Miracle-dz/UniFAR-FTUs huggingface dataset:
Wall-to-Wall Above-Ground Biomass Estimation with Alos-2 Palsar-2 L-Band SAR Data and GEDI

Under the impact of climate change, monitoring forest carbon stock becomes an important task to evaluate the changes in carbon sequestrated from the atmosphere. Forest carbon stock estimation is still a challenging task, due to limited data sources that have a high correlation with above-ground biomass. With the help of the NASA Global Ecosystem Dynamics Investigation (GEDI) mission, above-ground biomass (AGB) can be measured by using the LiDAR data provided. However, GEDI data is sparse since it only samples about 4% of the Earth’s land surface between 51.6° N&S. Previous studies demonstrated L-Band SAR’s promising ability in retrieving forest stem volumes and estimating above-ground biomass. In this work, we propose a Deep Learning based workflow which utilizes PALSAR-2 L-Band images and GEDI to generate wall-to-wall above-ground biomass maps of North America. The workflow uses Convolutional Neural Network as the DL model and leverages both PALSAR-2 L-Band images and GEDI Relative Heights data to estimate the dense above-ground biomass maps. The results show that, by fusing GEDI Level 2 Relative Heights data with PALSAR-2 L-Band SAR data, it is possible to achieve a significantly high correlation with GEDI level 4 AGB data, as the final R-squared score of our model is as high as 0.83.
Redescription de Streptospondylus altdorfensis, le dinosaure théropode de Cuvier, du Jurassique de Normandie

Redescription of Streptospondylus altdorfensis, Cuvier’s theropod dinosaur from the Jurassic of Normandy. The theropod dinosaur remains from the Callovo-Oxfordian of the Vaches Noires, figured for the first time by Cuvier, are redescribed. The systematic revision shows that Streptospondylus altdorfensis is the valid name to which the whole of the material should be assigned. A few vertebral features suggest the close relationships existing between Streptospondylus and Eustreptospondylus from the Callovian of England: both genus are related to Spinosauroidea. The diversity of the European theropods at the end of the Middle Jurassic and the beginning of the Late Jurassic is outlined.
MENTAL HEALTH ANALYSIS USING NATURAL LANGUAGE PROCESSING

Many people all over the world are depressed and are completely unaware of it. Depression is a mental illness in which a person is constantly unhappy and loses interest in almost everything. Depression can result in self-harm or even suicide. People can, thankfully, recover from depression with the help of therapy and medication. When a person's depression is detected early, his or her recovery will be greatly aided. Our project's main goal is to detect the depression in users' speech while also providing assistance for depression recovery. Nlp models such as word embedding and tone analyzer are used to detect depression, and recovery guidance given to the patient by providing the consultant details in their surroundings.
Stare Processing Improves GNSS-R, Machine Learning-Based Ocean Wind Speed Retrieval

The objective of this study is to investigate the utility of the GNSS-R based stare processing technique in combination with machine learning (ML) for ocean surface wind speed retrieval from CYGNSS delay-Doppler maps (DDM). Building on our previous work using a simulated data set [1], this study presents an analysis of stare processing and machine learning methodologies applied to real CYGNSS data. We train three ML algorithms, neural network, bagging regressor, and gradient boosting regressor, with six feature sets derived from the DDM observable and satellite geometry. We compare stare processing performance to the baseline best-performance result from our previous work in [2], measuring performance with root mean squared error (RMSE) and mean absolute percentage error (MAPE). Stare processing in combination with the bagging regressor algorithm achieves an 8.3% RMSE accuracy improvement over the baseline, with an RMSE of 2.5 $\frac{m}{s}$. By replicating and comparing to the process used to produce the best performance result in [2], we can directly see the effect of the addition of stare processing features. We show that stare processing improves ocean wind speed prediction accuracy, and expect that it could be an advantageous addition to other ML-based wind speed prediction methods in the future.
Using UAV Data to Improve the Situational Awareness for First Responders in Disaster Management: The Example of Flooding in the AHR Valley, Germany

In October 2021 and 2022 two field exercises, led by the Bavarian Red Cross (BRK) and German Aerospace Center (DLR) took place in the Ahr Valley, Germany, which was hit by severe floods in July 2021. Focus thereby was the use of Unmanned Aerial Vehicles (UAV) for crisis mapping to enhance situational awareness for first responders in disaster management. The case study highlights the benefits of decentralized UAV data acquisition, processing, and transfer. This includes subsequent mapping of flooded areas as well as automatically detecting buildings and vehicles using machine learning workflows. Additionally, navigable and analyzable 3D scenes are calculated from the drone data and provided to the first responders. The exercise in the Ahr Valley showed that the acquisition of structured UAV data can improve situational awareness, disaster management strategies, and response capabilities, leading to more effective and efficient emergency management.
AI-Based Performance-Optimized Quantization for Future SAR Systems

Next generation SAR systems will bring a huge improvement in terms of SAR performance and coverage through the use of large bandwidths and digital beamforming techniques in combination with multiple acquisition channels. This will allow for overcoming the limitations imposed by conventional SAR imaging for the acquisition of wide swaths and, at the same time, of finer resolutions. The significant improvements that can be achieved in terms of performance are associated with the generation of large volumes of data, which, in turn, set harder requirements for the onboard memory and downlink capacity of the system.In this scenario, an efficient quantization of SAR raw data is of crucial importance, as it defines the amount of onboard memory and it directly affects the quality of the generated SAR products. In the Performance-Optimized BAQ (PO-BAQ), the basic concept of the original BAQ is further extended according to the approach proposed in [1], which represents a first attempt for an optimization of the resource allocation depending on the performance requirement defined for the final higher-level SAR/InSAR product. As quantization errors are significantly influenced by the local distribution of the SAR intensity, such an optimization is achieved by exploiting the a priori knowledge of the SAR backscatter statistics of the acquired scene.In this contribution we investigate the feasibility of deriving a Performance-Optimized bitrate map through a machine learning-based architecture, in view of a future possible onboard realization.
Installation and Initial Operation of DOE's 449-MHz Wind Profiling Radars on the U.S. West Coast

The U.S. Department of Energy (DOE), in collaboration with the National Oceanic and Atmospheric Administration (NOAA), has recently completed the installation of three new wind profiling radars on the Washington and Oregon coasts. These systems operate at a frequency of 449 MHz and provide mean wind profiles to a height of roughly 8 km, with the maximum measurement height depending on time-varying atmospheric conditions. This is roughly half the depth of the troposphere at these latitudes. Each system is also equipped with a radio acoustic sounding system (RASS), which provides a measure of the temperature profile to heights of approximately 2 km. Other equipment deployed alongside the radar includes a surface meteorological station and GPS for column water vapor. This project began in fiscal year 2014, starting with equipment procurements and site selection. In addition, environmental reviews, equipment assembly and testing, site access agreements, and infrastructure preparations have been performed. Finally, with equipment deployment with data collection and dissemination, the primary tasks of this project have been completed. The three new wind profiling radars have been deployed at airports near Coos Bay, OR, and Astoria, OR, and at an industrial park near Forks, WA. Data are available through the NOAAmore » Earth Systems Research Laboratory Data Display website, and will soon be made available through the DOE Atmosphere to Electrons data archive and portal as well. « less
Modeling Spectral Mixing for Geological Mixtures: Detecting Nonlinearly Mixed Pixels in Hyperspectral Image of Banded Hematite Quartzite

Modeling spectral mixing for geological mixtures is challenging. The endmembers in these mixtures interact at a microscopic scale resulting in nonlinear mixing. Prior to applying inversion techniques for spectral unmixing, it is essential to identify the nature of nonlinear mixing that would facilitate a faster analysis of hyperspectral images for geological mixtures. This paper attempts pixel-wise nonlinearity detection of a hyperspectral image of a geological mixture (rock sample) collected in a controlled environment in the laboratory. The identified nonlinearly mixed regions were mapped and further validated through their spectral features in the principal component space. The insights obtained in this study would further support identifying the nature of mixing in geological mixtures.
Evaluation of Different Machine Learning Framework in Daily 1 Km Resolution Gridded Pm2.5 Prediction

Atmospheric fine particles (PM2.5) poses a significant threat to human health and several studies have been conducted to predict the PM2.5 concentrations in time series at monitoring stations. Gridded PM2.5 concentration prediction is relatively scarce. In this study, we evaluated representative ML-Based framework in daily 1 km gridded PM2.5 prediction to identify suitable prediction methods. To achieve this, random forest, convolutional neural network, and transformer were selected for evaluation. The results showed that the transformers model slightly outperformed the other two models in both qualitative and quantitative evaluations. However, all three models showed poor performance during winter, as the PM2.5 concentration is inherently higher and more variable. It is precisely such situation that people are most concerned about. Our research suggests that resolving "class imbalance" and "concept drift" are two potential approaches to address this issue.
Reconstruction of Sea Surface Temperature under Clouds using Masked Autoencoders

This paper presents a methodology for reconstructing high-spatial-resolution sea surface temperature (SST) fields under cloud cover using masked autoencoders (MAE). The MAE model is trained on high-resolution SST maps from the ECCO forward simulation, LLC4320, and reconstructs missing data by masking out a portion of the input pixels. The impact of masking ratios and methods, as well as network architecture variations, is investigated. Preliminary results show that MAE can reconstruct global SST under a random 80% mask to within 0.3°C root mean squared error (RMSE). Applying this methodology to SST data with significant cloud contamination can enhance dataset quality, uncovering details hidden by clouds and expanding the use of high-resolution SST images.
Replication and Update of Molecular Biology Databases in a Grid Environment

Update of molecular biology databases is a growing burden on the biomedical research community. As the grid allows to share and replicate data, we propose a service to automatically update the biology databases from a single changing reference using web services. In this paper we report the components, the architecture and the deployment of the update service on the french RUGBI grid infrastructure. RUGBI is a computing grid infrastructure based on existing middleware and technologies for the community of scientists in bioinformatics.
Machine Learning and Feature Extraction for Industrial Smoke Plumes Detection from Sentinel-2 Images

The detection of smoke plumes by satellite imagery is a comprehensive research topic that can be used to better monitor activity and emissions from the energy and industrial sectors. In this study, we propose a machine learning methodology based on the extraction of relevant features from Sentinel-2 images to perform industrial smoke plume detection. This computer vision problem is modeled as an image classification task based on the presence or absence of plumes from previously identified sources. A dataset of nearly 17,000 hand-labeled images of smoke plumes for activity classification has been compiled to train and evaluate our detection models. The final Gradient Boosting model only uses the 3 RGB bands of Sentinel-2 and after a post-processing step reaches an accuracy of 95%.
Promoting SAR-Based Urban Flood Mapping with Adversarial Generative Network and Out of Distribution Detection

One remaining challenge in synthetic aperture radar (SAR) based flood mapping is the flood detection over urban areas, particularly around buildings. To address it, we propose an unsupervised change detection method that decomposes SAR images into noise and ground condition information, and to detect flood induced changes using out-of-distribution detection. The proposed method is based on adversarial generative learning, specifically using the multimodal unsupervised image translation model. It involves training the model by reconstructing SAR images under different combinations of noise models and ground conditions. The method is tested using Sentinel-1 dual-polarization and ancillary data in Hurricane Harvey 2017 over the urban area of Houston. Subsequent research endeavors will be dedicated to enhancing the method's stability, conducting additional validation, and extending its application to diverse SAR sensors.
Methodology to estimate loss of semi-natural grasslands due to shrub encroachment in Denmark from 1965 to 2010–a sample-based study using dot grids on aerial photographs

Due to abandonment of agricultural management, shrub encroachment is thought to be a major threat to semi-natural grasslands in Denmark. However, no long-term, retrospective studies have been conducted to clarify the actual extent of this problem. To estimate the loss of semi-natural grassland caused by shrub encroachment, we analysed a sample of 1383 hectares of semi-natural grassland within a 5000 km2 study area located in Denmark. Applying a dot-grid-based interpretation of aerial photographs together with selected thematic data, we estimated change from semi-natural grassland to shrub for the periods 1965–1987 and 1987–2010. Our results show that 9.4% of the semi-natural grassland from 1965 had changed into shrub by 2010. The speed of loss was constant with 5% in each of the periods 1965–1987 and 1987–2010. We conclude that shrub encroachment is a major threat to the remaining semi-natural grasslands. Furthermore, we recommend our sample- and dot-grid method as a quick and robust approach for assessing retrospective land cover changes, e.g. semi-natural grasslands.
A Multispectral-Infrared Object Detection Method Based on Cross-Modality Image Feature Filtering Fusion

In this paper, a multi-spectral infrared target detection method is established based on image feature filtering fusion. Multispectral(MS) images and infrared(INF) images are used for fusion. Considering the advantages of the multi-spectral image and infrared image, use the feature filtering method to extract the features of the two kinds of images, and then carry out the fusion and subsequent detection. We use FLIR datasets to evaluate the proposed method, and the accuracy has been improved. The comparison results show that the proposed multispectral-infrared object detection method based on cross-modality image feature filtering fusion over the conventional detection methods.
Improved SAR Image Generation with Double Top-K Training Method on Auxiliary Classifier GAN

Synthetic aperture radar (SAR) is a critical imaging technique that is widely used for civil and military tasks, as it is featured with an excellent ability for high resolution imaging. However, due to the severe shortage of SAR images, the performance of automatic target recognition (ATR) is greatly sabotaged. Generative adversarial network (GAN) is often applied for data augmentation of small-sized dataset. In this paper, based on auxiliary classifier GAN (ACGAN) and top-k training technique, we propose double top-k training, which implements a modification during training without any further adjustment on model architecture. The proposed method is to enforce generator to only optimize on generated images that perform well in both discriminator and auxiliary classifier, and discard images of poor performance. We evaluate the generated images via recognition on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) score indicate better generation results of the proposed method compared with original ACGAN.
Use of terrain variables for mapping gully erosion susceptibility in Lebanon

This paper predicts the geographic distribution and size of gullies across central Lebanon using a geographic information system (GIS) and terrain analysis. Eleven primary (elevation; upslope contributing area; aspect; slope; plan, profile and tangential curvature; flow direction; flow width; flow path length; rate of change of specific catchment area along the direction of flow) and three secondary (steady‐state; quasi‐dynamic topographic wetness; sediment transport capacity) topographic variables were generated and used along with digital data collected from other sources (soil, geology) to statistically explain gully erosion field measurements. Three tree‐based regression models were developed using (1) all variables, (2) primary topographic variables only and (3) different pairs of variables. The best regression tree model combined the steady‐state topographic wetness and sediment transport capacity indices and explained 80% of the variability in field gully measurements. This model proved to be simple, quick, realistic and practical, and it can be applied to other areas of the Mediterranean region with similar environmental conditions, thereby providing a tool to help with the implementation of plans for soil conservation and sustainable management. Copyright © 2007 John Wiley & Sons, Ltd.
Super-Resolution of GOES ABI Data Based on VIIRS Measurements and Auxiliary Environmental Information

Due to the limited spatial or temporal resolution, current satellite-based products from VIIRS (Visible Infrared Imaging Radiometer Suite), MODIS (Moderate Resolution Imaging Spectroradiometer), and GOES ABI (Geostationary Operational Environmental Satellite Advanced Baseline Imager) are often inadequate for real-time applications such as wildfire edge detection and wildfire suppression. The overarching goal of this research is to employ deep learning to create high spatial and temporal resolution fire products needed by fire managers for real-time wildfire monitoring and perimeter mapping. To create a super-resolution GOES fire product, this paper will adapt an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to train the 2-km GOES ABI data (Channels 2, 7, 14, and 15 in particular). The concurrent overlapping higher spatial resolution VIIRS data (Channels I1-I5, 375 m resolution) from Suomi NPP, NOAA-20, and NOAA-21 (nominal temporal resolution is 12 h or less depending on the latitude) will be used as target labels in training the deep learning super-resolution model for downscaling GOES ABI data. The adapted ESRGAN model can then be applied to the real-time high temporal resolution GOES ABI data to produce high spatial resolution, i.e., 375-m, fire products.
Iterative Autoencoder Coupling with Constrained Energy Minimization for Hyperspectral Target Detection

The common purpose of HTD is to distinguish the target from the BKG in HSIs using either fully-known or partly-known prior knowledge. Traditional HTD methods generally rely on distance or similarity measurement and statistical techniques to identify discriminating characteristics. Inspired by the simplicity and efficiency of AE in feature extraction and dimension reduction, and considering the susceptibility of limited prior knowledge to spectral variability, a novel HTD method based on hidden dimension-restricted iterative AE guided by posterior knowledge is proposed. The latent space mapping from the original data is constrained with the concept of VD by the algorithm of NWHFC, which allows the reconstruction more tendentious to target and purified BKG. The posterior knowledge, obtained by CEM on a subpixel level, is iteratively added to the input of AE to improve the diversity of the target and BKG collaboratively. The results show that the BKG is effectively suppressed and the target is prominently highlighted layer by layer. Comprehensive experiments and analysis conducted on the public dataset demonstrate that the proposed method is structurally simple yet efficient, outperforming five state-of-the-art algorithms rank by 3-D ROC. The key indicator, target detectability, exceeds the comparison methods by 51.1%.
Application of Semantic Image Segmentation for Efficient UAS-SfM Photogrammetry Mapping

Structure from Motion (SfM) photogrammetry in combination with the Multi-View Stereo (MVS) technique (SfM-MVS ) is a low-cost and effective tool to reconstruct the 3D structure of the real-world environments and objects using a set of overlapping images. This is approach is often implemented using imagery collected by a small uncrewed aircraft system (UAS) equipped with a high-resolution digital camera (referred to as UAS-SfM). The generated dense point cloud is typically considered the ultimate product of the workflow in almost all commercial and open-source software packages. Although, currently, UAS photogrammetry is being widely used in many remote sensing (RS) applications, there are some challenges regarding the efficiency, quality, and computational complexity in deriving some geospatial products in certain environments, especially where frequent mapping of the area is required to monitor certain changes in the surveyed environment. Efficient mapping of the coastal environments can be challenging due to the presence of water or other moving objects in almost all UAS images. It may also be challenging due to the need for accurate classified 2D and 3D land cover maps or digital terrain models (DTMs) which are required to examine the rate of changes in those environments. Providing those products through the SfM-MVS photogrammetry requires processing a large number of UAS images as well as performing expensive and complicated processing on 2D images and/or 3D point clouds which leads to an exponential increase in the computational complexity and cost in coastal mapping. This study proposes a novel approach that integrates semantic UAS image segmentation into the SfM-MVS photogrammetry workflow to address the aforementioned challenges in UAS photogrammetry mapping in coastal environments. The proposed approach leads to a higher level of automation in generating geospatial products with efficient exploitation of the available computation resources for SfM-MVS computations. The proposed approach exploits state-of-the-art deep learning (DL) for semantic UAS image segmentation to predict land cover image masks. Image masks predicted for moving objects, present in the study area, e.g., water bodies, are used to automatically exclude those areas from SfM-MVS computation. That leads to a higher computation efficiency and an lower noise in the reconstructed model. The proposed approach is then expanded to predict image mask through the DL-based image segmentation model for an automatic generation of the classified point cloud simultaneous with the SfM-MVS processing for reconstruction.