Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.
Automated Resistivity Profiling (ARP) to Explore Wide Archaeological Areas: The Prehistoric Site of Mont'e Prama, Sardinia, Italy

This paper deals with the resistivity continuous surveys on extensive area carried out at the Mont’e Prama archaeological site, in Sardinia (Italy). From 2013 to 2015, new research was performed using both non-destructive surveys and traditional archaeological excavations. The measurements were done in order to find geophysical anomalies related to unseen buried archaeological remains and to define the spatial extension of the ancient necropolis. The electrical resistivity of soils was measured by means of the Automated Resistivity Profiling (ARP©) system. This multi-pole method provided high-resolution maps of electrical resistivity in the whole investigated area using a computer-assisted acquisition tool, towed by a small vehicle. Through this acquisition layout, a surface of 22,800 m2 was covered. The electrical resistivity data were derived in real time with centimetric horizontal precision through a differential GPS positioning system. Thanks to the simultaneous acquisition of ARP and GPS data, the rigorous georeferencing of the tridimensional experimental dataset was made possible, as well as the reconstruction of a detailed Digital Terrain Model. Here, the experimental results are analyzed and critically discussed by means of the integration of the results obtained by a high-resolution prospection performed with a multi-channel Ground Penetrating Radar system and taking into account other information derived from previous geological and archaeological studies. Geophysical results, jointly with topographic reconstruction, clearly permitted the identification of more interesting areas where future archaeological investigations could be focused.
Driving Factors of Land Change in China's Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications

Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement).
A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network

Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors. Recently proposed learning-based spatiotemporal fusion methods can address the prediction of both phenological and land-cover change. In this paper, we propose a novel deep learning-based spatiotemporal data fusion method that uses a two-stream convolutional neural network. The method combines both forward and backward prediction to generate a target fine image, where temporal change-based and a spatial information-based mapping are simultaneously formed, addressing the prediction of both phenological and land-cover changes with better generalization ability and robustness. Comparative experimental results for the test datasets with phenological and land-cover changes verified the effectiveness of our method. Compared to existing learning-based spatiotemporal fusion methods, our method is more effective in predicting phenological change and directly reconstructing the prediction with complete spatial details without the need for auxiliary modulation.
Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.
An Assessment of the GOCE High-Level Processing Facility (HPF) Released Global Geopotential Models with Regional Test Results in Turkey

The launch of dedicated satellite missions at the beginning of the 2000s led to significant improvement in the determination of Earth gravity field models. As a consequence of this progress, both the accuracies and the spatial resolutions of the global geopotential models increased. However, the spectral behaviors and the accuracies of the released models vary mainly depending on their computation strategies. These strategies are briefly explained in this article. Comprehensive quality assessment of the gravity field models by means of spectral and statistical analyses provides a comparison of the gravity field mapping accuracies of these models, as well as providing an understanding of their progress. The practical benefit of these assessments by means of choosing an optimal model with the highest accuracy and best resolution for a specific application is obvious for a broad range of geoscience applications, including geodesy and geophysics, that employ Earth gravity field parameters in their studies. From this perspective, this study aims to evaluate the GOCE High-Level Processing Facility geopotential models including recently published sixth releases using different validation methods recommended in the literature, and investigate their performances comparatively and in addition to some other models, such as GOCO05S, GOGRA04S and EGM2008. In addition to the validation statistics from various countries, the study specifically emphasizes the numerical test results in Turkey. It is concluded that the performance improves from the first generation RL01 models toward the final RL05 models, which were based on the entire mission data. This outcome was confirmed when the releases of different computation approaches were considered. The accuracies of the RL05 models were found to be similar to GOCO05S, GOGRA04S and even to RL06 versions but better than EGM2008, in their maximum expansion degrees. Regarding the results obtained from these tests using the GPS/leveling observations in Turkey, the contribution of the GOCE data to the models was significant, especially between the expansion degrees of 100 and 250. In the study, the tested geopotential models were also considered for detailed geoid modeling using the remove-compute-restore method. It was found that the best-fitting geopotential model with its optimal expansion degree (please see the definition of optimal degree in the article) improved the high-frequency regional geoid model accuracy by almost 15%.
Analysis of DNA and recombinant viral vaccines against P. falciparum in malaria-naïve and malaria-exposed humans

The hypotheses under test were as follows. Firstly that sequential immunisation of humans with two candidate vaccines recombinant for the same malarial DNA sequence would be safe, would produce higher frequencies of antigen-specific T cells in peripheral blood and greater efficacy than repeated immunisation with one vaccine. Secondly that recombinant viral malaria immunization would be more immunogenic in malaria-exposed than malaria-naive individuals. The third hypothesis was contingent on the conduct of a field efficacy trial; liver-stage specific T cells induced by the regimen with greatest immunogenicity would provide protection against natural infection. Three delivery systems were evaluated - a circular plasmid DNA molecule (DNA), modified vaccinia virus Ankara (MVA) and fowlpox strain 9 (FP9); each recombinant for the multiple epitope - thrombospondin related anonymous (or adhesion) protein (ME-TRAP) P. falciparum DNA sequence. DNA ME-TRAP and MVA ME-TRAP were safe but poorly immunogenic when given alone in both UK and Gambian adults. Two doses of 1mg DNA ME-TRAP administered intramuscularly followed by one dose of 3 x 107 plaque forming units (pfu) MVA ME-TRAP administered intradermally induced higher effector T cell frequencies as measured by ex vivo γ-interferon ELISPOT (enzyme-linked immunospot) assay than three doses of either alone. A second MVA ME-TRAP immunisation did not increase immunogenicity above that after a single immunisation. At these doses the DNA/MVA regimen was more immunogenic in malaria-experienced Gambian adults than in malaria-naive British adults. Increasing the dose of DNA to 2mg and MVA to 1.5 x 108 pfu increased immunogenicity further. Two doses of FP9 ME-TRAP showed a trend towards to being less immunogenic than two doses of DNA ME-TRAP prior to a single MVA immunisation. The heterologous DNA/MVA regimen afforded protection manifested by delay in time to parasitaemia in a clinical challenge model in the UK. Therefore a randomised double-blind controlled trial was conducted in men aged 15 45 in The Gambia. This trial confirmed safety and high immunogenicity but there was 10.3% (95%CI -22% to +34%) efficacy for the time to infection primary endpoint. Potential reasons for failure of the intervention include an unfavourable CD4+/CD8+ T cell ratio, inadequate TRAP expression in infected hepatocytes, inadequate cross-reactivity and the duration or magnitude of the peak T cell immunogenicity.
Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.
Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.
Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.
The Use of Lightweight Concrete Effectiveness in Volcanic Resistant Construction

The article is devoted to the topical problem - to increase seismic resistance of erected buildings in the Republic of Armenia. Armenia is rich in volcanic rocks of glassy structure, which possess sufficient excess free energy of particles of matter and this energy can be regarded as a work stock that they can accomplish, i.e. can be regarded as kinetic energy. One of the most active rocks in the republic is the lithoid pumice of the Jrauber-Lusavan deposit, a spongy material of an acidic composition, the amorphous phase of which reaches up to 98%. We studied this rock for obtaining lightweight structural concrete using energy-saving technology. The amorphous structure of pumice gives the possibility of expensive Portland cement to be replaced in part by a cheaper bonded binder - a fine-grained mixture of quicklime and aluminosilicate rock. Due to the non-stable state of volcanic pumice, which possesses chemical activity, i.e. property to interact with lime, the composition of lightweight concrete, which can be used for the manufacture of blocks, reinforced concrete structures. In studies to regulate and give concrete the necessary properties, a plasticizing additive of the firm Sika Visco Crete 510P and a mineral additive - microsilica were used. Lightweight concrete cement was obtained, where the compressive strength increased to 43.17 MPa in 28-day increments, and 4.1 MPa in the case of bending: and with partial replacement of Portland cement with lime, the strength naturally decreased, because lime is slowly gaining strength, but for a long time, and the strength to break has increased to 4.6 MPa, which is explained by the structure of the neoplasms.
The Use of Sucrose Granule as Pore Maker in Preparation of Porous Calcium Sulfate Dihydrate

Calcium sulfate dihydrate (CSD) has been clinically used as bone filler for decades. CSD bone graft is cheap, biocompatible and can be transformed to other osteoconductive ceramics such as hydroxyapatite and carbonate apatite. In addition, porous ceramic bone grafts is desired clinically. Development of porous ceramics bone graft with simple and cost-effective method is preferred. Thus, in this study, porous CSD was developed. Porous CSD can be used both as bone filler or precursor for porous hydroxyapatite and carbonate apatite. Porous CSD was prepared by mixing calcium sulfate hemihydrate (CSH) containing sucrose granules with distilled water. After setting, the sucrose granules were removed by immersion in distilled water. Porous CSD was obtained after sucrose leaching. It was observed that more pore formed in the specimen with 50% sucrose granule compared with that of 25% sucrose granule. The sucrose was completely removed from the porous CSD evident from ATR-FTIR analysis. The diametral tensile strength of the porous CSD tend to decrease with the increase of sucrose granule. Finally, sucrose granule was feasible to be used as pore maker in preparation of porous CSD.