Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.

Non-destructive testing (NDT) techniques play an important role in the characterization and diagnosis of historic buildings, keeping in mind their conservation and possible rehabilitation. This paper presents a new approach that merges building information modeling (BIM) with environment geospatial data obtained by several non-destructive techniques, namely terrestrial laser scanning, ground-penetrating radar, infrared thermography, and the automatic classification of pathologies based on RGB (red, green, blue) imaging acquired with an unmanned aircraft system (UAS). This approach was applied to the inspection of the Monastery of Batalha in Leiria, Portugal, a UNESCO World Heritage Site. To assess the capabilities of each technique, different parts of the monastery were examined, namely (i) part of its west façade, including a few protruding buttresses, and (ii) the masonry vaults of the Church (nave, right-hand aisle, and transept) and the Founder’s Chapel. After describing the employed techniques, a discussion of the optimization, treatment and integration of the acquired data through the BIM approach is presented. This work intends to contribute to the application of BIM in the field of cultural heritage, aiming at its future use in different activities such as facility management, support in the restoration and rehabilitation process, and research.

The abundance of beach litter has been increasing globally during the last decades, and it is an issue of global concern. A new survey strategy, based on uncrewed aerial vehicles (UAV, aka drones), has been recently adopted to improve the monitoring of beach macro-litter items abundance and distribution. This work identified and analysed the 15 studies that used drone for beach litter surveys on an operational basis. The analysis of technical parameters for drone flight deployment revealed that flight altitude varied between 5 and 40 m. The analysis of final assessments showed that, through manual and/or automated items detection on images, most of studies provided litter bulk characteristics (type, material and size), along with litter distribution maps. The potential standardization of drone-based litter survey would allow a comparison among surveys, however it seems difficult to propose a standard set of flight parameters, given the wide variety of coastal environments, the different devices available, and the diverse objectives of drone-based litter surveys. On the other hand, in our view, a set of common outcomes can be proposed, based on the grid mapping process, which can be easily generated following the procedure indicated in the paper. This work sets the ground for the development of a standardized protocol for drone litter data collection, analysis and assessments. This would allow the provision of broad scale comparative studies to support coastal management at both national and international scales.

To predict the degradation of concrete structures is extremely challenging. The typical approach combines periodic visual inspections with required non-destructive tests. However, this methodology only discretely evaluates few areas of the structure, being also time consuming and subject to human error. Therefore, a new method designated ‘automatic concrete health monitoring’ is herein presented which aims at automatically characterising and monitoring the state of conservation of concrete surfaces by combining photogrammetry, image processing and multi-spectral analysis. The method was designed to (i) characterise crack pattern, displacement and strain fields; (ii) map damages and (iii) assess and define restoration tasks.