Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery

The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy.This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Grant Agreement No. 847402. The authors would like to thank the EPA-funded iHabiMap project for providing the data used in this work. We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. The authors declare no conflicts of interes

Monitoring threatened irish habitats using multi-temporal multispectral aerial imagery and convolutional neural networks

The monitoring of threatened habitats is a key objective of European environmental policy. Due to the high cost of current field-based habitat mapping techniques there is a strong research interest in proposing solutions that reduce the cost of habitat monitoring through increasing their level of automation. Our work is motivated by the opportunities that recent advances in machine learning and Unmanned Aerial Vehicles (UAVs) offer to the habitat monitoring problem. In this paper, a deep learning based solution is proposed to classify four priority Irish habitats types present in the Maharees (Ireland) using UAV aerial imagery. The proposed method employs Convolutional Neural Networks (CNNs) to classify multi-temporal multi-spectral images of the study area corresponding to three different dates in 2020, obtaining an overall classification accuracy of 93%. A comparison of the proposed method with a multi-spectral 2D-CNN model demonstrates the advantage of including temporal information enabled by the proposed multi-temporal multi-spectral CNN model.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847402

Rank-based ant system with originality reinforcement and pheromone smoothing

Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark.This research work was funded by the European project PDE-GIR of the European Union’s Horizon 2020 research & innovation program (Marie Sklodowska-Curie action, grant agreement No 778035), and by the Spanish government project #PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU “Una manera de hacer Europa”

Crop classification from Sentinel-2 time series with temporal convolutional neural networks

Automated crop identification tools are of interest to a wide range of applications related to the environment and agriculture including the monitoring of related policies such as the European Common Agriculture Policy. In this context, this work presents a parcel-based crop classification system which leverages on 1D convolutional neural network supervised learning capacity. For the training and evaluation of the model, we employ open and free data: (i) time series of Sentinel-2 optical data selected to cover the crop season of one year, and (ii) a cadastre-derived database providing detailed delineation of parcels. By considering the most dominant crop types and the temporal features of the optical data, the proposed lightweight approach discriminates a considerable number of crops with high accuracy

Minimum time search in real-world scenarios using multiple UAVs with onboard orientable cameras

This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.This work was supported by Airbus under SAVIER AER30459 projec

Ant colony optimization for multi-UAV minimum time search in uncertain domains

This paper presents a new approach based on ant colony optimization (ACO) to determine the trajectories of a fleet of unmanned air vehicles (UAVs) looking for a lost target in the minimum possible time. ACO is especially suitable for the complexity and probabilistic nature of the minimum time search (MTS) problem, where a balance between the computational requirements and the quality of solutions is needed. The presented approach includes a new MTS heuristic that exploits the probability and spatial properties of the problem, allowing our ant based algorithm to quickly obtain high-quality high-level straight-segmented UAV trajectories. The potential of the algorithm is tested for different ACO parameterizations, over several search scenarios with different characteristics such as number of UAVs, or target dynamics and location distributions. The statistical comparison against other techniques previously used for MTS (ad hoc heuristics, cross entropy optimization, bayesian optimization algorithm and genetic algorithms) shows that the new approach outperforms the others.This work was supported by Airbus under the SAVIER AER-30459 project

Planificador de búsqueda en tiempo mínimo en un sistema de control de RPAS

[Resumen] El gran número de nuevas aplicaciones y misiones en las que intervienen RPAS (Remotely Piloted Aircraft System) y su creciente complejidad conlleva un notable interés por la investigación sobre la incorporación de nuevas tecnologías en sistemas de control de RPAS. En concreto, será necesario incorporar nuevos automatismos en las Estaciones de Control de Tierra (GCS, Ground Control Station) para evitar la sobrecarga del operador y conseguir un desempeño óptimo de las misiones. Entre las distintas misiones que lleva a cabo un operador de RPAS, se encuentran las tareas de búsqueda en tiempo mínimo (MTS, Minimum Time Search), en las cuales uno o más objetivos necesitan ser encontrados lo antes posible. Por lo tanto, un planificador MTS, que proponga al operador trayectorias de búsqueda óptimas y permita visualizar la información más relevante sobre la misión, puede ser de gran utilidad, al reducir parte de la carga de trabajo del operador durante las misiones de búsqueda. En este artículo se presenta un nuevo planificador MTS y su proceso de integración en una GCS. Como fruto de la integración se pueden validar los resultados del planificador en entornos simulados complejos y se ha dotado a la GCS de nuevas capacidades de planificación.https://doi.org/10.17979/spudc.978849749808

Diagnostico de caries mediante dos herramientas en el grado de Odontología: DIAGNOdent™ pen. e Inteligencia Artificial

Depto. de Odontología Conservadora y PrótesisFac. de OdontologíaFALSEsubmitte

Simulación Numérica de la propagación de ondas electromagnéticas mediante el método Leapfrog ADI-FDTD

Los métodos en diferencias finitas permiten resolver numéricamente las ecuaciones de Maxwell en el dominio del tiempo. El primer método FDTD fue propuesto por Kane Yee en 1966, y es el más utilizado su simplicidad y eficacia. En los métodos FDTD el dominio computacional se divide en celdas y los valores de los campos eléctrico y magnético se calculan a partir de los valores de los campos en instantes anteriores. El método FDTD propuesto por Yee presenta buenos resultados, sin embargo es un método condicionalmente estable, y en casos que requieren un mallado espacial fino resulta demasiado lento. Posteriormente, como solución a este problema, se desarrollaron los métodos incondicionalmente estables como el ADI-FDTD, en los cuales la elección del incremento temporal depende únicamente de la exactitud requerida. En el método ADI-FDTD los valores de los campos se calculan dos veces por cada iteración temporal. Recientemente se ha propuesto un nuevo método, denominado Leapfrog ADI-FDTD, que es también incondicionalmente estable, pero que al igual que el FDTD convencional calcula los valores de los campos una única vez por cada iteración temporal. Esto hace que sea un método más eficiente que su predecesor, el ADI-FDTD. El objetivo de este trabajo de fin de carrera es implementar el método Leapfrog ADI-FDTD para la polarización TMz y estudiar sus propiedades numéricas.Licenciatura en Físic