Juan Manuel Jurado

Hyperspectral image analysis presents significant challenges associated with the high dimensionality of the data, the substantial computational cost of processing algorithms, and the difficulty of obtaining real-world datasets that are sufficiently diverse and well-controlled to evaluate new methodologies. Although the high correlation between spectral bands introduces redundancy in the recorded information, this property also offers an opportunity to reduce the dimensionality of the problem without necessarily eliminating the most relevant features of the spectral signatures. In this context, this work proposes a methodological framework aimed at studying dimensionality reduction techniques and unsupervised clustering in hyperspectral images.The main contribution of this work is the development of a pipeline for generating synthetic hyperspectral datasets, designed to evaluate algorithms under controlled experimental conditions and with varying levels of spectral complexity. The proposed framework enables the simulation of hyperspectral scenes through the controlled introduction of noise, spectral variability and spatial mixing, providing a reproducible environment for analysing the behaviour of different band-reduction strategies. Within this environment, a comparative evaluation is carried out of dimensionality reduction methods, including PCA, Wavelet Transforms (Haar, Daubechies and Symlet) and an approach based on Douglas-Peucker simplification for the explicit selection of spectral features.The experimental evaluation is carried out on high-dimensional synthetic datasets, comprising hypercubes of 1024×1024 pixels and 500 bands, which are subsequently reduced to compact subspaces whilst preserving relevant spectral information. The validation of the reduced representations is carried out using an unsupervised clustering algorithm based on the Spectral Angle Mapper (SAM), employing clustering metrics, correspondence with the reference classes and spectral preservation as measured by NRMSE. The results show that, in the configurations evaluated, Wavelet-based techniques provide the best balance between cluster separation capability, spectral preservation and computational cost. Meanwhile, Douglas-Peucker performs competitively as an interpretable spectral feature selection strategy, whilst PCA exhibits limitations in more complex scenarios.

The rapid expansion of utility-scale solar photovoltaic (PV) energy is increasingly constrained by land scarcity, environmental permitting complexity, social acceptance, and grid-connection limitations. Transmission line rights-of-way (TL-ROWs) represent pre-established energy corridors with direct grid access and reduced land acquisition requirements, yet they are rarely treated as primary spatial assets in PV site-selection studies. This study develops a corridor-centered GIS-based multi-criteria decision-making framework using the Analytic Hierarchy Process (AHP) to identify and prioritize TL-ROW segments for PV deployment in the State of São Paulo, Brazil. Seven criteria were integrated through a weighted linear combination, while environmental, infrastructural, and safety constraints were applied to define the deployable corridor surface. Corridor orientation was subsequently evaluated as a post-processing geometric filter to identify layout-compatible segments. Results indicate that 66.3\% of the deployable TL-ROW area falls within the High and Very High suitability classes. The estimated photovoltaic generation potential reaches approximately 32 GW$_\mathrm{p}$ across the deployable corridor network and 9.8 GW$_\mathrm{p}$ after applying the orientation filter. By shifting from conventional greenfield-oriented site selection toward corridor-based infrastructure prioritization, the proposed framework supports grid-aware and conflict-sensitive solar expansion while maintaining methodological transparency and replicability.

Soil surface moisture (SSM) is a critical indicator of agricultural drought, yet high-resolution projections under climate change remain scarce. This study develops a machine learning framework to predict and project SSM at 1 km resolution across five European Living Labs (LLs), encompassing vineyards, olive groves, and fruit tree systems. Historical Sentinel-1 SSM observations (2014–2024) were used to train ensemble models (Random Forest, XGBoost, ExtraTrees, LightGBM) incorporating climate variables, soil texture, topography, and land use. Tree-based models achieved R² values of 0.63–0.87. Vineyards showed the highest predictability (R² ≈ 0.87), reflecting their sensitivity to short-term atmospheric demand and surface water availability, whereas olive groves were the least predictable (R² ≈ 0.63–0.68), consistent with deeper rooting systems and greater drought buffering capacity. When forced with bias-corrected CMIP6 projections under SSP1-2.6 and SSP5-8.5 for 2041–2070, models indicate minimal changes under SSP1-2.6 but pronounced SSM declines of 8–24% under SSP5-8.5, with historically wetter regions experiencing the largest absolute losses. SHAP analysis confirmed precipitation and potential evapotranspiration as dominant predictors across all crops. This framework provides spatially explicit, crop-relevant SSM projections to support climate adaptation in European agricultural landscapes.

Satellite radar interferometry (InSAR) has become an invaluable tool for monitoring dams and large ponds, providing significant advantages when complemented with geotechnical and geodetic monitoring. InSAR uses radar signals from satellites to detect ground movements with millimeter precision by comparing phase differences between images taken at different times. This technique enables large-scale, continuous monitoring, which is critical for identifying potential structural problems and preventing catastrophic failures. Unlike traditional geotechnical and geodetic monitoring, which require extensive equipment and manual data collection, InSAR provides a non-intrusive, efficient solution that covers vast areas with high temporal frequency. The European Ground Motion Service (EGMS) exemplifies these advantages by providing standardized ground motion data across Europe, derived from Sentinel-1 satellite radar data. EGMS enables routine and comprehensive monitoring of ground stability and infrastructure integrity, assisting in the early detection of deformation patterns and supporting proactive maintenance and risk management. For dam managers, the integration of InSAR with traditional methods enhances the reliability of structural health assessments. Geotechnical sensors offer localized information on soil and material properties, while geodetic methods provide precise positional data; InSAR complements these by delivering comprehensive, continuous deformation maps. This synergy ensures robust monitoring and enhances the ability to predict and mitigate potential problems, significantly improving the effectiveness and efficiency of monitoring dams and large ponds, and contributing to safer and more resilient infrastructure management. This work presents several case studies from the SIAGUA project as examples, highlighting the practical applications and benefits of combining InSAR with traditional monitoring techniques.

Handheld LiDAR devices are revolutionizing 3D data capture, offering unprecedented accessibility and ease of use even for inexperienced users. These compact systems facilitate robust virtual reconstructions by extracting detailed geometric information from real-world environments. Advanced positioning systems in these devices generate highly accurate georeferenced 3D point clouds, enhancing the precision of scanned scenes. Leading manufacturers like FARO Technologies, Leica Geosystems, and Trimble offer diverse models, including the FARO Focus, Leica BLK360, and Trimble X7. These devices are invaluable in fields such as cultural heritage conservation, where they document and preserve historical sites; topographic mapping, providing accurate surveys in challenging terrains; and industrial inspection, ensuring thorough examinations of infrastructure and machinery. Additionally, the visualization of these 3D models on web platforms significantly enhances their accessibility and interaction. Web-based visualization tools allow users to explore, manipulate, and analyze 3D data from any location with an internet connection, democratizing access to detailed spatial information and enabling a broader range of users, from researchers to educators, to engage with complex datasets without the need for specialized software. Interactive features on these platforms, such as zooming, rotating, and measuring, provide an intuitive interface for detailed examination and collaboration. Consequently, the integration of handheld LiDAR technology with web visualization platforms opens new avenues for innovation and collaboration across various disciplines, further revolutionizing traditional methods and expanding the potential applications of 3D data capture.

Virtual reality (VR) has evolved substantially beyond its initial remit of gaming and entertainment, catalyzed by advancements such as improved screen resolutions and more accessible devices. Among various interaction techniques introduced to VR, eye-tracking stands out as a pivotal development. It not only augments immersion but offers a nuanced insight into user behavior and attention. This precision in capturing gaze direction has made eye-tracking instrumental for applications far beyond mere interaction, influencing areas like medical diagnostics, neuroscientific research, educational interventions, and architectural design, to name a few. Though eye-tracking’s integration into VR has been acknowledged in prior reviews, its true depth, spanning the intricacies of its deployment to its broader ramifications across diverse sectors, has been sparsely explored. This survey undertakes that endeavor, offering a comprehensive overview of eye-tracking’s state of the art within the VR landscape. We delve into its technological nuances, its pivotal role in modern VR applications, and its transformative impact on domains ranging from medicine and neuroscience to marketing and education. Through this exploration, we aim to present a cohesive understanding of the current capabilities, challenges, and future potential of eye-tracking in VR, underscoring its significance and the novelty of our contribution.

There are some archaeological sites with hard accessibility which remain unexplored and barely documented. The use of unmanned aerial systems (UAS) alleviates this challenge with aerial observations monitored with distant remote control. In addition to acquiring images in the visible wavelengths, other devices can be coupled on aerial platforms to inspect beyond the remaining structure of an archaeological site. For instance, thermography has proven to be of great help in the detection of buried remains due to observed temperature anomalies. This work explores the Castle of Puerta Arenas fortress to build the first aerial 3D reconstruction of this site by using RGB and thermographic images collected from a UAS. Orthomosaics have been applied to hypothesize about the original shape of the fortress, whereas 3D reconstructions have been rather applied to visualization and analysis. In this regard, the explored remains have been processed as dense point clouds in the visible and long-wave infrared spectrum, with the latter leading to the detection of hypothetical and still unknown towers. The detection of anomalies has been automatized by performing statistical analyses, globally and limited to smaller 3D voxel neighbourhoods. As a result, the studied remains have been documented and observed from an unexplored perspective, helping their conservation and dissemination, as well as suggesting future excavations.