Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Source Fusion Network for Remote Sensing Image Segmentation with Hierarchical Transformer

Recently, due to the limitations of single sensor, it is hard to improve the performance of land cover classification. The traditional image segmentation methods can not process the optical remote sensing images effectively, especially when optical sensor is affected by complex weather conditions. However, as an active radar, synthetic aperture radar(SAR) has the advantage of not being restricted by weather conditions with the the penetrability of electromagnetic radiation. So multi-sensor data fusion provides a great potential for land cover classification. In this paper, a new fusion network called SegFusion is proposed to improve the performance of land cover classification. There are two main components in SegFusion which are hierarchical Transformer encoder and Swin-Fusion(SW-Fusion) module. First, a hierarchical Transformer encoder is used to extract multilevel feature of optical and SAR images. By integrating features from different layers, we can obtain powerful representation that combines both low-resolution fine-grained features and high-resolution coarse-grained features. Second, SW-Fusion module is used to fuse the features of optical and SAR data. In SW-Fusion, we use modified Swin Transformer [1] block with multi-head cross-attention mechanism to exchange information between features from different sources.
Impact of Training Data Size on Classifiers When Coarse Resolution Imageries Were Used for Regional Land Cover Mapping

This work analyzes the impacts of training sample size on the performance of supervised classification methods when coarse resolution imageries are employed for regional land cover mapping. We utilized FegnYun-3C composite imageries with 1km spatial resolution and random forest (RF) and support vector machine (SVM) algorithms that were trained and tested with five sets of reference datasets: 66/34, 69/31, 73/27, 76/24 and 79/21.The results show that the performance of the two algorithms increases with increasing the size of the training examples until a certain point, and achieves the maximum accuracy (0.86 for RF and 0.84 for SVM) when the ratio was 76/24. However, considering the 79/21 (train/test) ratio made no change in accuracy, implying increasing a training dataset beyond a certain limit has no effect. Moreover, despite the size of training samples employed, the RF outperformed the SVM in contrary to the claim that SVM yields a better accuracy in case of scarce training data by pervious studies.
IDENTIFICATION AND CLASSIFICATION SPATIOTEMPORAL OF RAINY OR DRY CRITICAL EVENTS IN PERNAMBUCO STATE

The objective of this study was to analyze the dry and wet events historically occurred in the Pernambuco state and diagnose their intensities and durations. The identification and classification of areas susceptible to critical events has become a frequent need in the current context of climate change, which has caused disasters in the world. The diagnosis based on meteorological, agricultural and hydrological impacts can be measured with the application of the methodology of the Standardized Precipitation Index (SPI) for different time scales. The SPI was able to identify critical events in the state of Pernambuco and highlight the main through their intensities and durations. Scales were analyzed from quarterly to biennial which indicated the beginning, trend, duration and intensity of events. The results identified years with rainfall events in the moderate and severe categories, but also dry events in moderate to extreme categories. It was observed that the values of correlations between SPI scales are better as they approach in time. The cluster analysis of the SPIs showed well-defined areas, up to four groups, at all scales of time, this indicates that these groups have similar behavior during critical events.
Comparative Study of Various Components of Atmospheric Gradient in Relation to Rainfall over a Region

Precipitable water vapor (PWV) is a crucial atmospheric parameter in meteorological studies as it evinces a clear correlation with precipitation which makes it incumbent for rainfall prediction. Nevertheless, the rainfall is not solely dependent on PWV, other dynamic factors also initiate the occurrence of rain events. Hence, the research on weather forecast demands the investigation of other atmospheric parameters as well which can better predict the rain events with improved accuracy and better lead time, particularly for tropical region. Atmospheric gradient is another imperative parameter which changes its direction with the movement of impending weather event and provides real atmospheric information. Till date, previous studies have been conducted to predict rainfall for individual stations. Now, we have put a step forward to carry out our research for a region to analyze an imminent weather front and predict it accurately with sufficient lead time. The atmospheric gradient shows a converging nature at the time of precipitation exhibiting the similar behavior as its wet component, however, the hydrostatic gradient increases in magnitude when the rain event starts to occur.
Generative Framework Approach to Match Landsat and Sentinel-2 Data

Linear regression and histogram matching-based techniques have been widely used to minimize the surface reflectance difference between two similar satellite observations such as Landsat-8/9 and Sentinel-2A/B products. However, regionally or globally derived conversion factors may not be suitable for all land cover types and locations, resulting in noticeable residual differences between the sensors. Generative Adversarial Network (GAN) has shown promise in the field of image processing for domain or style transfer. In this work we aim to minimize the surface reflectance difference between Landsat and Sentinel-2 products based on GAN.This work selected 26 pairs of same-day Landsat and Sentinel-2 30-meter spatial resolution surface reflectance images in the green spectral band from NASA’s Harmonized Landsat/Sentinel-2 project (HLS), with each pair having over 90% spatial overlap. Upstream pre-processing steps included atmospheric correction, cloud masking and BRDF normalisation. The generator architecture was based on U-Net and discriminator as PatchGAN. GAN was trained for 43000 iterations. Finally, the Landsat images generated from the Sentinel-2 images by GAN are compared to the original Landsat 8/9 images in terms of SSIM and MSE metrics. Result showed a higher SSIM score for the generated data, which can be interpreted to mean that the generated images were closer to the real Landsat images. And, overall MSE for generated data was lower than that for the original Sentinel-2 images.This study, for the first time, reports a GAN-based spatial matching between Landsat8/9 and Sentinel-2 surface reflectance images. The results indicate that this approach has the potential to map data between the two satellite images with reasonable accuracy. This method may prove to be more robust and could be applied globally, potentially replacing the previous approach of simulated data. The method can be further extended with more data and in other spectral bands and potentially be used in HLS processing.
Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models

Over the past decade, Deep Learning (DL) models have proven to be efficient at classifying remotely sensed Earth Observation (EO) hyperspectral imaging (HSI) data. Those models show state-of-the-art performances across various bench-marked data sets by extracting abstract spatial-spectral features using 2D and 3D convolutions. However, the black-box nature of DL models hinders explanation, limits trust, and underscores the need for profound insights beyond raw performance metrics. In this contribution, we implement a simple yet powerful mechanism for the explainability of DL-based absorption features using an axiomatic approach called Integrated Gradients, and showcase how such an approach can be used to evaluate the relevance of a network’s decisions, and compare network sensitivities when trained using single and dual sensor data.
Valor de corte de HAQ para predecir discapacidad laboral en pacientes con artritis reumatoidea

La discapacidad laboral ocurre de forma temprana en pacientes con Artritis Reumatoidea (AR), con un importante impacto socioeconomico. En estudios previos, el HAQ ha demostrado ser un fuerte predictor de perdida del status laboral. Objetivos: Determinar el valor de corte de HAQ-A y otros factores asociados a desocupacion laboral en pacientes con AR. Material y metodos: Se incluyeron 309 pacientes con AR de la consulta ambulatoria del IREP. Se consignaron variables demograficas, socioeconomicas, manifestaciones extraarticulares y tratamientos recibidos. La actividad de la enfermedad fue evaluada por DAS28, dano radiologico de manos y pies por score de Sharp van der Heidje y capacidad funcional por HAQ-A. Resultados: 80 pacientes estaban desocupados, de los cuales en 49 la desocupacion estaba relacionada con la AR (62%). En el analisis univariado, una menor edad, edad menor al inicio de la enfermedad y mayor valor de HAQ se asociaron significativamente con desocupacion. En el analisis multivariado, la discapacidad medida por HAQ fue la unica variable asociada. En el analisis por curvas ROC, un valor de corte de HAQ 0,87 tuvo una sensibilidad de 61% y una especificidad de 67% para predecir discapacidad laboral. Conclusiones: Este es el primer estudio en nuestro pais, que demuestra que un valor de HAQ mayor de 0,87 se asocio con discapacidad laboral.
Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning

In this paper, we discuss the integration of systems such as multi-dimensional radiative transfer models (RTM) with deep learning (DL) algorithms to estimate plant biochemical, physiological, and morphological traits at canopy level using high-resolution hyperspectral imagery (361 bands in the 400-1000 nm spectral range). We applied the approaches to two case studies for dryland cropping in Australia (i.e., wheat and sorghum). Crop type averages for the early flight for leaf area index (LAI) varied between 2, for Canola, to as high as 4.3 for Lentils. Wheat and Barley had LAI of 4.1 and 3.8 (m<sup>2</sup>/m<sup>2</sup>), respectively. Chlorophyll a+b (C<inf>a+b</inf>) averages for emerged crops were 18, 41, 44, 51 and 59 μg/cm<sup>2</sup> for Faba beans, Wheat, Canola, Barley and Oats, respectively. The pigment Anthocyanin varied from 4.9 to 15.9 μg/cm<sup>2</sup> for Lentils and Canola, respectively. Similar patterns were observed in the Carotenoid (C<inf>x+c</inf>) levels (as high as 16.5 μg/cm<sup>2</sup> for Oats). For sorghum plots, the integrated DL approaches showed significant high correlation in predicting sorghum LAI (R<sup>2</sup> = 0.84, RMSE = 0.65 m<sup>2</sup>/m<sup>2</sup>) and C<inf>a+b</inf> (R<sup>2</sup> = 0.94, RMSE = 4.94 µgcm<sup>-2</sup>). The maximum velocity carboxylation rates (Vcmax) varied between 45-75 µmol m<sup>-2</sup>s<sup>-1</sup>. For both studied periods, we yielded a R<sup>2</sup> > 0.78 and RMSE <= 5.35 µmol m<sup>-2</sup>s<sup>-1</sup>, being the RMSE lower when using the modelled fluorescence emission for retrieving the Vcmax. In addition, we derived the solar induced fluorescence emission hyperspectral narrowband (5.8 nm) sensing and radiative transfer models (RTM).
Design Platform Test and Airborne Implementation of FMCW SAR System

The traditional SAR radar system is relatively complex, high cost, large volume and weight, and can not be installed on small and light aircraft and starchain platforms, which limits the large-scale application of SAR. The radar system design scheme adopts technologies such as FM continuous wave system, function and system integration, RF direct modulation, digital demodulation and chip design, which reduces the complexity of the system, reduces the volume, weight and power consumption of the system, and realizes a small size, light weight and low power consumption micro SAR system. The waveguide slot array antenna and the corresponding test platform are designed, and the imaging algorithm of FMCW SAR is improved. Through the flight test of the micro SAR system on a small aircraft platform, the test results verify the effectiveness and feasibility of the system.
A Siamese Network for Semantic Change Detection Based on Multiscale Context Fusion

Bi-temporal semantic change detection(SCD) is more sophisticated than binary change detection and it provides more detailed changing information with categories. Naturally, it is more challenging than traditional binary change detection. In this paper, a Siamese CNN is proposed for SCD. For the problems of complex backgrounds of remote sensing images, we use multiscale context information and correlation to enhance SCD performance. For the problem of insufficient feature utilization between subtasks, a channel fusion module is proposed to explore the temporal correlation between bi-temporal images, which benefits the extraction of the final changing map. The experiments in this paper are conducted on the SECOND dataset. Our proposed method outperforms compared methods and obtains more completed changing maps than other methods.
An Enhanced Deep Learning Approach for Detecting COVID-19 using Chest X-rays Images

Since the SARS-CoV-2 virus (Covid-19) epidemic began, many scientific methods and research have contributed to speeding up and automating the process of diagnosing patients who are carriers of the virus. Medical and health regulations over CT recommended X-rays as the first line of diagnosis for many reasons, including the safety of spreading the virus, less cost, and faster results. Thus, extensive research efforts used deep learning techniques to detect COVID-19 and types of pneumonia. Despite these efforts yielding high-performance results, their diagnosis has several concerns and limitations regarding the trained model's reliability, robustness, and quality. Radiology images are not adequate in the early stage of infection. Also, Several research efforts used overlapped images of common diseases, improper images, and a combination of inconsistent image sources. Thus, the paper's contribution is multifold. First, We present a cleaned and consistent dataset with the help of radiologists who re-annotated an existing public dataset. Second, we present a framework using a convolutional neural network (CNN). To distinguish the types of Pneumonia we used two layers of CNN models. Results of experiments on the proposed cleaned dataset using different CNN models demonstrate that the VGG16 model achieved 92% and 84% specificity and accuracy, respectively, in classifying viral and bacterial pneumonia.
Soil Moisture Retrieval Using Sentinel-1 Data Based on Resnext

High-resolution soil moisture (SM) products are of broad scientific interest and practical application value. The flexibility of estimating soil moisture from Sentinel-1 data has been widely recognized. However, the backscatter coefficient of synthetic aperture radar (SAR) data is heavily influenced by noise during radiation transmission as well as vegetation cover. This directly leads to the inability to accurately achieve high-resolution SM estimation using a single SAR data. In this paper, we proposed a deep learning model that fuses multisource data that estimates the SM in the depth of ~5 cm. The inputs to the model integrated remote sensing data and soil information data. The model was trained and validated on data from in-situ sensors of the international soil moisture network (ISMN). The proposed method achieved a coefficient of determination (R2) of 0.732 and a root mean square error (RMSE) of 0.069. In addition, the validation experiments in Anhui Province, China also demonstrated the effectiveness and robustness of the proposed method with a R2 of 0.85.
Land Use Classification Efficient Vision Transformer

Land use classification has been a topic of great interest for many years. The ability to automatically recognize land cover as well as detect changes in remote sensing images is important for a variety of industries. Due in part to the size of satellite images, as well as the inefficiency of current methods, machine learning solutions are often very large. With a growing need for on device analysis, it is becoming more important to perform accurate processing without over utilizing resources. To meet this growing need, this paper presents an efficient algorithm for land use classification. The efficacy of the proposed solution is verified through studies to accurately classify remotely sensed images within the EuroSAT dataset.
Transfomer Based Hyperspectral Dimensionality Reduction with Gabor Kernel CNN for Feature Extraction

Dimensionality Reduction (DR) algorithms are used to identify sparse representation of given dataset. The proposed MT-CGF (Multiheaded Transformer with CNN and Gabor Filter) model uses the Multi-head Transformer-based Attention technique [1] for channel attention, which helps identify the most important bands in the dataset. The attention mask generated by the Transformer model is then used to select the top 'n' optimal bands. CNN with Gabor Filter [2], extract complex spatial and spectral features from the dimensionality reduced dataset. Gabor Filters are linear filters that closely resemble the human visual system and possess optimal localization properties in both spatial and frequency domains. Overall, the proposed MT-CGF model combines the strengths of multiple techniques to perform dimensionality reduction and feature extraction from hyperspectral data. By identifying the most important bands it reduces the dataset's dimensionality without significantly compromising its accuracy. It becomes more manageable and easier to analyze, and it can also help to reduce the risk of overfitting.
Formal analysis are fundamental for the de�nition of honey, a product representing speci�c territories and their changes: the case of North Tyrrhenian dunes (Italy)

Honey is a variegate matrix depending on the �oral origin, and it can become an important agri-food product to valorise speci�c territories. Being so diverse, different analytical techniques are necessary for its description. Herein we characterized the honey produced in one of the largest Italian sand dunes systems hosting beekeeping activities. In terms of �oristic origin, phytochemical characterization, and sensory and colour analysis, honey collected in 2021 and 2022 was comparable. Honey was poly�oral, with several pollens from dune habitat plants classi�ed as minor. The allochthonous Amorpha fruticosa L. and the ruderal Rubus fruticosus L. pollens in the category of the secondary pollens testify the alteration of the park vegetation. The phytochemical pro�le was rich in polyphenols. Other interesting compounds were coumarine derivatives, likely attributable to resin-laden plants as rockroses, long chain hydroxyacids typical of royal jelly and nicotinic acid and its analogues (2-hydroxynicotinic acid and 2-hydroxyquinoline). The above-mentioned honey showed interesting features and was a good representation of the vegetation of this area. Our study pointed out the importance of relying on multiple analytical techniques for the characterization of honey and the advisability of a technical support toward beekeepers to correctly describe and valorise their product.
Extent and Depth of Flooding Using SAR Sentinel-1 and Machine Learning Algorithms

Climate change is causing extreme events to become more and more frequent around the world; In particular, Mexico faces recurring floods with loss of human lives and infrastructure. The objective of this work was to identify the extent and depth of flooded areas using SAR Sentinel-1, DEM, and Random Forest and Logistic Regression algorithms. The Random Forest algorithm achieved better classification metrics over the Logistic Regression algorithm; both with a good performance to identify floods using SAR and DEM images to respond to the contingencies caused by this extreme phenomenon.
RCS Statistical Feature Extraction for Space Target Recognition Based on Bi-LSTM

Space target recognition is of great significance for ballistic missile interception, especially in distinguishing between real and decoy warheads. This paper proposes a recognition method that combines feature extraction and Bi-LSTM, surpassing existing algorithms in classification performance. To obtain more discriminative sequences, the method extracts 11-dimensional statistical features of the Radar Cross Section (RCS) sequence using a sliding window, and inputs the feature sequence into the Bi-LSTM network for classification. Even under low signal-to-noise ratio conditions, the proposed algorithm maintains strong performance, demonstrating its noise resistance and generalization ability.
Feature Preserving Decimation of Urban Meshes

3D models of urban buildings have paramount importance to most digital urban applications. However, requirement of large storage and high computational cost for processing the geometric details of urban objects have been observed as a major limitation to existing 3D modeling approaches. This draws the need of lightweight modeling techniques requiring less computational storage to capture the details of the urban entities. Additionally these models should facilitate accelerated visualizations along with consuming lesser bandwidth for online applications. In this paper, we propose a lightweight urban modeling method using gradient structure tensors based feature point extraction to produce highly detailed lightweight 3D building models from LiDAR scans. Further, a mean cost-based edge collapse operation is proposed to preserve the feature points. The qualitative and quantitative analysis and comparative study of different building façade models shows the efficacy of our method in generating simplified models with a trade-off between model simplification and accuracy.
Quantitative Study on the Structure-bioavailability Relationship of Dissolved Organic Nitrogen in Wastewater Treatment Plant E�uent

This study systematically investigated the relationship between the structure properties and biological characteristics of DON in the e�uents from municipal wastewater treatment plants. Ultra�ltration, FTIR spectroscopy, UV spectroscopy and EEM �uorescence spectroscopy were used to characterize the structure of organic matters in the e�uent samples, and the bioavailability of DON was determined by algal/bacterial based bioassay. The quantitatively analysis of EEM spectra conducted by �uorescence regional integration method showed that the organic portion of all samples were mainly consistent with fulvic acid and protein. Combined with the bioassay results, a positive correlation between the DON bioavailability and the protein content (sum of region I and region II) (r=0.80, P(cid:0)0.02) and soluble microbial byproduct-like materials (region IV) (r=0.76, P(cid:0)0.03) were observed. Nevertheless, the humic substances content represented by the region III and V would negatively affect the DON bioavailability. High humi�cation degree (high HIX value) (r=-0.77, P(cid:0)0.03) was related to low bioavailability. Furthermore, according to UV spectroscopy results, strong aromaticity (high UV 254 values) (r=-0.78, P(cid:0) 0.03) suggested low DON bioavailability. The ultra�ltration experiment showed that the low molecular weight DON ((cid:0)3kDa) accounted for 30-73% of the total DON, and no notable relationship was observed for DON molecular weight and its bioavailability.