Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

X-ray per la diagnosi di Covid19 con Deep Convolutional Neural Network

Questa tesi di laurea compie uno studio sull' utilizzo di reti neurali convoluzionali per la diagnosi di covid-19 attraverso l' utilizzo di radiografie. Dopo una breve introduzione sul deep learning, sul funzionamento delle reti convoluzionali e del loro attuale impiego in ambito medico verrà progettato e implementato un modello basato sull' architettura ResNet50 e adattato per renderlo maggiormente funzionale al task di binary classification proposto, questo semplice modello viene poi allenato e testato sul dataset più esteso ed aggiornato esistente e infine confrontato con lo stato dell' arte delle reti che affrontano lo stesso problema in letteratura. Verrà poi costruita e allenata una rete from scratch da confrontare a quella usata nel tentativo di coglierne similarità e differenze. In ultima istanza viene condotta un' analisi dell' approccio usato evidenziandone le ragioni d'essere (dalle reti convoluzionali all' explainability dei risultati) e i problemi derivati (quantità di dati limitata, bias etc.)

Explaining deep convolutional models by measuring the influence of interpretable features in image classification

The accuracy and flexibility of Deep Convolutional Neural Networks (DCNNs) have been highly validated over the past years. However, their intrinsic opaqueness is still affecting their reliability and limiting their application in critical production systems, where the black-box behavior is difficult to be accepted. This work proposes EBANO, an innovative explanation framework able to analyze the decision-making process of DCNNs in image classification by providing prediction-local and class-based model-wise explanations through the unsupervised mining of knowledge contained in multiple convolutional layers. EBANO provides detailed visual and numerical explanations thanks to two specific indexes that measure the features’ influence and their influence precision in the decision-making process. The framework has been experimentally evaluated, both quantitatively and qualitatively, by (i) analyzing its explanations with four state-of-the-art DCNN architectures, (ii) comparing its results with three state-of-the-art explanation strategies and (iii) assessing its effectiveness and easiness of understanding through human judgment, by means of an online survey. EBANO has been released as open-source code and it is freely available online

Double trouble. Synergy between habitat loss and the spread of the alien species Caulerpa cylindracea (Sonder) in three Mediterranean habitats

The role of habitat degradation on the spread of the alien green alga Caulerpa cylindracea is reported here by comparing observations achieved through a multi-year assessment on three Mediterraneans habitats, namely Posidonia oceanica meadows, Phyllophora crispa turf, and coralligenous reefs. Due to the peculiarity of the study site, both natural-reference and impacted conditions were investigated. C. cylindracea occurred in all the studied habitats under impacted conditions. High susceptibility to the invasion characterized impacted P. oceanica, where Caulerpa cover reached 70.0% in summer months. C. cylindracea cover did not differ significantly among conditions in P. crispa turf, where values never exceeded 5.0%. Conversely, the invasive green algae was low in abundance and patchily distributed in coralligenous reefs. Our results confirmed that habitat loss enhances the spread of C. cylindracea, although with different magnitudes among habitats. Dead matte areas of P. oceanica represented the most vulnerable habitat among those analyzed, whereas coralligenous reefs were less susceptible to the invasion under both the studied conditions

Unmanned Aerial Systems (UASs) for Environmental Monitoring: A Review with Applications in Coastal Habitats

Nowadays the proliferation of small unmanned aerial systems or vehicles (UAS/Vs), formerly known as drones, coupled with an increasing interest in tools for environmental monitoring, have led to an exponential use of these unmanned aerial platforms for many applications in the most diverse fields of science. In particular, ecologists require data collected at appropriate spatial and temporal resolutions to describe ecological processes. For these reasons, we are witnessing the proliferation of UAV-based remote sensing techniques because they provide new perspectives on ecological phenomena that would otherwise be difficult to study. Therefore, we propose a brief review regarding the emerging applications of low-cost aerial platforms in the field of environmental sciences such as assessment of vegetation dynamics and forests biodiversity, wildlife research and management, map changes in freshwater marshes, river habitat mapping, and conservation and monitoring programs. In addition, we describe two applications of habitat mapping from UAS-based imagery, along the Central Mediterranean coasts, as study cases: (1) The upper limit of a Posidonia oceanica meadow was mapped to detect impacted areas, (2) high-resolution orthomosaic was used for supporting underwater visual census data in order to visualize juvenile fish densities and microhabitat use in four shallow coastal nurseries

Colonization of transplanted Posidonia oceanica. Understanding the spatial dynamics through high-spatial resolution underwater photomosaics

Following the restoration of a Posidonia oceanica meadow impacted by theConcordia shipwreck, we investigated the spatial dynamic of the most important andprotected Mediterranean endemic seagrass over a two-year period applying three spatialmetrics: number of patches, mean patch size and total cover. By means of underwaterphotomosaics, we noticed a diminution in the number of patches in favour of the mean sizeand total cover. The outcomes showed that, under suitable environmental conditions,P. oceanica colonizes rapidly the dead matte substrate. This study underlines the importanceof considering the spatial dynamic of transplanted seagrasses in monitoring programmes andgives new insights on the progression rate of transplanted P. oceanica

Robust plan execution with unexpected observations

In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which committing to a wrong ordering of actions can cause the plan failure, even when all the actions succeed. We propose an approach to the execution of a task plan that permits some adaptability to unexpected observations of the state while maintaining the validity of the plan through online reasoning. Our approach computes an adaptable, partially-ordered plan from a given totally-ordered plan. The partially-ordered plan is adaptable in that it can exploit beneficial differences between the world and what was expected. The approach is general in that it can be used with any task planner that produces either a totally or a partially-ordered plan. We propose a plan execution algorithm that computes online the complete set of valid totally-ordered plans described by an adaptable partially-ordered plan together with the probability of success for each of them. This set is then used to choose the next action to execute

Automating concept-drift detection by self-evaluating predictive model degradation

A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In this paper, we propose a novel methodology to automatically detect prediction-quality degradation of machine learning models due to class-based concept drift, i.e., when new data contains samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions.Comment: 5 pages, 4 figure