Autumnal Beach Litter Identification by Mean of Using Ground-Based IR Thermography

Cagnazzo, Cosimo; Potente, Ettore; Regnauld, Hervé; Rosato, Sabino; Mastronuzzi, Giuseppe

doi:10.3390/environments8050037

Cited by 8 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…We could possibly use this finding in the TIR sensing of plastic pollution levels on beaches. Cagnazzo et al [37] recognized beach litter in thermal images as hotter spots in conditions of air that was warmer than the wet, sandy soil.…”

Section: Questions Answeredmentioning

confidence: 99%

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

et al. 2022

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In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the surface reflectance of sunlight, would benefit from complementary measurements using different technologies. Thermal infrared (TIR) sensing shows potential as a novel method for monitoring macro plastic litter floating on the water surface, as the physics behind surface-leaving TIR is different. We assessed a thermal radiance model for floating plastic litter using a small UAV-grade FLIR Vue Pro R 640 thermal camera by flying it over controlled floating plastic litter targets during the day and night and in different seasons. Experiments in the laboratory supported the field measurements. We investigated the effects of environmental conditions, such as temperatures, light intensity, the presence of clouds, and biofouling. TIR sensing could complement observations from VIS, NIR, and SWIR in several valuable ways. For example, TIR sensing could be used for monitoring during the night, to detect plastics invisible to VIS-SWIR, to discriminate whitecaps from marine litter, and to detect litter pollution over clear, shallow waters. In this study, we have shown the previously unconfirmed potential of using TIR sensing for monitoring floating plastic litter.

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Section: Questions Answeredmentioning

confidence: 99%

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

et al. 2022

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“…The geological framework of the Apulia region is determined by three main geological domains: the Apulia Foreland, the Bradanic Trough, and the Apennine Chain. These geological domains are related to the geodynamic processes that involved the Adria Plate and European Plate [61,62].…”

Section: Tectonic Settingmentioning

confidence: 99%

“…In the first phase, the westernmost sectors of the Apulian Foreland underwent strong subsidence [69], which was caused by subduction under the Apennine Chain. In this context, a Bradanic Trough sedimentary cycle [61,64] began to accumulate with coastal deposits belonging to the Calcarenite di Gravina Formation [72] culminating in silty clayey hemipelagic deposits belonging to the stratigraphic argille subappennine informal unit [73]. In the second geodynamic phase (from the middle Pleistocene), the entire Apulian Foreland and Bradanic Trough were uplifted [68,73,74].…”

Section: Tectonic Settingmentioning

confidence: 99%

Coastal Quarries as Relative Sea-Level Markers: A Methodological Approach Applied in the Apulia Region (Southern Italy)

Scardino

Piscitelli

Scicchitano

et al. 2022

Oceans

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The assessment of past sea-level positions requires a multidisciplinary approach that involves both scientific and historical humanistic fields. The use of a multidisciplinary approach allows us to obtain reliable information on the relative sea-level position, the determination of which requires the evaluation of the eustatic and steric components as well as an assessment of the vertical ground displacements, such as the isostatic adjustments and tectonic movements. In this context, coastal geoarchaeological markers play a fundamental role since their architectural height (generally defined as functional height) was relative to the sea level at the time of their construction. Thus, a comparison between the current elevation of geoarchaeological structures (or depth in the case they are currently submerged) with their estimated functional height allows us to obtain the relative sea-level variation. In this study, we applied a methodological procedure for the evaluation of the functional height of architectural elements using modern technologies (Terrestrial Laser Scanner and GPS-Real Time Kinematic) and detailed sea-level analysis. The proposed methodology was applied to coastal quarries located along the coast of Bari (Apulia region, southern Italy). The results allowed us to confirm the functional height of the detachment surface reported in the literature and to assess the sea-level position in the fifth and fourth centuries before Christ.

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“…They are especially pertinent in flood detection and monitoring [15], as UAVs are more capable of providing the necessary aerial images than their satellite counterparts [16]. Also, in the realm of ecological preservation, UAVs can capture greater detail than satellites, allowing ecologists and authorities to monitor the effect of tourism (e.g., 'trampling') in nature areas [17] or litter on beaches [18]. In the construction industry, UAVs support tasks including the surveying of earthworks, aspects of site management, progress monitoring, safety inspection and damage assessment [19].…”

Section: Introductionmentioning

confidence: 99%

Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

Schnieder

2024

Smart Cities

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Background: Drones, also known as unmanned aerial vehicles, could potentially be a key part of future smart cities by aiding traffic management, infrastructure inspection and maybe even last mile delivery. This paper contributes to the research on managing a fleet of soaring aircraft by gaining an understanding of the influence of the weather on soaring capabilities. To do so, machine learning algorithms were trained on flight data, which was recorded in the UK over the past ten years at selected gliding clubs (i.e., sailplanes). Methods: A random forest regressor was trained to predict the flight duration and a random forest (RF) classifier was used to predict whether at least one flight on a given day managed to soar in thermals. SHAP (SHapley Additive exPlanations), a form of explainable artificial intelligence (AI), was used to understand the predictions given by the models. Results: The best RF have a mean absolute error of 5.7 min (flight duration) and an accuracy of 81.2% (probability of soaring in a thermal on a given day). The explanations derived from SHAP are in line with the common knowledge about the effect of weather systems to predict soaring potential. However, the key conclusion of this study is the importance of combining human knowledge with machine learning to devise a holistic explanation of a machine learning model and to avoid misinterpretations.

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Autumnal Beach Litter Identification by Mean of Using Ground-Based IR Thermography

Cited by 8 publications

References 24 publications

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

Coastal Quarries as Relative Sea-Level Markers: A Methodological Approach Applied in the Apulia Region (Southern Italy)

Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

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