The conservation of architectural heritage is a crucial task for preserving the history and cultural value of our cities. This work proposes a methodology that combines advanced techniques of multisensory data acquisition and analysis to generate labeled datasets of anomalous zones in historic buildings. The proposed method includes the use of artificial intelligence techniques, such as the Segment Anything Model (SAM), for image labeling, ensuring high precision through the involvement of conservation experts. Furthermore, due to the limited number of images focusing on specific anomalies, we address this issue by employing the Gaussian Splatting algorithm, which enables the reconstruction of 3D scenes and the generation of synthetic images centered on the anomalies of interest. These synthetic images are automatically labeled, resulting in a high-quality dataset suitable for training neural networks that support preventive conservation strategies in architectural heritage. By tackling the critical challenge of insufficient labeled data, this approach improves the robustness and reliability of machine learning models for anomaly detection in architectural structures.

Remote sensing technologies, such as LiDAR, produce billions of points that commonly exceed the storage capacity of the GPU, restricting their processing and rendering. Level of detail (LoD) techniques have been widely investigated, but building the LoD structures is also time-consuming. This study proposes a GPU-driven culling system focused on determining the number of points visible in every frame. It can manipulate point clouds of any arbitrary size while maintaining a low memory footprint in both the CPU and GPU. Instead of organizing point clouds into hierarchical data structures, these are split into groups of points sorted using the Hilbert encoding. This alternative alleviates the occurrence of anomalous groups found in Morton curves. Instead of keeping the entire point cloud in the GPU, points are transferred on demand to ensure real-time capability. Accordingly, our solution can manipulate huge point clouds even in commodity hardware with low memory capacities. Moreover, hole filling is implemented to cover the gaps derived from insufficient density and our LoD system. Our proposal was evaluated with point clouds of up to 18 billion points, achieving an average of 80 frames per second (FPS) without perceptible quality loss. Relaxing memory constraints further enhances visual quality while maintaining an interactive frame rate. We assessed our method on real-world data, comparing it against three state-of-the-art methods, demonstrating its ability to handle significantly larger point clouds.

The increasing importance of water supply systems, particularly in drought-prone regions, and the construction of linear structures, like reservoir dams, have led to the need for adapting these structures to changing conditions. Proper management of dam risks is crucial, as their failure can have severe economic and social consequences. Aging dams, such as those in Spain, require effective monitoring strategies to identify structural issues before they become critical threats. In-person inspections are conducted for maintenance, but modern dams integrate monitoring devices. Satellite radar interferometry (InSAR) technology, specifically the European Ground Motion Service (EGMS), enables precise and non-invasive monitoring of surface deformations using Sentinel-1 satellite data. EGMS provides millimeter-scale precision data, offering insights into subsidence, landslides, and stability issues impacting infrastructure. It is a valuable tool for civilian users and dam managers, providing monitoring data without requiring technical expertise. The SIAGUA project in Spain utilizes EGMS to monitor dams and surrounding areas, providing surveillance information to dam managers.

Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The wide availability of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. The most relevant contributions are summarized and classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, and (3) image-based modeling. In addition, we describe other approaches focused on the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.

This work describes an efficient approach for generating large 3D thermal point clouds considering the occlusion of camera viewpoints. For that purpose, RGB and thermal imagery are first corrected and fused with an intensity correlation-based algorithm. Then, absolute temperature values are obtained from the normalized data. Finally, thermal imagery is mapped on the point cloud using the Graphics Processing Unit (GPU) hardware. The proposed occlusion-aware mapping algorithm is massively parallelized using OpenGL's compute shaders. Our solution allows generating dense thermal point clouds in a lower response time compared with other notable soft-ware solutions (e.g., Agisoft Metashape or Pix4Dmapper) that yield results with a significantly lower point density.

Modelling of material appearance from reflectance measurements has become increasingly prevalent due to the development of novel methodologies in Computer Graphics. In the last few years, some advances have been made in measuring the light-material interactions, by employing goniometers/reflectometers under specific laboratory’s constraints. A wide range of applications benefit from data-driven appearance modelling techniques and material databases to create photorealistic scenarios and physically based simulations. However, important limitations arise from the current material scanning process, mostly related to the high diversity of existing materials in the real-world, the tedious process for material scanning and the spectral characterisation behaviour. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach for material appearance acquisition using hyperspectral data is proposed. A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. The observed hyperspectral signatures were used to recognise natural and artificial materials in the 3D point cloud according to spectral similarity. Then, a parametrisation of Bidirectional Reflectance Distribution Function (BRDF) was carried out by sampling the BRDF space for each material. Consequently, each material is characterised by multiple samples with different incoming and outgoing angles. Finally, an analysis of BRDF sample completeness is performed considering four sunlight positions and 16x16 resolution for each material. The results demonstrated the capability of the used technology and the effectiveness of our method to be used in applications such as spectral rendering and real-word material acquisition and classification.

Image mapping on 3D huge scenarios of the real world is one of the most fundamental and computational expensive processes for the integration of multi-source sensing data. Recent studies focused on the observation and characterization of Earth have been enhanced by the proliferation of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few methods have been developed to characterize the study area by fusing heterogeneous data such as thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts required for image mapping on dense and complex geometric models. In this paper, we propose an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed method is out-of-core and it uses available resources of the GPU’s machine to perform two main tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and two sets of multispectral images collected by two drone flights. We focus on launching the proposed method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The results demonstrate the method’s capabilities in terms of performance and versatility to be computed by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded and edge computing devices for 3D image mapping on large-scale scenarios in near real-time.

In this work, we present an efficient graphics processing unit (GPU)-based light detection and ranging (LiDAR) scanner simulator. Laser-based scanning is a useful tool for applications ranging from reverse engineering or quality control at an object scale to large-scale environmental monitoring or topographic mapping. Beyond that, other specific applications require a large amount of LiDAR data during development, such as autonomous driving. Unfortunately, it is not easy to get a sufficient amount of ground-truth data due to time constraints and available resources. However, LiDAR simulation can generate classified data at a reduced cost. We propose a parameterized LiDAR to emulate a wide range of sensor models from airborne to terrestrial scanning. OpenGL’s compute shaders are used to massively generate beams emitted by the virtual LiDAR sensors and solve their collision with the surrounding environment even with multiple returns. Our work is mainly intended for the rapid generation of datasets for neural networks, consisting of hundreds of millions of points. The conducted tests show that the proposed approach outperforms a sequential LiDAR scanning. Its capabilities for generating huge labeled datasets have also been shown to improve previous studies.

Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario.

Real-time visualization in web-based system remains challenging due to the amount of information associated to a 3D urban models. However, these 3D models are not able to provide advanced management of urban infrastructures, such as underground facilities. Nowadays, 3D GIS is considered the appropriate tool to provide accurate analysis and decision support based on spatial data. This paper presents a web-GIS application for 3D visualization, navigation, interaction and analysis of underground infrastructures through virtual reality. The growth of underground cities is a complex problem without easy solutions. In general, these infrastructures cannot be directly visualized. Thus, subsoil mapping can help us to develop a clearer representation of underground's pipes, cables or water mains. In addition, the approach of virtual reality provides an immersive experience and novelty interaction to acquire a complete knowledge about underground city structures. Experimental results show an integral application for the efficient management of underground infrastructure in real-time.