Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity

Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities.
Understanding the Public Sentiment and Discourse on COVID-19 Vaccine

The novel Coronavirus disease has negatively impacted human lives in various aspects leading to the global health crisis and economic fallout. The availability of vaccines in the U.S. is a relief. However, many people are not in favor of the COVID-19 vaccine. The study aimed to identify the emotions and themes of discourse related to the COVID-19 vaccine posted on Twitter. Sentiment analysis and topic modeling techniques were employed to discover the sentiment and prevalent themes. The findings suggest that overall, the public has positive opinions towards the vaccine. Those people that show positive opinions appreciate the efforts of leadership, medical experts and pharmaceutical companies in developing the COVID-19 vaccine. They are feeling hopeful and relieved as the vaccines are/will be available. There are also people that hold negative attitudes towards the vaccines because of disbelief in the government and concerns about the efficacy and adverse reactions caused by the vaccines. © AMCIS 2021.
A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation

Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose a novel image mosaic method based on homography matrix compensation to solve the mentioned problem. A set of spaceborne ScanSAR images from the Gaofen-3 (GF-3) satellite were selected to test the performance of the new method. First, images are preprocessed by an improved Wallis filter to eliminate intensity inconsistencies. Then, to reduce the enormous computational redundancy of registration, the overlapping areas of adjacent images are coarsely extracted using geolocation technologies. Furthermore, to improve the efficiency of stitching and maintain the original information and resolution of images, we deduce a compensation of homography matrix to implement downsampled images registration and original-size images projection. After stitching, the transitions at the edges of the images were smooth and seamless, the information and resolution of the original images were preserved successfully, and the efficiency of the mosaic was improved by approximately one thousand-fold. The validity, high efficiency and reliability of the method are verified.
A Quasiconvex Formulation for Radial Cameras

In this paper we study structure from motion problems for 1D radial cameras. Under this model the projection of a 3D point is a line in the image plane going through the principal point, which makes the model invariant to radial distortion and changes in focal length. It can therefore effectively be applied to uncalibrated image collections without the need for explicit estimation of camera intrinsics.We show that the reprojection errors of 1D radial cameras are examples of quasiconvex functions. This opens up the possibility to solve a general class of relevant reconstruction problems globally optimally using tools from convex optimization. In fact, our resulting algorithm is based on solving a series of LP problems. We perform an extensive experimental evaluation, on both synthetic and real data, showing that a whole class of multiview geometry problems across a range of different cameras models with varying and unknown intrinsic calibration can be reliably and accurately solved within the same framework.1
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.
Formal Approach Based on Petri Nets for Modeling and Verification of Video Games

Video games are complex systems that combine technical and artistic processes. The specification of this type of system is not a trivial task, making it necessary to use diagrams and charts to visually specify sets of requirements. Therefore, the underlying proposal of this work is to present an approach based on the formalism of Petri nets for aiding in the design process of video games. The activities of the game are represented by a specific type of Petri net called Work-Flow net. The definition of a topological map can be represented by state graphs. Using Colored Petri nets, it is possible to define formal communication mechanisms between the model of activity and the model of the map. The simulation of the timed models allows then to produce an estimated time that corresponds to the effective duration a player will need to complete a level of a game. Furthermore, a kind of Soundness property related to gameplay in a game Quest can be verified through state space analysis. For a better understanding of the approach, the video game Silent Hill II is used.
Four Unrecorded Aspergillus Species from the Rhizosphere Soil in South Korea

Abstract The genus Aspergillus is commonly isolated from various marine and terrestrial environments; however, only a few species have been studied in rhizosphere soil. As part of the Korean indigenous fungal excavation project, we investigated fungal diversity from rhizosphere soil, focusing on Aspergillus species. A total of 13 strains were isolated from the rhizosphere soil of three different plants. Based on phylogenetic analysis of β-tubulin and calmodulin and morphological characteristics, we identified five Aspergillus species. A. calidoustus and A. pseudodeflectus were commonly isolated from the rhizosphere soil. Four species were confirmed as unrecorded species in Korea: A. calidoustus, A. dimorphicus, A. germanicus, and A. pseudodeflecuts. The detailed morphological descriptions of these unrecorded species are provided.
Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.
Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method

Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted attention in China and globally. In addition, due to the rolling terrain of the reservoir area, synthetic aperture radar (SAR) imaging will affect the interactive synthetic aperture radar (InSAR) deformation results. Only by obtaining effective deformation information can active landslides be accurately identified. Therefore, the banks of the Hulukou Xiangbiling section of the Baihetan reservoir area before impoundment in the Jinsha River Basin were studied in this paper. Using terrain data and the satellite parameters from Sentinel-1A ascending and descending orbits and ALOS PALSAR ascending orbit, the line-of-sight visibility was quantitatively analyzed, and an analysis method was proposed. Based on the SAR data visibility analysis, the small baseline subset (SBAS) technique was used to process the SAR data to acquire effective deformation. InSAR deformation data was combined with Google Earth imagery to identify 25 active landslides. After field verification, 21 active landslides (14 new) were determined. Most of the active landslides are controlled by faults, and the strata of the other landslides are relatively weak. This InSAR analysis method based on SAR data visibility can provide a reference for identifying and analyzing active landslides in other complicated terrain.
Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites

Earth rotation parameters (ERP) are one of the key parameters in realization of the International Terrestrial Reference Frames (ITRF). At present, the International Laser Ranging Service (ILRS) generates the satellite laser ranging (SLR)-based ERP products only using SLR observations to Laser Geodynamics Satellite (LAGEOS) and Etalon satellites. Apart from these geodetic satellites, many low Earth orbit (LEO) satellites of Earth observation missions are also equipped with laser retroreflector arrays, and produce a large number of SLR observations, which are only used for orbit validation. In this study, we focus on the contribution of multiple LEO satellites to ERP estimation. The SLR and Global Positioning System (GPS) observations of the current seven LEO satellites (Swarm-A/B/C, Gravity Recovery and Climate Experiment (GRACE)-C/D, and Sentinel-3A/B) are used. Several schemes are designed to investigate the impact of LEO orbit improvement, the ERP quality of the single-LEO solutions, and the contribution of multiple LEO combinations. We find that ERP estimation using an ambiguity-fixed orbit can attain a better result than that using ambiguity-float orbit. The introduction of an ambiguity-fixed orbit contributes to an accuracy improvement of 0.5%, 1.1% and 15% for X pole, Y pole and station coordinates, respectively. In the multiple LEO satellite solutions, the quality of ERP and station coordinates can be improved gradually with the increase in the involved LEO satellites. The accuracy of X pole, Y pole and length-of-day (LOD) is improved by 57.5%, 57.6% and 43.8%, respectively, when the LEO number increases from three to seven. Moreover, the combination of multiple LEO satellites is able to weaken the orbit-related signal existing in the single-LEO solution. We also investigate the combination of LEO satellites and LAGEOS satellites in the ERP estimation. Compared to the LAGEOS solution, the combination leads to an accuracy improvement of 0.6445 ms, 0.6288 ms and 0.0276 ms for X pole, Y pole and LOD, respectively. In addition, we explore the feasibility of a one-step method, in which ERP and the orbit parameters are jointly determined, based on SLR and GPS observations, and present a detailed comparison between the one-step solution and two-step solution.
Stability Analysis for the Discrete-Time T-S Fuzzy System with Stochastic Disturbance and State Delay

In this paper, we study the stability for discrete-time Takagi and Sugeno (T-S) fuzzy systems with perturbation disturbance and time delay. Stochastic delay-dependent stability criteria are derived for stochastic T-S fuzzy systems with time-invariant and time-varying delays, respectively. For the time-varying delay case, a novel fuzzy Lyapunov–Krasovskii functional (LKF) without requiring all the involved symmetric matrices to be positive definite is constructed to reduce the conservatism. These stability conditions are then represented in terms of finite linear matrix inequalities (LMIs), which can be solved efficiently by using standard LMIS optimisation techniques. Two numerical examples are given to illustrate the feasibility of the proposed method.
A Satellite-USV System for Persistent Observation of Mesoscale Oceanographic Phenomena

Traditional tools and methodologies for mesoscale observation of oceanographic phenomena are limited by under-sampling and data latency. In this article we evaluate three different scenario variants of an architecture for how heterogeneous sensor nodes can be integrated with satellite remote sensing. Independent space and marine sensing platforms are interconnected either directly or by means of a ground-based mission control center responsible for data processing, relay, and coordination of the assets. A wave-propelled unmanned surface vehicle (USV) persistently collects in situ data of the targeted phenomenon. In two variants of the architecture, a dedicated small satellite acts as a sensor node, a data processing facility and a communication node. We have used a System-of-Systems (SoS) modeling approach coupled with operational simulations in different locations on Earth, in order to support the proposed methodology and investigate quantitatively the reduction the data latency to end-users. Through a combination of field experiments and simulations we estimate how the different scenarios perform with respect to providing remote sensing data that are used to create a measurement and navigation plan for the autonomous vessel.
Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.
A New Marine Species of Miracula (Oomycota) Parasitic to Minidiscus sp. in Iceland†

Abstract Obligate endoparasitic oomycetes are known to ubiquitously occur in marine and freshwater diatoms, but their diversity is still largely unexplored. Many of these parasitoids are members of the early-diverging oomycete lineages (Miracula, Diatomophthora), others are within the Leptomitales of the Saprolegniomycetes (Ectrogella, Lagenisma) and some have been described in the Peronosporomycetes (Aphanomycopsis, Lagenidium). Even though some species have been recently described and two new genera were introduced (Miracula and Diatomophthora), the phylogeny and taxonomy of most of these organisms remain unresolved. This is contrasted by the high number of sequences from unclassified species, as recently revealed from environmental sequencing, suggesting the presence of several undiscovered species. In this study, a new species of Miracula is reported from a marine centric diatom (Minidiscus sp.) isolated from Skagaströnd harbor in Northwest Iceland. The morphology and life cycle traits of this novel oomycete parasite are described herein, and its taxonomic placement within the genus Miracula is confirmed by molecular phylogeny. As it cannot be assigned to any previously described species, it is introduced as Miracula islandica in this study. The genus Miracula thus contains three described holocarpic species (M. helgolandica, M. islandica, M. moenusica) to which likely additional species will need to be added in the future, considering the presence of several lineages known only from environmental sequencing that clustered within the Miracula clade.
Automatic Generation of Seamless Mosaics Using Invariant Features

The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.