Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network

Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of “ground truth” in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA’s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.
Compact Cloud Detection with Bidirectional Self-Attention Knowledge Distillation

The deep convolutional neural network has made significant progress in cloud detection. However, the compromise between having a compact model and high accuracy has always been a challenging task in cloud detection for large-scale remote sensing imagery. A promising method to tackle this problem is knowledge distillation, which usually lets the compact model mimic the cumbersome model’s output to get better generalization. However, vanilla knowledge distillation methods cannot properly distill the characteristics of clouds in remote sensing images. In this paper, we propose a novel self-attention knowledge distillation approach for compact and accurate cloud detection, named Bidirectional Self-Attention Distillation (Bi-SAD). Bi-SAD lets a model learn from itself without adding additional parameters or supervision. With bidirectional layer-wise features learning, the model can get a better representation of the cloud’s textural information and semantic information, so that the cloud’s boundaries become more detailed and the predictions become more reliable. Experiments on a dataset acquired by GaoFen-1 satellite show that our Bi-SAD has a great balance between compactness and accuracy, and outperforms vanilla distillation methods. Compared with state-of-the-art cloud detection models, the parameter size and FLOPs are reduced by 100 times and 400 times, respectively, with a small drop in accuracy.
Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.
New Insights into Long-Term Aseismic Deformation and Regional Strain Rates from GNSS Data Inversion: The Case of the Pollino and Castrovillari Faults

We present a novel inverse method for discriminating regional deformation and long-term fault creep by inversion of GNSS velocities observed at the spatial scale of intraplate faults by exploiting the different spatial signatures of these two mechanisms. In doing so our method provides a refined estimate of the upper bound of the strain accumulation process. As case study, we apply this method to a six year GNSS campaign (2003–2008) set up in the southern portion of the Pollino Range over the Castrovillari and Pollino faults. We show that regional deformation alone cannot explain the observed deformation pattern and implies high geodetic strain rate, with a WSW-ENE extension of 86±41×10−9/yr. Allowing for the possibility of fault creep, the modelling of GNSS velocities is consistent with their uncertainties and they are mainly explained by a shallow creep over the Pollino fault, with a normal/strike-slip mechanism up to 5 mm/yr. The regional strain rate decrease by about 70 percent and is characterized by WNW-ESE extension of 24±28×10−9/yr. The large uncertainties affecting our estimate of regional strain rate do not allow infering whether the tectonic regime of the area is extensional or strike-slip, although the latter is slightly more likely.
A NEW SPECIES OF BELBA AND NEW RECORDS OF BELBA AND POROBELBA FROM CHINA (ACARI: ORIBATIDA: DAMAEIDAE)

Three genera of the oribatid mite family Damaeidae (Sensu lato) have been reported from China. This paper deals with two other genera--Belba and Porobelba. A new species of Belba and six new records of Belba and porobelba are reported in the paper. It also gives additional description and leg illustrations of partial known species.
Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series

The detection of irrigated areas by means of remote sensing is essential to improve agricultural water resource management. Currently, data from the Sentinel constellation offer new possibilities for mapping irrigated areas at the plot scale. Until now, few studies have used Sentinel-1 (S1) and Sentinel-2 (S2) data to provide approaches for mapping irrigated plots in temperate areas. This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions. The combined use of the whole cumulative data (radar, optical and weather) improves the irrigated crop classifications (Overall Accuary (OA) ≈ 0.7) compared to the classifications obtained using each data separately (OA < 0.5). The use of monthly cumulative rainfall allows a significant improvement of the Fscore of irrigated and rainfed classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.
Models and Solutions of Strategic Resource Allocation Problems: Approximate Equilibrium and Online Learning in Blotto Games. (Modèles et Solutions de Problèmes d'Allocation de Ressources Stratégiques : Équilibre Approximatif et Apprentissage en Ligne dans les Jeux de Blotto)

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Models and Solutions of Strategic Resource Allocation Problems: Approximate Equilibrium and Online Learning in Blotto Games Dong Quan Vu
Effects of sterilization on growth of cucumber plants and soil microflora in a continuous mono-cropping soil

Pot experiments were carried out to investigate effect of sterilization and application of amino acid fertilizer on growth of cucumber plants and soil microflora in a continuous mono-cropping soil. The soil which had been planted with continuous mono-cropping cucumber for 4 seasonal crops and had a severe continuous obstacle, was used in the experiment. The experiments had 6 treatments with 8 replicates for each treatment, including CK (Non-sterilized soil with no application of amino acid fertilizer), 0.5% AAF (Non-sterilized soil with the addition of 0.5% amino acid fertilizer based on soil dry weight), 1.0% AAF (Non-sterilized soil with the addition of 1.0% amino acid fertilizer based on soil dry weight),S-CK (Sterilized soil with no application of amino acid fertilizer), S-0.5% AAF (Sterilized soil with the addition of 0.5% amino acid fertilizer based on soil dry weight) and S-1.0% AAF (Sterilized soil with the addition of 1.0% amino acid fertilizer based on soil dry weight). Uniform cucumber seedlings with two cotyledons were transplanted to pots (0.5 L, 400 g soil) at March 7, 2006, and harvested at May 10, 2006. The results obtained are listed as follows. Soil sterilization could improve growth of cucumber plants to greater extent, and, for example, SPAD value, plant height, leaf area and fresh weight of cucumber plants grown in sterilized soil were significantly higher than those in non-sterilized soil. The differences of cucumber growth between the sterilized soil and non-sterilized soil decreased with increase of AAF application, showing that the application of AAF could solve some problems coming from continuous mono-cropping system. SOD and POD activities in the leaves of cucumber plants grown in sterilized soil were higher than CK, but their activities became lower with increase of AAF application. CAT activities and soluble protein contents of all treatments in sterilized soil were significantly higher than those in non-sterilized soil while MDAs were significantly lower. The number of bacteria and fungi in sterilized soil was increased by 1.89—3.03 folds and 6.96—2.43 folds, respectively, compared with those in non-sterilized soil and the ratio of fungi to bacteria was significantly increased. In addition, Actinomycetes were slightly enhanced. The analyses of PCR-DGGE profiles showed that the number of bands on the lanes of DGGE profiles in sterilized soil was more than those in non-sterilized soil, with low similarities of DGGE profiles, and the diversities of soil microorganism in sterilized soil were increased compared to those in non-sterilized soil. The incidence of Fusarium wilt of cucumber plants grown in non-sterilized soil was up to 31.2%—68.8%, but no incidence in sterilized soil.
Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks

Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.
Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR

The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.
Evaluating the Performance of a Convection-Permitting Model by Using Dual-Polarimetric Radar Parameters: Case Study of SoWMEX IOP8

In this study, a dual-polarimetric radar observation operator is established and modified for the Taiwan area for the purpose of model verification. A severe squall line case during the Southwest Monsoon Experiment Intensive Observing Period 8 (SoWMEX IOP#8) on 14 June 2008, is selected and examined. Because the operator is adopted from the use of the midlatitude region, sensitivity tests are performed to obtain the optimal setting of the operator in the subtropical region. To accurately capture the dynamic structure of the squall lines, the ensemble-based data assimilation system, which assimilates both radial wind and reflectivity data, is used to obtain the optimal analysis field on the mesoscale for evaluating the performance of model simulation. The characteristics of two microphysics schemes are investigated, and the results obtained using the schemes are compared with the S-band dual-polarimetric radar observations. The horizontal and vertical cross-sections show that the analyses resemble the observations. Both schemes can replicate the polarimetric parameter signature such as ZDR and KDP columns. When comparing model simulation with polarimetric parameters through the drawing of contour frequency by altitude diagrams (CFADs), the results reveal that the single moment microphysics scheme performs better than the double moment scheme in this case. However, the reflectivity field in the stratiform area is more accurately captured when using the double moment scheme. Furthermore, validation with polarimetric variables (ZH, ZDR and KDP) histograms shows underestimation of the KDP field in both schemes. Overall, this study indicates the benefit of assimilating radial wind and reflectivity data for the analyses of severe precipitation systems and the necessity of assimilating polarimetric parameters for the accuracy of microphysical processes, especially complex microphysics schemes in subtropical region.
Multilevel assessment of grooming behavior against Varroa destructor in Italian and Africanized honey bees

The ectoparasitic mite Varroa destructor is one of the main plagues of honey bees Apis mellifera. Grooming behavior is a resistance mechanism through which parasitized bees can dislodge mites by themselves (autogrooming) or by the action of other bees (allogrooming). The objective of this study was to evaluate grooming behavior in Italian (A. m. ligustica) and Africanized (hybrids of A. m. scutellata) bees at the individual, group, and colony levels. Firstly, five behaviors were recorded observing bees individually placed on a Petri dish and after placing a mite on their thorax. Secondly, 30 bees of each colony were placed in a Petri dish along with 20 mites and 24 h later fallen mites were counted. Lastly, the proportion of injured mites collected in the hive floor was determined. At the individual level, Africanized bees showed a higher total number of reaction behaviors to V. destructor than did Italian bees (U = 182.5; p = 0.02). Groups of Italian bees could dislodge 60.8 ± 20.0% of mites and Africanized bees dislodged 65.9 ± 15.6% of mites, without showing significant differences (t = 0.735; p = 0.47). Colonies of Africanized bees showed a higher proportion of injured mites (29.0 ± 8.6%) than colonies of Italian bees did (17.7 ± 9.8%) (t = 2.92; p = 0.009). Africanized bees are characterized by presenting higher resistance to V. destructor than European bees. This study shows that such difference can be, partly due to grooming behavior. The importance of auto and allogrooming regarding resistance to V. destructor is discussed.
Taxonomic Study of the Family Ectobiidae (Order Blattodea) and Its Phylogenetic Relationships with Other Egyptian Blattodea

Since 1758 cockroaches have been collected and described from Egypt by several authors. The relationships within the superorder Dictyoptera have been debated in the literature. The Egyptian cockroaches of the family Ectobiidae (Blattodea) are reviewed. Five species in five genera are recognised, including Blattella germanica, Chorisia reducta, Phyllodromica nilotica, Supella longipalpa and Matabeli natreitlina. Gene sequence of 28s ribosomal RNA is provided for Chorisia reducta (Walker) for the first time. The PCR product was found to be about 512 bp long, after sequencing this result aligned with the GenBank data then constructing the phylogenetic tree by using Mega 0.6 program. Colour images, specimens examined and distributions are given for each species.
Rapid and Accumulated Modulation of Action-Effects on Action

Abstract Auditory feedback to a keypress is used in many devices to facilitate the motor output. The timing of auditory feedback is known to have an impact on the motor output, yet it is not known if a keypress action can be modulated on-line by an auditory feedback or how quick an auditory feedback can influence an ongoing keypress. Furthermore, it is not clear if the prediction of auditory feedback already changes the early phase of a keypress action independent of sensory feedback, which would suggest that such prediction changes the motor plan. In the current study, participants pressed a touch-sensitive device with auditory feedback in a self-paced manner. The auditory feedback was given either after a short (60 msec) or long (160 msec) delay, and the delay was either predictable or not. Our results showed that the keypress peak force was modulated by the amount of auditory feedback delay even when the delay was unpredictable, thus demonstrating an on-line modulation effect. The latency of the on-line modulation was suggested to be as low as 70 msec, indicating a very fast sensory to motor mapping circuit in the brain. When the auditory feedback delay was predictable, a change in the very early phase of keypress motor output was found, suggesting that the prediction of sensory feedback is crucial to motor control. Therefore, even a simple keypress action contains rich motor dynamics, which depend on expected as well as on-line perceived sensory feedback.
Working Memory in Unilateral Spatial Neglect: Evidence for Impaired Binding of Object Identity and Object Location

Working memory (WM) is known to be impaired in patients with stroke experiencing unilateral spatial neglect (USN). Here, we examined in a systematic manner three WM components: memory of object identity, memory of object location, and binding between object identity and location. Moreover, we used two different retention intervals to isolate maintenance from other mnemonic and perceptual processes. Fourteen USN first-event stroke patients with right-hemisphere damage were tested in two different WM experiments using long and short retention intervals and an analog response scale. Patients exhibited more identification errors for items displayed on the contralesional side. Localization errors were also more prominent in the contralesional side, especially after a long retention interval. These localization errors were often a result of swap errors, that is, erroneous localizations of correctly identified contralesional objects in correctly memorized locations of ipsilesional objects. We conclude that a key WM deficit in USN is a lateralized impairment in binding between the identity of an object and its spatial tag.