Miracle-dz/UniFAR-FTUs huggingface dataset:
Deforestation Monitoring Using Sentinel-L SAR Images in Humid Tropical Areas

Tropical forests are vulnerable to deforestation and various monitoring techniques have been developed based on remotely sensed data to map deforestation, but are facing multiple problems in the tropical areas. For instance, the techniques based optical data, which are widely used to monitor deforestation, face severe limitations in the humid tropical forest due to high cloud cover. Sentinel-l C-SAR dense time series can be used for a temporally more accurate monitoring. In this study, a change detection algorithm commonly used in the financial domain, the Cumulative Sum (CuSum) algorithm, was modified to be applied on time-series of Sentinel-l images in a forest concession of Democratic Republic of Congo (DRC) near Kisangani. The validation was made through the visual interpretation of PlanetScope OrthoScene images as in-situ data were missing. The results show a precision up to 0.75, an accuracy up to 0.95 and a kappa coefficient up to 0.40 for clear cut detection. The algorithm is able to detect forest degradation activities before the clear cuts.
Whats Next in AI4EO?

Surfing the wave of artificial intelligence (AI) and thanks to open Earth observation (EO) data, there has been a buzz in the community for some time about exploiting AI methods like deep learning (DL) to solve problems in EO that have been solved before (“we can also do it with deep learning”) yet to achieve superior performances. There is no doubt that AI/ML/DL can do a great job in classification and detection tasks, which are only small fractions of EO problems. Is this all? Are you getting bored by applying AI methods in EO and playing with toy data? As the introduction talk of the invited session “AI4EO: Reasoning, Uncertainty and Ethics”, two important RD lines are discussed, namely developing cutting-edge next generation AI4EO methods, and generating large scale and representative benchmarks enabling these developments. With all the advancements in EO and AI technology, so far non-existing geo-information has and will become openly available that may raise ethical issues, like mis-use, bias or stigmatization. Hence, ethical accompanying AI4EO research is becoming increasingly important.
Tree Species Mapping in Tropical Forests Using Hyperspectral Remote Sensing and Machine Learning

Tree species-level information is essential for effective forest management, conservation, policy development, and utilization. The availability of hyperspectral data creates new possibilities for species mapping. This study mapped tropical tree species in Shimoga, Karnataka, India by using the Airborne Visible and Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) hyperspectral data. Species mapping was performed by modifying the Random forest (RF) classifier using Principal Component Analysis (PCA) to transform the variables at each node into another space. The performance of PCA-based Rotation Random Forest (RoRF), was then compared with RF and Support Vector Machine (SVM), where RoRF outperformed both of them. A total of 20 tropical tree species were classified, highlighting the potential of the AVIRIS-NG data for species-level classification.
Sar Tomography Based on Reweighted Atomic Norm Minimization

Synthetic aperture radar tomography (TomoSAR) obtains three-dimensional reflectivity of scenes by extending the aperture principle into the elevation direction, and thus solves the layover problem. Because the scatters are sparse along elevation, compressed sensing methods are introduced into TomoSAR. However, conventional compressed sensing methods based on discretized grids suffer from off-grid effect. As a method with continuous dictionary, reweighted atomic norm minimization (RANM) can avoid this problem completely. In this paper, we propose an algorithm for SAR tomographic imaging based on reweighted atomic norm minimization to eliminate off-grid effect and locate scatters more accurately. The performance of the proposed method is verified by simulated data.
Remote Sensing Imagery Scene Classification Based on Spiking Neural Network

In order to overcome the large computational cost of deep neural networks (DNNs), spiking neural networks (SNNs) have been proposed, which are more biologically reasonable. It has the potential to achieve energy efficiency while maintaining performance comparable to DNNs. Although SNNs have achieved good results on the MNIST and CIFAR10 data sets, its potential in remote sensing has not been studied and explored. This paper adopts the idea of converting a trained DNN into an SNN, and proposes a multi-bit-based SNN, and introduces channel normalization (channel-norm) to replace the previous layer normalization to achieve remote sensing imagery scene classification tasks. By achieving multi-bit spiking and channel-norm in the way of DNN conversion to SNN, the SNN proposed in this paper achieves lossless conversion on the UC Merced data set and WHU-RS data set.
Lung Inflammatory Classification of Diseases using X-ray Images

Recently, studies in inflammatory diseases categorization become of interest in the research community, especially with the sudden outbreak of the Covid-19 virus. Transfer learning proved to be the state-of-the-art when it comes to image classification problems, or related tasks. These methods achieve good results in this type of applications. Lately, this pre-trained embedding became even popular due to X-ray related studies for early Covid-19 diagnosis. In this study, we investigate the X-ray image classification problem using the transfer learning method. We fine-tuned and trained our model using pre-trained models such as AlexNet, VGG16, DenseNet etc, and a baseline deep neural network. We then evaluated this model in terms of classification evaluation metrics. The study shows that DenseNet achieves high accuracy compared to the other pre-trained and baseline CNN models.
Walking-in-Place Foot Interface for Locomotion Control and Telepresence of Humanoid Robots

We present a foot-tapping walking-in-place type locomotion interface and algorithm to generate high-level movement patterns for humanoid remotely controlled robots. Foot tapping motions acting on the platform are used as movement commands to remotely control locomotion of a humanoid robot. We describe two separate motion mapping algorithms suitable for wheeled and bipedal humanoid locomotion. Our interface enables remote locomotion control of humanoid robots with the help of a seated and hands-free interface and enables the use of both handheld or desktop-based interfaces for manipulation tasks. An experimental study with eight participants controlling walking speed of a virtual robot was conducted to explore if the participants could maintain distance (1-3m range) to a reference target (leading robot) moving at different speeds. All participants were able to use the proposed interface to track the leading robot efficiently for walking speeds of less than 1 m/s and an average tracking error was 0.47 m. We discuss the results of the study along with the NASA TLX and system usability surveys.
A Grid-Structured Model of Tubular Reactors

We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which describe the dynamics of the chemical process inside a tubular reactor. The proposed model may be entirely based on the known form of the partial differential equations or it may contain generic machine learning components such as multi-layer perceptrons. We show that the proposed model can be trained using limited amounts of data to describe the state of a fixed-bed catalytic reactor. The trained model can reconstruct unmeasured states such as the catalyst activity using the measurements of inlet concentrations and temperatures along the reactor.
Optimization of Antenna Rotation Speed and Super-Resolution Imaging Based on Split Bregman Algorithm for circular Scan ISAR Systems

Regarding the imaging of circular scan inverse synthetic aperture radar (ISAR), the rapid circular scan of the antenna expands the beam coverage area. However, how to determine the rotation speed of the antenna is still a difficult problem. This paper proposes an antenna speed optimization model to quantify the antenna rotation speed. Under the constraints of the beam coverage area per second and the accumulated signal-to-noise ratio (SNR), the value of azimuth resolution is minimized. In addition, to improve the resolution of the image, the split Bregman algorithm is also introduced. This algorithm solves the problem that the azimuth resolution is still lower than the traditional ISAR after the antenna rotation speed is optimized. By optimizing the antenna rotation speed and processing data by the split Bregman algorithm, a super-resolution ISAR image is obtained. The simulation results verify the effectiveness of the method.
Web-Based Monitoring of Boro Rice Production Using Improvised NDVI Threshold of MODIS MOD13Q1 and MYD13Q1 Images

In this work, we used MODIS (Moderate Resolution Imaging Spectroradiometer) data MOD13Q1 and MYD13Q1 images to improvise the existing rice model[1] where MODIS NDVI threshold value was used for calculating Boro rice areas in the Sylhet Haor region of Bangladesh. A new algorithm has been proposed here for finding the improvised NDVI threshold value with the Brute Force technique. In total 126 NDVI images have been accumulated and processed in this work. First, a Boro rice model implemented for Sylhet Haor region and ground truth data has been used from Bangladesh Bureau of Statistics (BBS) database for evaluation. The improvised model's accuracy is compared with the previous model. The proposed model shows better accuracy with 2.5% improvement in the Boro rice area prediction for Sylhet Haor region. A new rice model using these improvised thresholds are extended for the whole regions of Bangladesh. The results show better accuracy for all regions. The dataset is used to develop a satellite-based Boro rice area monitoring system. Moreover, it may help in forecasting the Boro rice yield.
Global Daily 500-M Evapotranspiration Estimation Over Vegetated Areas Using Rnadom Forest from MODIS Data

Evapotranspiration (ET) is an important variable in hydrological cycle and widely used in the study of water management and climate change. This paper developed a Random Forest (RF) model for global daily ET estimation over vegetated areas with 500 m spatial resolution using merely MODIS data. 255 in-situ sites from AmeriFlux network, FLUXNET Network and National Tibetan Plateau Data Center (TPDC) of China have been used to evaluate the RF model. Results indicated that the RF model-estimated global daily ET exhibited reasonable accuracy compared to the in-situ observations using MODIS datasets as inputs, with root mean square error (RMSE) between 0.52-0.97 mm/d over six different land-cover types representing forest, shrubland, cropland, savanna, grassland and wetland. The models generally achieved the best performance in shrubland, grassland and savanna, while provided the worst in wetland.
Snow Water Equivalent Retrieval from COSMO-SkyMed Observations Through Machine Learning Algorithms and Model Simulations

The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMO-SkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model. Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area. The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.
Mapping Microwave Penetration Depths Over Arid Areas

Passive microwave remotely sensed data are increasingly used for the estimation of earth system parameters with application for precision agriculture or meteorology. They are almost impervious to clouds and can be used to provide long term global observations of Earth surface temperatures, soil moisture, etc. However the effective sampling depth of the soil is not constant and this leads to dubious results over arid areas. To account for this phenomenon, in this study we analyse the relationship between the soil geological characteristics such as porosity, sand/clay/silt fractions, and its thermal and electrical properties. Indeed the brightness temperatures measured at different frequencies depend on the effective temperature and emissivity that is a related to the sampling depth and the soil temperature profile. The soil temperature profile can be estimated based on the diffusion equation that relies on the thermal properties of the soil, and the microwave sampling depth is related to the dielectric permittivity of the material. The early results shown here show how we can use soil characteristics maps, and relate them to differences in brightness temperatures at the GMI frequencies between 10 and 89 GHz over the Sahara desert and to different penetration depths at these frequencies.
S-MobileNetV2+SegNet Model and Rapid Identification of Sugarcane

At present, there are many deep learning models in the identification field, but they usually have problems with identification accuracy and slow speed. In this paper, we propose the S-MobileNetV2+SegNet model to identify sugarcane, and improve the SegNet model by replacing the encoder VGG16 model with the reduced structure Mobile-NetV2 model and introducing different ratios of dilated convolution to expand the local receptive field to eliminate the problem of insufficient information capture. Then reduce the network structure of the decoder and the number of convolution kernels to achieve a network with fewer parameters. To verify the accuracy and speed of the S-MobileNetV2+SegNet model in sugarcane identification, it is compared with SegNet, DeepLabV3+, and DeepLab-V3+Mobile-NetV2 models. The experimental results show that the S-MobileNetV2+SegNet model has better results and performance for sugarcane identification.
BRDF Sampling from Hyperspectral Images: A Proof of Concept

Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario.
Combining One-Sided Communications with Task-Based Programming Models

Hybrid programming combining task-based and message-passing models is an increasingly popular technique to exploit multi-core clusters. The Task-Aware MPI (TAMPI) library integrates both models enabling the safe overlap of computation and communication tasks using two-sided MPI communications. Two-sided primitives combine data transfers with implicit synchronizations, but one-sided models usually offer more efficient data transfers decoupling synchronizations. MPI offers four distinct one-sided synchronization modes, while GASPI is a PGAS API providing one-sided operations with remote notifications for fine inter-process synchronizations.In this paper, we study the challenges of integrating MPI and GASPI one-sided operations with the OpenMP and OmpSs-2 tasking models. We propose and implement several extensions to the GASPI and OmpSs-2 programming models, which are leveraged by a new library called Task-Aware GASPI (TAGASPI). The TAGASPI library allows the efficient and safe use of one-sided operations with remote notifications inside tasks. Both TAGASPI and TAMPI transparently manage communications issued by tasks and allow these to overlap with computation tasks naturally, following a data-flow model. These libraries are complementary and can be mixed in the same application.Our experience porting several mini-apps to this hybrid model shows that TAGASPI helps leverage one-sided communications with similar complexity to pure and hybrid two-sided MPI approaches. We show that our hybrid one-sided approach outperforms the pure MPI strategies, but it also surpasses the TAMPI’s performance when stressing communication phases, e.g., increasing the communication parallelism and reducing the communication tasks’ sizes.
An FPGA-Based Fully Pipelined Bilateral Grid for Real-Time Image Denoising

The bilateral filter (BF) is widely used in image processing because it can perform denoising while preserving edges. It has disadvantages in that it is nonlinear, and its computational complexity and hardware resources are directly proportional to its window size. Thus far, several approximation methods and hardware implementations have been proposed to solve these problems. However, processing large-scale and high-resolution images in real time under severe hardware resource constraints remains a challenge. This paper proposes a real-time image denoising system that uses an FPGA based on the bilateral grid (BG). In the BG, a 2D image consisting of x- and y-axes is projected onto a 3D space called a “grid,” which consists of axes that correlate to the x-component, y-component, and intensity value of the input image. This grid is then blurred using the Gaussian filter, and the output image is generated by interpolating the grid. Although it is possible to change the window size in the BF, it is impossible to change it on the input image in the BG. This makes it difficult to associate the BG with the BF and to obtain the property of suppressing the increase in hardware resources when the window radius is enlarged. This study demonstrates that a BG with a variable-sized window can be realized by introducing the window radius parameter wherein the window radius on the grid is always 1. We then implement this BG on an FPGA in a fully pipelined manner. Further, we verify that our design suppresses the increase in hardware resources even when the window size is enlarged and outperforms the existing designs in terms of computation speed and hardware resources.
An FPGA-based Stochastic SAT Solver Leveraging Inter-Variable Dependencies

Control rules of Internet of Things (IoT) and embedded systems are often representable in Satisfiability (SAT) problem. This paper proposes a solution search acceleration technique applicable to hardware SAT solvers by leveraging inter-variable dependencies inherited in SAT-encoded realistic applications. We applied our technique on the FPGA implementation of AmoebaSAT, a state-of-the-art stochastic SAT algorithm. Our evaluation on SAT instances from two different IoT-oriented domains demonstrated a significant speedup in solution search against a state-of-the-art hardware SAT solver while incurring almost no effect on hardware implementation compared with the original (dependency-unaware) implementation.
Classification and Generation of Earth Observation Images Using a Joint Energy-Based Model

Deep learning has changed unbelievably the processing of Earth Observation tasks such as land cover mapping or image registration. Yet, today new models are needed to push further the revolution and enable new possibilities. We propose a new framework for generative modelling of Earth Observation images. It learns an energy-based model to estimate the underlying distribution of the data while jointly training a deep neural network for classification. On the varied image types of the EuroSAT benchmark, we show this model obtains classification results on par with state-of-the-art and moreover allows us to tackle a wide range of high-potential applications: image synthesis, out-of-distribution testing for domain adaptation, and image completion or denoising.
Building Footprint Extraction Using Deep Learning Semantic Segmentation Techniques: Experiments and Results

Deep Learning Semantic Segmentation are techniques based on convolutional neural networks that have been employed in several research applications in Computer Sciences. In recent years, Remote Sensing researchers have used such techniques and achieved remarkable results. In this research paper we employ semantic segmentation techniques to extract building footprints from remote sensing imagery. We use a custom dataset called Brazilian Army Geographic Service Building Dataset to train several neural network architectures such as U-Net and FPN, combined with the following backbones: EfficientNet-B0, EfficientNet-B1, SE-ResNeXt-101 and ResNet-152. To train the mentioned structures, a framework based on Keras and Tensorflow called segmentation_models_trainer (https://github.com/phborba/segmentation_models_trainer) was used. Transfer Learning from ImageNet weights were used, as well as data augmentation on training images.
Ultrawideband Propagation Experiment Through the Antartica Firn at the Concordia Station in the 0.4 - 2 GHz Frequency Range

In recent years, special attention has been paid, in microwave remote sensing from space, to extend observations to low frequencies (i.e lower than L band). Active mission at P-band (i.e. the ESA's Biomass mission based on SAR) will be launched in 2022 and missions based on low frequency microwave radiometer are proposed to both ESA and NASA. Because of the high penetration depth in ice, these sensors are particularly suitable for investigating parameters of the cryosphere. Nevertheless, in order to properly derive information on such geophysical parameters, there is the need to improve our knowledge of the interaction of electromagnetic waves and the different media. In this paper we present and discuss a first series of results obtained during a propagation measurement campaign carried out at the French–Italian Concordia research station during the summer campaign 2019-2020 in Antarctica in order to investigate on the electromagnetic properties of the Antarctic's firn in the frequency range 0.4-2 GHz. Specific instrumentation has been designed and assembled, essentially consisting of a transmitting unit and a receiving one, to be let down into two boreholes in the firn. Preliminary measurement results are presented and discussed.