Miracle-dz/UniFAR-FTUs huggingface dataset:
Oil Spill Detection in Calm Ocean Conditions: A U-Net Model Novel Solution

Oil spills severely damage marine life and coastal environments. To reduce their polluting effect on the ecosystem, it is important to promptly react to potential spills for early detection and monitoring. In this paper, we propose a drone-based solution with a deep-learning U-net model. It processes the radar backscattering dominated by the specular component in calm ocean conditions to detect contaminated sea surfaces with oil spills. Results show that our approach achieves a high detection rate exceeding 90% for thick oil slicks in the range of 1-10 mm.
An Extremely Lightweight U-Net with Soft Fusion for Flood Detection Using Multi-Source Satellite Images

Multi-source heterogeneous satellite image time series (MSH-SITS) have become a robust way to acquire flood detection results thanks to their high-resolution and wide coverage areas advantages. In this paper, we propose an extremely lightweight and soft fusion-based Unet (LSFUnet) architecture for flood detection based on MSH-SITS to improve the computation efficiency of existing methods. Specifically, we build the encoder-decoder module with lightweight residual blocks using a reduced number of convolution layers and smaller convolution kernel size to accelerate our flood detection method. To compensate for the performance loss caused by lightweight operations, we utilize the historical flood information as the long-term and short-term constraints on the current flood detection results. Experimental results using Gaofen-1, Gaofen-3, Gaofen-6, Huanjing-2, Sentinel-1, and Sentinel-2 satellite images demonstrate the efficiency and effectiveness of our proposed LSFUnet method.
Ship Classification Using Layover in Sentinel-1 Images

In this paper, a novel algorithm for ship classification in Sentinel-1 synthetic aperture radar (SAR) images is presented. The algorithm utilises layover as the main classification feature, which is based on the different relative heights of superstructures in oil tankers, container ships, geared and gearless bulk carriers. The algorithm has been tested using 20 ship samples from Sentinel-1 stripmap images over the port of Santos, divided equally amongst the four ship classes. An overall classification accuracy of 75% has been achieved.
An Automated Theorem Proving Framework for Information-Theoretic Results

We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e.g. Wyner and Gács-Körner common information), and discovery of non-Shannon-type inequalities, under a unified framework. Our implementation successfully generated proofs for 32 out of 56 theorems in Chapters 1–14 of the book Network Information Theory by El Gamal and Kim. Our framework is based on the concept of existential information inequalities, which provides an axiomatic framework for a wide range of problems in information theory.
Image Reconstruction for Low-Oversampled Staggered SAR Based on Bayesian Compressive Sensing

Staggered synthetic aperture radar (SAR) is an innovative concept of high-resolution and wide-swath systems, it combines SCan-On-REceive (SCORE) with continuous variation of the pulse repetition interval (PRI) to deal with the blind ranges over wide areas. Since the acquired data is not uniformly sampled and is partially lost, proper reconstruction methods should be considered to achieve good image quality. The existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform traditionally focused processing. However, these algorithms lack robustness with regard to the PRI strategies, especially under low oversampling or even sub-Nyquist sampling. In this article, a reconstruction algorithm based on Bayesian compressive sensing (BCS) is proposed for low-oversampled staggered (LS) SAR in the presence of an optimum random strategy. Simulations and experiments on raw data generated in LS SAR are performed to verify the effectiveness of the proposed method.
An Automatic Detection of Drowsy or Distracted Driver Based on Brain Computer Interface

Driving requires sustained concentration, yet many road accidents are attributed to factors such as driver tiredness, drowsiness, inattention, and distraction. Electroencephalographic (EEG) recordings, which capture electrical brain activity via head-attached electrodes, have proven effective for drowsiness identification. This paper aims to utilize a brain-computer in-terface (BCI) that gathers EEG brain signals, classify driver's drowsiness using machine learning algorithms on mentioned EEG signals, and developing a mobile interface to alert drowsy classified drivers. The recorded brain signals from the BCI undergo several phases, including feature extraction and classifi-cation using various learning-based algorithms such as Logistic Regression, K-Nearest Neighbor, Naive Bayes, Support Vector Machines, Random Forest, Decision Tree, and Neural Networks. The accuracy of drowsiness detection using these algorithms is evaluated, with the Decision Tree achieving the highest accuracy of 78.08%. Detected instances of drowsiness are translated into visual and audible feedback on a mobile device. This research contributes to advancing drowsiness detection methods and intervention techniques, potentially enhancing road safety and mitigating the risks associated with driver fatigue.
Detection time for global and regional sea level trends and accelerations

Many studies analyze trends on sea level data with the underlying purpose of finding indications of a long-term change that could be interpreted as the signature of anthropogenic climate change. The identification of a long-term trend is a signal-to-noise problem where the natural variability (the “noise”) can mask the long-term trend (the “signal”). The signal-to-noise ratio depends on the magnitude of the long-term trend, on the magnitude of the natural variability, and on the length of the record, as the climate noise is larger when averaged over short time scales and becomes smaller over longer averaging periods. In this paper, we evaluate the time required to detect centennial sea level linear trends and accelerations at global and regional scales. Using model results and tide gauge observations, we find that the averaged detection time for a centennial linear trend is 87.9, 76.0, 59.3, 40.3, and 25.2 years for trends of 0.5, 1.0, 2.0, 5.0, and 10.0 mm/yr, respectively. However, in regions with large decadal variations like the Gulf Stream or the Circumpolar current, these values can increase up to a 50%. The spatial pattern of the detection time for sea level accelerations is almost identical. The main difference is that the length of the records has to be about 40–60 years longer to detect an acceleration than to detect a linear trend leading to an equivalent change after 100 years. Finally, we have used a new sea level reconstruction, which provides a more accurate representation of interannual variability for the last century in order to estimate the detection time for global mean sea level trends and accelerations. Our results suggest that the signature of natural variability in a 30 year global mean sea level record would be less than 1 mm/yr. Therefore, at least 2.2 mm/yr of the recent sea level trend estimated by altimetry cannot be attributed to natural multidecadal variability.
Distributed SAR Data Processing Aided by Machine Learning

The distribution of the space key resources, usually concentrated in a single, large, and complex satellite, can be shared among small-sized and simpler systems, thanks to the proper combination of the signals from each single node of the swarm. A proper sensor positioning, and on-ground data recombination allow the azimuth ambiguities to be cancelled out, guaranteeing gain in terms of signal to noise ratio. However, sensors synchronization, position control and trajectory knowledge errors are significant impairments which may make ineffective the recombination algorithms since the error contributions are hard to estimate.In this paper, a Deep Learning based approach is proposed for performing the recombination of data coming from a swarm of satellites and where no a priori knowledge error is exploited. The proposed system provides promising results in terms of ambiguity rejection.
Tranfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on Lidar Data

When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawback. However, there is still a need to explore its effectiveness when applied across different archaeological datasets. This paper compares the performance of various transfer learning configurations using two semantic segmentation deep neural networks on two LiDAR datasets. The experimental results indicate that transfer learning-based approaches in archaeology can lead to performance improvements, although a systematic enhancement has not yet been observed. We provide specific insights about the validity of such techniques that can serve as a baseline for future works.
Easy Park: Mobile Application for Parkinson's Disease Detection and Severity Level

Parkinson's Disease (PD) is a severe neurodegen-erative disorder that affects the central nervous system of humans. The lack of specific clinical tests to accurately diagnose Parkinson's Disease poses a challenge in early detection. PD is characterized by motor symptoms such as tremors, rigidity, and bradykinesia, along with additional motor deficiencies like handwriting problems and speech impairments. Another defining characteristic, often present in the early stages, is hypomimia, a decrease in facial expressiveness. Early detection is crucial to initiate timely therapy and prevent further damage. However, diagnosing PD in its early stages is challenging due to subtle symptoms that can mimic other conditions, and the absence of specific diagnostic tests. Various techniques including machine learning and deep learning approaches utilizing hand-drawn wave and spiral images, facial photographs, and voice recordings as potential biomarkers for PD detection. In our study, we applied convolutional neural networks (CNN) to analyze spiral and wave drawings, achieving an accuracy of 85% for spirals and 80% for waves. Additionally, we utilized a support vector machine (SVM) for facial analysis, obtaining an accuracy of 71%. However, it should be noted that the accuracy of facial analysis was limited due to the small dataset size. Lastly, for voice recordings, we employed XGBoost and achieved an impressive accuracy of 97%. Our results demonstrate promising accuracy for spiral, wave, and voice-based detection, highlighting the potential of machine learning techniques in PD diagnosis. The integration of biomarkers such as imaging and biological markers holds further potential for enhancing diagnostic capabilities in Parkinson's Disease.
Decentralized Market-Based Observation Assignment Strategy for Dynamic Networks in Sensor Web Mission Concepts

Monitoring of short-lived and highly-dynamic processes and events such as floods or forest fires has gained an increasing interest in Earth Observation, particularly as global climate change is affecting these processes. The observation of such dynamic events is often limited by the response time of human operation of Earth-Observing satellites or UAVs.To address this bottleneck, this paper presents a Modified Asynchronous Consensus Constraint-Based Bundle Algorithm (MACCBBA) for observation task allocation in Sensor Web mission concepts for Earth Observation. This algorithm allows for the decentralized allocation of observation tasks amongst a network of Satellites and UAVs based on recently measured data processed on board, or on messages received from other sensors or from the ground. This algorithm is also capable of reaching a feasible plan in a dynamic communications network such as the ones present in some Sensor Web mission concepts and allows for complex temporal constraints and dependencies between tasks to model the value of near-simultaneous co-observations by complementary or synergistic sensors.
A High Accuracy Detection Network for Rotated Multi-Class SAR Ship Detection

At present, most of the ship detection methods use horizontal detection bounding box, which results in the interference of dense ships and decreases the detection accuracy. In addition, most of the current ship detection methods remain in single-category detection, and do not achieve multi-class ship detection, which limits the further promotion and application of these detection methods. In this work, we propose a novel network for high-precision detection of rotated multi-class ships by rotated bounding box, called Rotated Multi-Class Detection Network (RMCD-Net). In RMCD-Net, we adapt a rotated anchor-feature alignment module (RAAM) to solve the misalignment problem between rotated anchors and horizontal features. In RMCD-Net, we adapt double detection head mechanism for better regression and classification. Also, we apply focal loss to classification task. Experimental results on the public dataset SRSDD show that mAP of RMCD-Net is 61.62% that is better than the second-best model by 5.39%.
Color-Aware Self-Supervised Learning for Scene Classification and Segmentation of Remote Sensing Images

Recently, fully supervised deep learning has achieved excellent success in remote sensing (RS) scene classification and segmentation. However, supervised learning requires tremendous labels, which are difficult to obtain in the field of RS. Self-supervised contrastive methods alleviate this problem by learning impressive transferable representations invariant to different data augmentations, e.g. color jittering. Such invariance could be harmful to RS scene classification and segmentation, which is sensitive to color changes. Therefore, we introduce a color-aware self-supervised learning framework (ColorSelf) for RS scene classification and segmentation. Our model encourages to preserve color-aware information in learned representation to improve their transferability. Extensive experiments on two challenging RS datasets demonstrate the proposed ColorSelf brings a significant performance improvement in both RS scene classification and segmentation task.
Enhancing Extractive Text Summarization of Long Documents Using Features Dimensionality Reduction

Automatic Text Summarization (ATS) task is considered one of the most challenging tasks in Natural Language Processing field, especially for long documents. There are many techniques for ATS, including Artificial Intelligence (AI) approaches, statistical-based approaches, graph-based approaches, etc., but the generated summaries are still less accurate than human summaries. This research presents an extractive Neural-based model to summarize long documents using both attentive global and local contexts, Word2Ve$c$ embeddings, and a bidirectional Gated Recurrent Unit (GRU) as a Sentence Encoder. The proposed model uses a Multi-Layer Perceptron (MLP) that consists of two linear layers with Rectified Linear Activation Units (RELUs) to reduce features dimensionality. The proposed model is evaluated on PubMed and arXiv datasets using ROUGE metrics, and the evaluation results show remarkable and promising improvements for ROUGE-1, ROUGE-2, and ROUGE-L.
A coupled SWAT-MODFLOW model to evaluate the effects of management practices on surface-groundwater

Abstract. Water quality pollutants, which may be generated due to point and non-point sources (e.g. agricultural activities), can affect both surface water and groundwater resources. The objective of this study is to assess the effects of management practices on surface water and groundwater using Soil and Water Assessment Tool (SWAT) and MODFLOW models in the Big Sunflower River Watershed (BSRW) in Mississippi. The MODFLOW model, was used to simulate groundwater flow with boundary, recharge and HRUs calculated from SWAT model. A coupled SWAT-MODFLOW model was applied to evaluate the water quality and quantity effects due to different agricultural management practices. The results of this study showed that more interactions between surface water and groundwater exist in the downstream area of watershed. Different management practices will be applied to coupled SWAT-MODFLOW model to evaluate the effects of water quantity on surface-groundwater.
A Deep Learning Approach for Detection of Internal Gravity Waves in Earth’s Ionosphere

Tsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd.
BRAIN TUMOR CLASSIFICATION BASED ON 3D AUTO ENCODING TECHNIQUE

The brain is an essential part of the entire nervous system because it can control and regulate body parts. The International Agency for Research on Cancer (IARC) estimates that 76% of deaths from brain tumors are cancer related. Abnormal cells in the brain cause brain tumors and make life dangerous if untreated at the early stages. A significant portion of the Image is extracted using image processing techniques during tumor image classification. The 3D shape is a complex structure in the 3D space; because of this reason, there would be a minimal of 3D shapes for feature learning. To cope with those challenges, research paper transform 3D shapes into 2D space and use automatic encoders to learn features from 2D images. The proposed 3D based Spatial Auto Encoder (3D-SpAE) approach is automated by learning a state representation directly from camera images and the state-space constructions. Once the autoencoder is trained, the coefficients acquired for reconstructing an image based on prototypes which are utilized as a feature for 3D shape matching and retrieval. The autoencoder can achieve high performance for image retrieval because it can learn feature adaptively from training data. 3D-SpAE classified the brain tumor data into Whole-Tumor, Tumor Core, and Enhancing Tumor. The results showed that the suggested method obtained better values of the accuracy of 99.4% when compared to the existing Nonparametric Localization Enhancement Methods with U-Net, 3 D Context Deep Supervised U-Net, Deep Elman Neural network with adaptive fuzzy clustering, NSGA, Convolutional Neural Network, and Kernel-based SVM.
Automatic Detection of Xylella Fastidiosa in Aerial Hyperspectral and Thermal Data

Xylella fastidiosa (Xf) is a plant pathogen affecting olives trees, which has been identified as the bacterium responsible of a devastating landscape transformation in Apulia Region (Italy) from 2013. Actually, it has been found to affect 679 plant species worldwide, such as almond, vine and citrus.In this paper, experimental results concerning the automatic detection of trees infected by Xf from very high resolution hyperspectral and thermal images are shown. First of all, a set of vegetation indices and plant physiological traits related to rapid changes in photosynthetic pigments and leaf processes were computed from hyperspectral data. This information together with thermal data has been used as input to a RUSBoost classifier. Trees in training and test data set were labelled by performing quantitative real time-Polymerase-Chain-Reaction (qPCR) assays.Encouraging experimental results have been obtained, with Overall Accuracies greater than 90%, also when a reduced set of features is used as input for RUSBoost.
Mapping "Brain Coral" Regions on Mars Using Deep Learning

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been focusing on regions on Mars that may have liquid water. A set of critical areas may have seen cycles of ice thawing in the relatively recent past in response to periodic changes in the obliquity of Mars. In our study, we employ the use of convolutional neural networks to identify specific areas of the Martian surface that have a terrain feature referred to as "Brain Coral". This terrain type is known for its resemblance in morphology to "sorted stone circles" found on Earth, which may indicate that it formed due to cycles of freezing and thawing. Brain coral is a term used to describe the texture of a specific type of Martian terrain, which is characterized by stippled, labyrinthine ridges and are found primarily in mid-latitudes. They are often associated with lineated valley fill (LVF) and concentric crater fill (CCF) (e.g., [1] ; [2] ; [3] ). In arctic regions on Earth, "sorted stone circles" form as a result of rock-bearing and water-rich soil layers undergoing cycles of expansion and contraction in response to periodic freezing and thawing of the soil ( [4] ; [5] ; [6] ). These cycles result in rock and soil separation which in turn leads to pattern formation (e.g. [7] ; [8] ; [9] ). However, even though brain coral on Mars and sorted stone circles on Earth have similar appearances, it does not necessarily mean that the same processes formed them. There are multiple young geomorphic features on Mars that suggest thawing occurred in the recent past and may still be ongoing, but there is ongoing controversy about it ( [10] , [11] , [12] , [13] , [14] ). As such, it is crucial to compare existing hypotheses and conduct a thorough examination of this terrain to better understand its formation.
Research on Vegetation Parameter Inversion of Open-Pit Mines Based on Lidar Data

The monitoring of vegetation recovery was an important factor to evaluate the effectiveness of ecological restoration of open-pit mines. Lidar data can obtain the horizontal and vertical structures of trees that was wildly used to characterize the 3-dimensional (3D) structure of trees. In this paper, taking Ba’nan District of Chongqing as a study area, high density lidar data was applied to obtain the vegetation vertical structure parameters based on marker-controlled watershed segmentation method with IPTD filtering algorithm. The result showed, average tree height, diameter at breast height (DBH), crown diameter of open-pit mines was relatively lower compared with other areas outside as the trees of open-pit mines were mainly artificial planting and the growth time of trees was about 3-5 years. The trees had recovered well in the process of restoration. It indicated that the ecological restoration and planting of trees in open-pit mines were still in the process of recovery.