Ayala Lauroba, Christian
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Ayala Lauroba
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Christian
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Estadística, Informática y Matemáticas
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Publication Open Access Diffusion models for remote sensing imagery semantic segmentation(IEEE, 2023-10-20) Ayala Lauroba, Christian; Sesma Redín, Rubén; Aranda, Carlos; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA25-2022Denoising Diffusion Probabilistic Models have exhibited impressive performance for generative modelling of images. This paper aims to explore the potential of diffusion models for semantic segmentation tasks in the context of remote sensing. The major challenge of employing these models for semantic segmentation tasks is the generative nature of the model, which produces an arbitrary segmentation mask from a random noise input. Therefore, the diffusion process needs to be constrained to produce a segmentation mask that matches the target image. To address this issue, the denoising process is conditioned by utilizing the input image as a reference. In the experimental study, the proposed model is compared against other state-of-the-art semantic segmentation architectures using the Massachusetts Buildings Aerial dataset. The results of this study provide valuable insights into the potential of diffusion models for semantic segmentation tasks in the field of remote sensing.Publication Open Access A Deep Learning approach to land use classification in high resolution satellite imagery(2020) Ayala Lauroba, Christian; Galar Idoate, Mikel; Escuela Técnica Superior de Ingeniería Industrial, Informática y de Telecomunicación; Industria, Informatika eta Telekomunikazio Ingeniaritzako Goi Mailako Eskola TeknikoaA lo largo de los últimos años ha aumentado el interés y la necesidad de disponer de información de usos y coberturas del territorio fiable y actualizada, siendo numerosos los proyectos de carácter local, nacional e internacional cuyo objetivo es la creación y actualización de bases de datos de usos y ocupación del suelo. En los últimos años se han producido importantes avances tecnológicos en el sector de la teledetección y el tratamiento de imágenes de satélite. En Europa, se ha impulsado la investigación en el ámbito de la observación de la Tierra gracias al programa Copernicus gestionado por la Agencia Espacial Europea (ESA). Este proyecto se encuentra focalizado en la puesta a punto de una metodología para el seguimiento del grado de consolidación en las áreas de suelo en desarrollo urbano de las ciudades. Para tales fines se ha optado por segmentar semánticamente imágenes satelitales del programa Copernicus mediante la aplicación de innovadoras técnicas de Deep Learning. Los resultados obtenidos han sido comparados a los obtenidos mediante un proceso semiautomático, realizado por profesionales de teledetección.Publication Open Access Guidelines to compare semantic segmentation maps at different resolutions(IEEE, 2024) Ayala Lauroba, Christian; Aranda, Carlos; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaChoosing the proper ground sampling distance (GSD) is a vital decision in remote sensing, which can determine the success or failure of a project. Higher resolutions may be more suitable for accurately detecting objects, but they also come with higher costs and require more computing power. Semantic segmentation is a common task in remote sensing where GSD plays a crucial role. In semantic segmentation, each pixel of an image is classified into a predefined set of classes, resulting in a semantic segmentation map. However, comparing the results of semantic segmentation at different GSDs is not straightforward. Unlike scene classification and object detection tasks, which are evaluated at scene and object level, respectively, semantic segmentation is typically evaluated at pixel level. This makes it difficult to match elements across different GSDs, resulting in a range of methods for computing metrics, some of which may not be adequate. For this reason, the purpose of this work is to set out a clear set of guidelines for fairly comparing semantic segmentation results obtained at various spatial resolutions. Additionally, we propose to complement the commonly used scene-based pixel-wise metrics with region-based pixel-wise metrics, allowing for a more detailed analysis of the model performance. The set of guidelines together with the proposed region-based metrics are illustrated with building and swimming pool detection problems. The experimental study demonstrates that by following the proposed guidelines and the proposed region-based pixel-wise metrics, it is possible to fairly compare segmentation maps at different spatial resolutions and gain a better understanding of the model's performance. To promote the usage of these guidelines and ease the computation of the new region-based metrics, we create the seg-eval Python library and make it publicly available at https://github.com/itracasa/ seg-eval.