Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the Spectral Index to Detect a Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea

Information was obtained on the possibility of detecting oil-in-water emulsions located under the sea based on the modelling of the directional distribution of the radiance field above the water surface. The optical sea model used applies to the southern Baltic Sea, while the oil emulsion model is based on the optical properties of crude oil extracted in this region of the sea. The analyses were carried out while taking into account eight wavelengths in the range 412–676 nm, assuming different thicknesses of the layer contaminated with oil. The most favourable combination of two wavelengths (555/412 nm) for the determination of an index related to the polluted sea area compared to the same index for oil-free water (difference index) was identified, the value of which is indicative of the presence of the oil emulsion in water. Changes in the difference index depending on the viewing direction are shown for almost the entire upper hemisphere (zenith angles from 0° to 80°). The observation directions for which the detection of emulsions should be the most effective are shown.
Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.
Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.
Kinematic ZTD Estimation from Train-Borne Single-Frequency GNSS: Validation and Assimilation

Water vapour is one of the most important parameters utilized for the description of state and evolution of the Earth’s atmosphere. It is the most effective greenhouse gas and shows high variability, both in space and time. Thus, detailed knowledge of its distribution is of immense importance for weather forecasting, and therefore high resolution observations are crucial for accurate precipitation forecasts, especially for the short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of water vapour content from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. The most prominent parameterization of this delay is the Zenith Total Delay (ZTD), which has been studied extensively as a major error term in GNSS positioning. On the other hand, the ZTD has also been proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Based on these facts, the scientific area of GNSS Meteorology has emerged. The present study goes beyond the current status of GNSS Meteorology, showing how reasonable estimates of ZTD can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered from trains of the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. The special nature of the observations yields a number of additional challenges, ranging from appropriate pre-processing and parameter settings in PPP to more sophisticated validation and assimilation methodologies . The treatment of the ionosphere for SF-GNSS data represents one of the major challenges of this study. Two test cases (train travels) were processed using different strategies and validated using ZTD calculated from ERA5 reanalysis data. The validation results indicate a good overall agreement between the GNSS-ZTD solutions and ERA5-derived ZTD, although substantial variability between solutions was still observed for specific sections of the test tracks. The bias and standard deviation values ranged between 1 mm and 8 cm, heavily depending on the utilized processing strategy and investigated train route. Finally, initial experiments for the assimilation of GNSS-ZTD estimates into a NWP model were conducted, and the results showed observation acceptance rates of 30–100% largely depending on the test case and processing strategy.
SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.
Long-Term Projection of Water Cycle Changes over China Using RegCM

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.
A Cyclic Information-Interaction Model for Remote Sensing Image Segmentation

Object detection and segmentation have recently shown encouraging results toward image analysis and interpretation due to their promising applications in remote sensing image fusion field. Although numerous methods have been proposed, implementing effective and efficient object detection is still very challenging for now, especially for the limitation of single modal data. The use of a single modal data is not always enough to reach proper spectral and spatial resolutions. The rapid expansion in the number and the availability of multi-source data causes new challenges for their effective and efficient processing. In this paper, we propose an effective feature information–interaction visual attention model for multimodal data segmentation and enhancement, which utilizes channel information to weight self-attentive feature maps of different sources, completing extraction, fusion, and enhancement of global semantic features with local contextual information of the object. Additionally, we further propose an adaptively cyclic feature information–interaction model, which adopts branch prediction to decide the number of visual perceptions, accomplishing adaptive fusion of global semantic features and local fine-grained information. Numerous experiments on several benchmarks show that the proposed approach can achieve significant improvements over baseline model.
A CPK-Based Identity Authentication Scheme for IoT

As the power Internet of Things (IoT) enters the security construction stage, the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and practicality. The existing public key infrastructure (PKI)-based security authentication scheme is currently difficult to apply in many terminals in IoT. Its key distribution and management costs are high, which hinders the development of power IoT security construction. Combined Public Key (CPK) technology uses a small number of seeds to generate unlimited public keys. It is very suitable for identity authentication in the power Internet of Things. In this paper, we propose a novel identity authentication scheme for power IoT. The scheme combines the physical unclonable function (PUF) with improved CPK technology to achieve mutual identity authentication between power IoT terminals and servers. The proposed scheme does not require third-party authentication and improves the security of identity authentication for power IoT. Moreover, the scheme reduces the resource consumption of power IoT devices. The improved CPK algorithm solves the key collision problem, and the third party only needs to save the private key and the public key matrix. Experimental results show that the amount of storage resources occupied in our scheme is small. The proposed scheme is more suitable for the power IoT.
Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.
Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R

Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE.
A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.
Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.
Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.