Using InSAR and GPR techniques to detect subsidence: application to the coastal area of “A Xunqueira” (NW Spain)

Climate change represents an important cause of subsidence, especially in coastal cities affected by changes in surface water level and water table. This paper presents a complementary study of Interferometric Synthetic Aperture Radar (InSAR) and Ground Penetrating Radar (GPR) for the early detection of subsidence and sinkhole phenomena. The methodology was applied to a coastal urban area in Galicia, northwest Spain (humid region), showing apparent signs of subsidence and building settlement during the last two years. Two different InSAR methods are compared for the period from June 2021 to March 2022: PSI (Persistent Scatterer Interferometry) and SBAS (Small Baseline Subsets), and the average deformation velocities obtained resulted in −3.0 mm/yr and −4.1 mm/yr, respectively. Additional GPR data were collected in January 2022 to validate the InSAR results, which detected subsidence in agreement with the persistent scatters obtained from the PSI method. This is crucial information to plan preventive maintenance.Xunta de Galicia | Ref. ED431F 2021/08European Commission | Ref. 101036926European Commission | Ref. 101021714Ministerio de Ciencia e Innovación | Ref. RYC2019–026604–IMinisterio de Universidades | Ref. CAS21/00241Ministerio de Ciencia e Innovación | Ref. TED2021-130183B-I0

Automatic Generation of Urban Road 3D Models for Pedestrian Studies From LiDAR Data

[Abstract] The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.This research study was funded by the Directorate-General for Traffic of Spain, grant number SPIP2017-0234

Review of InfraRed Thermography and Ground-Penetrating Radar applications for building assessment

The first appearance of concern for the good condition of a building dates back to ancient times. In recent years, with the emergence of new inspection technologies and the growing concern about climate change and people’s health, the concern about the integrity of building structures has been extended to their analysis as insulating envelopes. In addition, the growing network of historic buildings gives this sector special attention. Therefore, this study presents a comprehensive review of the application of two of the most common and most successful Non-Destructive Techniques (NDTs) when inspecting a building: InfraRed Thermography (IRT) and Ground-Penetrating Radar (GPR). To the best knowledge of the authors, it is the first time that a joint compilation of the state-of-the-art of both IRT and GPR for building evaluation is performed in the same work, with special emphasis on applications that integrate both technologies. The authors briefly explain the performance of each NDT, along with the individual and collective advantages of their uses in the building sector. Subsequently, an in-depth analysis of the most relevant references is described, according to the building materials to be studied and the purpose to be achieved: structural safety, energy efficiency and well-being, and heritage preservation. Then, three different case studies are presented with the aim of illustrating the potential of the combined use of IRT and GPR in the evaluation of buildings for the purposes defined. Last, the final remarks and future lines are described on the application of these two interesting inspection technologies in the preservation and conservation of the building sector.European Union Next GenerationEU/PRTRAgencia Estatal de Investigación | Ref. PDC2021-121239-C32Agencia Estatal de Investigación | Ref. RYC2019-026604-

Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential

[EN] The effective exploitation and management of renewable energies requires knowledge not only of the energy intensity at the exploitation site but also of the influence of the geometry of the site and its surroundings. For this reason, the efficient processing and interpretation of combined geospatial and energy data is a key issue. This paper presents the development of a web-based tool for the automatic computation of photovoltaic potential on rooftops and on parcels without buildings. The tool called Ener3DMap-SolarWeb Roofs is based on Leaflet and supports WMS, GeoJSON, GeoCSV and KML formats, among others. With these data formats, base maps, geometric data from the rooftops automatically computed from LiDAR and imagery data with self-developed processing algorithms, cadastral data and a solar radiation model are integrated in the tool. These different types of data, the high level of automation and the different scales for which energy data is calculated (hourly, monthly and annually) are the main contributions of the presented tool compared to other existing solutions. The capacities of the tool are tested through its application to analyze the solar potential of rooftops with different shapes and for different solar panel configurations. The accuracy of the results is ensured through the integration of a validated methodology for the computation of geometry and a validated solar radiation model, PVGIS

Techniques to correct and prevent acid mine drainage: a review

Acid mine drainage (AMD) from mining wastes is one of the current environmental problems in the field of mining pollution that requires most action measures. This term describes the drainage generated by natural oxidation of sulfide minerals when they are exposed to the combined action of water and atmospheric oxygen. AMD is characterized by acidic effluents with a high content of sulfate and heavy metal ions in solution, which can contaminate both groundwater and surface water. Minerals responsible for AMD generation are iron sulfides (pyrite, FeS2, and to a lesser extent pyrrhotite, Fe1-XS), which are stable and insoluble while not in contact with water and atmospheric oxygen. However, as a result of mining activities, both sulfides are exposed to oxidizing ambient conditions. In order to prevent AMD formation, a great number of extensive research studies have been devoted to the mechanisms of oxidation and its prevention. In this work, we present an explanation and theoretical valuation of the pyrite oxidation process and a compendium on the measures most frequently employed for its prevention and correction

Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis

Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal wavelengths for these purposes are yet to be conclusively determined. A visible-near infrared hyperspectral camera with an ad-hoc built platform was used for image acquisition in the present study. Robust statistical techniques were used to conclude an optimal range between 573.45 and 779.88 nm to distinguish between healthy and non-healthy skin. Wavelengths between 429.16 and 520.17 nm were additionally found to be optimal for the differentiation between cancer types.Gerencia Regional de Salud de Castilla y León (GRS 2139/A/20); Spanish Ministry of Science, Innovation and Universities (PRE2019-089411); Instituto de Salud Carlos III (PI18/00587); Ibderdrola Spain; Junta de Castilla y León (GRS 1837/A/18). This project was funded by the Junta de Castilla y Leon, under the title project HYPERSKINCARE (Ref. GRS 1837/A/18). Lloyd Austin Courtenay is funded by the Spanish Ministry of Science, Innovation and Universities with an FPI Predoctoral Grant (Ref. PRE2019-089411) associated to project RTI2018-099850-B-I00 and the University of Salamanca. Susana Lagüela and Susana del Pozo are both funded by the Iberdrola Spain through the initiative Cátedra Iberdrola VIII Centenario of the University of Salamanca. Javier Cañueto is partially supported by the PI18/00587(Instituto de Salud Carlos III cofinanciado con fondos FEDER) and GRS 2139/A/20 (Gerencia Regional de Salud de Castilla y León

Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis

[EN] Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal wavelengths for these purposes are yet to be conclusively determined. A visible-near infrared hyperspectral camera with an ad-hoc built platform was used for image acquisition in the present study. Robust statistical techniques were used to conclude an optimal range between 573.45 and 779.88 nm to distinguish between healthy and non-healthy skin. Wavelengths between 429.16 and 520.17 nm were additionally found to be optimal for the differentiation between cancer typesSIGerencia Regional de Salud de Castilla y LeónSpanish Ministry of Science, Innovation and UniversitiesInstituto de Salud Carlos IIIJunta de Castilla y Leó

Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures

[EN] Non-melanoma skin cancer, and basal cell carcinoma in particular, is one of the most common types of cancer. Although this type of malignancy has lower metastatic rates than other types of skin cancer, its locally destructive nature and the advantages of its timely treatment make early detection vital. The combination of multispectral imaging and artificial intelligence has arisen as a powerful tool for the detection and classification of skin cancer in a non-invasive manner. The present study uses hyperspectral images to discern between healthy and basal cell carcinoma hyperspectral signatures. Upon the combined use of convolutional neural networks, with a final support vector machine activation layer, the present study reaches up to 90% accuracy, with an area under the receiver operating characteristic curve being calculated at 0.9 as well. While the results are promising, future research should build upon a dataset with a larger number of patients.SIJunta de Castilla y Leo