T; Pinho, TM; Ferreira, A; Sousa, AMR; Pádua, L; Sousa, J; Sousa, JJ; Peres, E; Morais, R;

Publication
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract