Grado en estadística
Máster en modelización matemática
9
Scopus Publications
Scopus Publications
From Wind to Smoke: A Unified WebGIS Platform for Wildfire Simulation and Visualization Saray Martínez-Lastras, José Manuel Iglesias, David Cifuentes-Jimenez, María Isabel Asensio, Diego González-Aguilera Fire, 2025 A unified WebGIS platform for wildfire simulation and visualization is presented, integrating three coupled physical models: HDWind for wind field computation, PhyFire for wildfire spread, and PhyNX for smoke plume dispersion. The system includes preprocessing and postprocessing scripts that enable the efficient integration of meteorological and cartographic data and support the visualization of outputs such as burned areas, wind and smoke fields, and emission estimates. The platform is deployed through a WebGIS interface that supports both decoupled and coupled simulations, providing operational flexibility and reducing computational demands when needed. A real wildfire scenario is simulated to demonstrate system capabilities. The case study highlights the platform’s applicability in operational contexts, reinforcing its potential to evolve into an accessible and user-oriented environmental decision support system for wildfire management.
qAicedrone-Roads. A Robust Tool for Road Marking Extraction Using Aerial Photogrammetry and U-Net Innes Barbero‐García, Saray Martínez‐Lastras, Ángel Marqués‐Mateu, David Hernández‐López Photogrammetric Record, 2025 Efficient and accurate road marking detection is essential for infrastructure maintenance, traffic management, and the development of digital twins for autonomous mobility. However, most existing methods rely on orthomosaics or single‐image detections, which suffer from geometric distortions and occlusions and have limited semantic insight. To address these limitations, this study introduces qAicedrone‐Roads, an open‐source tool integrated into QGIS that enables the automatic detection, classification, and mapping of road markings from UAV‐based photogrammetric imagery. The methodology combines U‐Net‐based semantic segmentation, a multiview photogrammetric approach, and alignment with a national road marking catalog to enhance geometric accuracy and assign semantic labels. Applied to a real‐world case study, the tool achieved high precision, with F1‐scores of 0.92 for nonlinear and 0.93 for linear markings, outperforming traditional single‐view Computer Vision (CV) methods. These results demonstrate the tool's robustness and accuracy in complex urban environments, enabling the efficient generation of detailed road marking datasets. By facilitating the scalable and reproducible creation of digital twins, qAicedrone‐Roads supports smart infrastructure monitoring and sustainable urban mobility planning.
Morphological and Environmental Drivers of Urban Heat Islands: A Geospatial Model of Nighttime Land Surface Temperature in Iberian Cities Gustavo Hernández-Herráez, Saray Martínez-Lastras, Susana Lagüela, José A. Martín-Jiménez, Susana Del Pozo Applied Sciences Switzerland, 2025 This study explores how urban morphological and environmental factors influence Urban Heat Islands (UHIs) using a geospatial modeling approach. The aim of the research is to develop a methodology to assess UHI effects, emphasizing the role of urban morphology, land use, and vegetation in nighttime heat accumulation. A micro-scale analysis with a 50 m resolution is conducted by integrating a custom QGIS plugin with open-access data, ensuring broad applicability. The 50 m resolution was chosen because it allows for the capture of local variations in UHI intensity while maintaining the scalability of the urban analysis across different city contexts. Non-parametric statistical analyses (ANOVA, Kruskal–Wallis H test, and correlation assessments) were used to evaluate the relationships between the urban parameters—wind corridors, altitude, vegetation (NDVI), surface water (NDWI), and the Sky View Factor (SVF)—and Nighttime Land Surface Temperature (LST). Given that UHI variations during summer, particularly in cities of the Iberian Peninsula, are closely linked to summer heat severity, this factor was considered to classify the cities for the study. Correlation analyses confirm that all tested factors influence LST, with wind corridors being the least significant. The model performance evaluation shows the highest errors in cities with lower summer severity (RMSE = 1.586 °C, MAE = 1.2686 °C, MAPE = 6.99%) and the best performance in warmer cities (RMSE = 1.4 °C, MAE = 1.14 °C, MAPE = 4.5%). Validation in four cities of the Iberian Peninsula confirmed the model’s reliability, with the worst RMSE value of 2.04 °C. These findings contribute to a better understanding of the factors driving UHIs and provide a scalable assessment framework.
Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification Lloyd A. Courtenay, Inés Barbero-García, Saray Martínez-Lastras, Susana Del Pozo, Miriam Corral, Diego González-Aguilera Photodiagnosis and Photodynamic Therapy, 2024 BACKGROUND: The early detection of Non-Melanoma Skin Cancer (NMSC) is essential to ensure patients receive the most effective treatment. Diagnostic screening tools for NMSC are crucial due to high confusion rates with other types of skin lesions, such as Actinic Keratosis. Nevertheless, current means of diagnosing and screening patients rely on either visual criteria, that are often conditioned by subjectivity and experience, or highly invasive, slow, and costly methods, such as histological diagnoses. From this, the objectives of the present study are to test if classification accuracies improve in the Near-Infrared region of the electromagnetic spectrum, as opposed to previous research in shorter wavelengths. METHODS: This study utilizes near-infrared hyperspectral imaging, within the range of 900.6 and 1454.8 nm. Images were captured for a total of 125 patients, including 66 patients with Basal Cell Carcinoma, 42 with cutaneous Squamous Cell Carcinoma, and 17 with Actinic Keratosis, to differentiate between healthy and unhealthy skin lesions. A combination of hybrid convolutional neural networks (for feature extraction) and support vector machine algorithms (as a final activation layer) was employed for analysis. In addition, we test whether transfer learning is feasible from networks trained on shorter wavelengths of the electromagnetic spectrum. RESULTS: The implemented method achieved a general accuracy of over 80 %, with some tasks reaching over 90 %. F1 scores were also found to generally be over the optimal threshold of 0.8. The best results were obtained when detecting Actinic Keratosis, however differentiation between the two types of malignant lesions was often noted to be more difficult. These results demonstrate the potential of near-infrared hyperspectral imaging combined with advanced machine learning techniques in distinguishing NMSC from other skin lesions. Transfer learning was unsuccessful in improving the training of these algorithms. CONCLUSIONS: We have shown that the Near-Infrared region of the electromagnetic spectrum is highly useful for the identification and study of non-melanoma type skin lesions. While the results are promising, further research is required to develop more robust algorithms that can minimize the impact of noise in these datasets before clinical application is feasible.
Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation Lloyd A. Courtenay, Inés Barbero-García, Saray Martínez-Lastras, Susana Del Pozo, Miriam Corral de la Calle, Alonso Garrido, Diego Guerrero-Sevilla, David Hernandez-Lopez, Diego González-Aguilera Plos One, 2024 One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66–1085.38 nm, 1109.06–1208.53 nm, 1236.95–1322.21 nm, and 1383.79–1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.
Accurate Detection of Road Markings from High-Resolution Photogrammetric Flights: Photogrammetry, Computer Vision, and AI Proceedings of the 31st International Workshop on Intelligent Computing in Engineering Eg ICE 2024, 2024
Accurate Rail and Railway Detection from High-Resolution Photogrammetric Flights: Photogrammetry, Computer Vision, and AI Proceedings of the 31st International Workshop on Intelligent Computing in Engineering Eg ICE 2024, 2024
Analysis of the Suitability of the EOLO Wind-Predictor Model for the Spanish Electricity Markets Saray Martínez-Lastras, Laura Frías-Paredes, Diego Prieto-Herráez, Martín Gastón-Romeo, Diego González-Aguilera Energies, 2023 Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted.
