José Rodríguez-Ortega

José Rodríguez-Ortega, Rohaifa Khaldi, Domingo Alcaraz-Segura et al.

Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a Long-Short Term Memory (LSTM)-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input-output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS multispectral time series at 460m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC abundances along with ancillary information. The dataset (https://zenodo.org/records/7752348) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public.

José Rodríguez-Ortega, Francisco Pérez-Hernández, Siham Tabik

Identifying anatomical landmarks in 3D dental models is essential for orthodontic treatment, yet manual placement is labor-intensive and requires expert knowledge. While machine learning methods have been proposed for automatic landmark detection in 3D Intraoral Scans (IOS), none provide a fully end-to-end solution that avoids costly tooth segmentation. We present CHaRM (Conditioned Heatmap Regression Methodology), the first fully end-to-end deep learning approach for tooth landmark detection in 3D IOS. CHaRM integrates four components: a point cloud encoder, a decoder with a heatmap regression head, a teeth-presence classification head, and the novel CHaR module. The CHaR module leverages teeth-presence information to adapt to missing teeth, improving detection accuracy in complex dental cases. Unlike two-stage workflows that segment teeth before landmarking, CHaRM operates directly on IOS point clouds, reducing complexity, avoiding error propagation, and lowering computational cost. We evaluated CHaRM with five point cloud learning backbones on IOSLandmarks-1k, a new dataset of 1,214 annotated 3D dental models. Both the dataset and code will be publicly released to address the scarcity of open data in orthodontics and foster reproducible research. CHaRM with PointMLP, named CHaRNet, achieved the best accuracy and efficiency. Compared to state-of-the-art methods (TSMDL and ALIIOS), CHaRNet reduced mean Euclidean distance error to 0.56 mm on standard dental models and 1.12 mm across all dentition type, while delivering up to 14.8x faster inference on GPU. This end-to-end approach streamlines orthodontic workflows, enhances the precision of 3D IOS analysis, and enables efficient computer-assisted treatment planning.

Francisco Pérez-Hernández, José Rodríguez-Ortega, Yassir Benhammou et al.

The detection of critical infrastructures in large territories represented by aerial and satellite images is of high importance in several fields such as in security, anomaly detection, land use planning and land use change detection. However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i.e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large. Besides, airports can have a surface area either small or too large with completely different shapes, which makes its correct detection challenging. As far as we know, these limitations have not been tackled yet in previous works. This paper presents (1) a smart Critical Infrastructure dataset, named CI-dataset, organised into two scales, small and large scales critical infrastructures and (2) a two-level resolution-independent critical infrastructure detection (DetDSCI) methodology that first determines the spatial resolution of the input image using a classification model, then analyses the image using the appropriate detector for that spatial resolution. The present study targets two representative classes, airports and electrical substations. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to Faster R-CNN, one of the most influential detection models.