Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Extraction of Wind Turbine all over the China Based on Domestic Satellite Remote Sensing Data

As wind energy is a kind of clean energy, wind power generation industry has developed rapidly in recent years. Therefore, it is of great significance for the power industry and relevant regulatory authorities to obtain the number and spatial distribution of wind turbine quickly based on remote sensing images. Despite of the rapid development of remote sensing information extraction, the traditional algorithms based on remote sensing classification and recognition cannot meet the requirements of wind turbine extraction on a large-scale area due to the insufficient feature expression of remote sensing images. In this paper, an iterative training method based on the Faster R-CNN (the target extract framework of deep learning) which uses the positive and negative sample set obtained from the first target detection result of ResNet classification for training again has been proposed, which has got the 97.5 percent accuracy for wind turbine target extraction on the 2m spatial resolution map of domestic satellite remote sensing image on the whole china.
Visibility Analysis of Glaciers on Steep Slopes in the European ALPS Using Terrasar-X/PAZ Data

This paper focuses on the visibility of glacier surfaces in the Mont Blanc Massif (Western European Alps) on TerraSAR-X/PAZ images and the identification of geometric distortions (GDs) based on the SAR acquisition geometry. Small glaciers which exist in complex topographies like steep slopes are most prone to GDs. We built a visibility map for both ascending/descending orbits by utilizing previously documented algorithms like the R-Index (RI) and the Layover Shadow (LS) simulations, combined with an analysis of the angle between the steepest slope direction (SSD) and line of sight (LOS) vectors. The visibility map allows us to identify glaciers on steeper slopes which should be considered for further analysis using TerraSAR-X/PAZ images.
New Data, Integrated Methods and Multiple Applications: A Review of Urban Studies based on Street View Images

In order to objectively grasp the research status and development trend in the field of urban research based on street view images, the advanced retrieval method was used to retrieve the core literature in the field of urban research based on street view images from 2005 to 2021 in the database of China national knowledge infrastructure(CNKI). We use bibliometrics to conduct statistical analysis on the publication year and the number of papers published in this field, and use software to explore the key words and development stages of research literature. The main research findings: $\bigcirc\! \! \! \! 1$ Street view images include Google Street View (GSV), Baidu Street View (BSV) and Tencent Street View (TSV). Compared with other city research data, street view images have the advantages of large volume, availability, complete information and full coverage. $\bigcirc\! \! \! \! 2$ The development of big data and artificial intelligence technology has promoted the surge of street view images in urban research. $\bigcirc\! \! \! \! 3$ Jrban research based on street view images tend to be diversified and hot, from the focus on street planning, street design, street renovation to the quality of street space. $\bigcirc\! \! \! \! 4$ The topics of urban research based on street view images mainly involve six aspects: street landscape, streets, built environment, street design, deep learning, and street view images.
A HOG Feature Fusion Method to Improve CNN-Based SAR Ship Classification Accuracy

Ship classification in Synthetic Aperture Radar (SAR) images is a fundamental and important step in ocean surveillance. Recently, with the rise of Deep Learning (DL), Convolutional Neural Network (CNN)-based SAR ship classifiers have made a huge accuracy progress compared with traditional hand-crafted feature methods. However, existing most CNN-based classification models uncritically abandon traditional mature hand-crafted features, but excessively rely on abstract features extracted by deep networks, which possibly brings great challenges in further improving classification performance. Therefore, to address this problem, this paper proposes a Histogram of Oriented Gradient (HOG) feature fusion method to improve CNN-based SAR ship classification accuracy. Experimental results on the open SAR ship classification dataset OpenSARShip reveal that when combining HOG feature fusion, the classification accuracy can achieve a 7.64% improvement.
Evaluation and Mitigation of Rain Effect on Wave Direction Estimation from X-Band Marine Radar Data

In this paper, the accuracy of wave direction estimation from X-band marine radar images under different rain rates is analyzed, and a simple sub-image selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple sub-images, and the sub-images with relatively clear wave signatures are identified based on random-forest-based classification model. Then, wave direction is estimated by performing Radon transform on each valid sub-image. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameters estimation accuracy and validate the proposed method. Experimental results show that the proposed sub-image selection scheme improves the estimation accuracy of wave direction under different rain rates, with reductions of RMSEs by 6.9°, 6.0°, 4.9°, and 1.0° for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively.
Comparison of Biomass Acquisition Modes for the Characterization of Forests

This aims to compare the performance of different observations modes of the future BIOMASS mission for the characterization of tropical forests. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and computes estimates using real data acquired at P band in the frame of the TropiSAR campaign. Results show that the best performance, reached in the Polarimetric and Tomographic mode, degrades slightly in the single-polarization case and significantly when the vertical tomographic resolution is reduced. Nevertheless, it is shown that in this latter case, the use of priors estimated in the high-resolution tomographic phase and consisting on the estimate of the wave extinction, leads to a spectacular improvement of the performance. This results reveals important in the frame of the BIOMASS missions which plans to phases with different acquisition configurations.
A Fully Automated Approach to Extract Landcover Features from Landsat Imageries

The land cover maps playa significant role in planning, managing, and monitoring our environment changes at different levels. Land cover denotes the surface cover on the ground, comprised of water, built up, bare land, vegetation, or other. This paper highlights a method to automatically extract the water, built-up, and vegetation landcover datasets using Index Extracted Water Index (IEWI), Index Extracted Built-up Index (IEBI), and Index Extracted Vegetation Index (IEVI), respectively. The results are then validated with ground truth points from the field survey. The advantages of this novel method are rapid extraction of landcover features, cost-effectiveness, reduction in misclassification, and comparable classification accuracy with superior methods of land cover classification. These findings suggest that this index -based, fully automated feature extraction approach can be quickly applied for various remote sensing applications with better accuracy.
Channel-Based Attention for Land Cover Classification using Sentinel-2 Time Series

Deep Neural Networks (DNNs) are getting increasing attention to deal with land cover classification relying on Satellite Image Time Series (SITS). Though high performances can be achieved, the rationale of a prediction yielded by a DNN often remains unclear. An architecture expressing predictions with respect to input channels is thus proposed in this paper. It relies on convolutional layers and an attention mechanism weighting the importance of each channel in the final classification decision. The correlation between channels is taken into account to set up shared kernels and lower model complexity. Experiments based on Sentinel-2 SITS show promising results.
Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) they assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice; (ii) they have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.
Studying Frozen Ground Dynamics by Using GNSS Interferometric Reflectometry: Achievements and Potential Synergy with Insar

Permafrost has been warming and thawing in the last decades in response to global warming. Monitoring the changes of the active layer and near-surface permafrost is crucial for revealing their dynamics. GNSS interferometric reflectometry (GNSS-IR) is a relatively new technique for studying frozen ground dynamics. In this paper, we summarized the major achievements in applying GNSS-IR to the signal-to-noise ratio data recorded by the continuous GNSS sites in permafrost areas in the Arctic and Qinghai-Tibet Plateau. We identified 23 sites in the Arctic permafrost regions which are suitable for GNSS-IR studies. We used the GNSS-IR-retrieved surface elevation changes to investigate the multi-year, interannual, and seasonal changes of the frozen ground. We improved the commonly-used GNSS-IR algorithm for estimating soil moisture in permafrost areas by mitigating the bias introduced by seasonal surface deformation. We also analogized GNSS-IR to InSAR, and advocated for a synergy between their observations to obtain improved, quantitative, and insightful measurements of the ever-warming frozen ground.
Spaceborne Earth Observation for Offshore Wind Energy Applications

Earth Observation, especially satellite-based remote sensing, with more than three decades of maturity, is being used for a wide suite of applications ranging from weather forecasting to monitoring land-use changes and natural disasters such as fires. The increase in installed and planned offshore wind energy capacity triggers new demands for observations of the remote oceanic environment. Satellite-derived ocean surface winds, with more than 20 years of near-seamless temporal coverage, offer a unique opportunity to characterize the offshore environment prior to deciding the installation of in situ measuring stations and high-resolution simulations. This paper demonstrates and summarises the applicability of satellite-based ocean surface winds for a range of needs stemming from the offshore wind energy sector.
A Novel Unambiguous Imaging Method for Geosynchronous Spaceborne-Airborne Bistatic SAR

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) consists of GEO transmitter and airborne receiver, which has extensive application prospects in both civilian and military fields. However, the Doppler bandwidth in this configuration exceeds the transmitted pulse repeat frequency (PRF), which leads to sub-Nyquist sampling. To solve this problem, multi-receiving technique has been applied to the receiver to increase the equivalent sampling rate and reconstruct unambiguous image. In this paper, we take a different approach to recover the unambiguous image for GEO-SA-BiSAR with less receiving channels. The GEO-SA-BiSAR imaging problem is modeled as a problem of joint sparse and low-rank matrices recovery. To reduce the computing time of the traditional recovery method, a modified alternating direction method of multipliers (M-ADMM) is proposed, where the computing and storage of the computational expensive observation matrix is avoided. Simulation results reveal that the proposed method can recover the original image scene with high computational efficiency.
Corner-Guided Building Polygon Construction from Aerial Images using Deep Multitask Learning

Automatic extraction of building is of great importance in many remote sensing applications. Nevertheless, existed deep-learning based methods tend to generate building footprints with fuzzy boundaries and the geometric details could not be well-preserved. In this paper, we propose a bottom-up deep learning based approach for automatic building polygon reconstruction. Specifically, an end-to-end learnable model, multitask DeepLabv3+, is designed for predicting building footprint, corner heatmap and edge heatmap. Then, A geometric-based strategy is utilized to reconstruct the extracted corners as a closed polygon based on the edge heatmap. By doing so, our method is able to generate polygons much closer to real buildings with fine-grained boundaries and well-preserved geometric details. Compared to state-of-the-art models, experiments on high-resolution aerials imagery dataset demonstrate that our proposed approach achieves better performance in quantitative and qualitative results.
Robust Balancing Control of a Spring-legged Robot based on a High-order Sliding Mode Observer

This paper presents a simulation study of the balancing problem for a monopod robot in which the lower body (the leg) has been modified to include a passively spring-loaded prismatic joint. Such a mechanism can move by hopping but can also stand and balance on a single point. We aim to investigate the extent to which a balance controller can deal with the large values and rapid changes in the spring-damper forces, while controlling the absolute positions and orientations of its parts and balancing on one leg. It can be shown that a good performance is achieved if the spring-loaded joint is instrumented and calibrated so that its position and velocity, as well as the stiffness and damping coefficients, are considered when calculating the controller state variables. We also demonstrate the effectiveness of the balance controller by adding a high-order sliding mode (HOSM) observer based on the finite-time algorithm for robust parameter estimation of the stiffness and damping coefficients. The stability analysis and convergence proofs are presented based on the Lyapunov stability theory. Numerical simulations are included to illustrate the performance and feasibility of the proposed methodology.
The Role of the Biomass Mission in Carbon Cycle Science and Politics

The European Space Agency's 7th Earth Explorer mission, BIOMASS, was proposed in 2005 and since then there have been major changes in the scientific and political conditions within which it was conceived, and also within the technology and methodology both of the mission itself and of the complementary systems that BIOMASS will work with. This paper describes some of the most important recent developments in this overall environment of the mission, and how they affect the likely use of data from BIOMASS mission after its launch in 2023 and over its nominal five-year lifetime.
Performance Assessment of the FSRETC Algorithm for the Estimation of the Frequency Sweep Rate in Airborne FMCW SAR Systems

In recent years, airborne Synthetic Aperture Radar (SAR) systems based on the Frequency Modulated Continuous Wave (FMCW) technology are becoming very popular thanks to their simple architecture and low cost. When FMCW SAR data are processed, it is essential to accurately know the electronic parameters of the system. In particular, the use of an incorrect value of the Frequency Sweep Rate (FSR) introduces geometric distortions in the focused images. In this work we provide the assessment of the performances of the FSRETC algorithm, recently proposed with the aim of estimating the correct FSR from the radar data. The presented analysis is conducted through the use of real data acquired by an airborne FMCW SAR system.
AI Mapping Risks to Wildlife in Tanzania: Rapid scanning aerial images to flag the changing frontier of human-wildlife proximity

Mapping wildlife and human distributions are critical to mitigating human and wildlife conflict and conserving wildlife, habitat, and human well-being. Aerial surveys are a critical tool, but new methods for these surveys generate massive image archives. The act of scanning these images is time-consuming and difficult, leading to significant delays in converting data into action. To address this Development Seed and the Tanzania Wildlife Research Institute (TAWIRI), developed an AI-assisted methodology, specifically, Tensorflow based image classification and object detection methods that significantly increase the speed of spotting and counting wildlife, human activities, and livestock after aerial surveys. Our AI-assisted survey method to map wildlife and human distributions and to detect potential conflict areas between wildlife and the human-associated activity for Tanzania. Once the training data quality and model performance of AI-assisted workflow mature and stabilize, we foresee the hours spent on getting accurate human-wildlife proximity maps would only take 19% of current human manual workflow, and potentially reduce the cost of identifying and counting objects over 81,000 aerial images from $20,000 to less than $5,000.
Estimation of an Aerosol Plume Mass Balance from Plume Property Retrievals Computed by the Combination of the Sentinel-2 Data with Hyperspectral Data Coupled with an Optimal Estimation Method

Particulate emissions from industrial sector are a concern for human health and air quality. In this paper we investigate the use of a hyperspectral image to retrieve plume mass concentration and its flow rate from the plume optical properties. The method uses the combination of Sentinel-2 and hyperspectral data to classify, correct and segment surface reflectances below the plume. The retrieval of plume properties is based on a statistical framework called the “Optimal estimation method” (OEM) initialized by using first a sequential method. The method is applied to a particulate matter plume observed by the airborne HYSPEX sensor in February 2016 over a steel plant in the south-east of France. The HYSPEX image was taken in the solar reflective domain between 410 and 2500 nm. The retrieved parameters are the modal radius, the surface reflectance and the aerosol optical thickness (AOT). From optimal estimation method retrievals, the plume particulate matter column mass enhancement are computed for each pixel where the plume is detected. Plume particulate matter column mass enhancement vary between 200 and 1300 mg/pixel and the retrieved mean flow rate per wind speed meter per second is 1.6 g s−1 (m s−1)−1.
Sentinel-3 Image Super-Resolution Using Data Fusion and Convolutional Neural Networks

With the increasing availability of Sentinel-2 (S2) and Sentinel-3 (S3) data, developing higher-level data products becomes a very attractive option to relieve the spatial limitations of the Ocean and Land Colour Instrument (OLCI) of S3. In this context, this paper investigates the suitability of super-resolving operational OLCI products using the Multi-Spectral Instrument (MSI) of S2 as an offline spatial reference. Specifically, the proposed approach assembles a multi -spectral data fusion scheme together with a convolutinal neural network (CNN) mapping function to project the OLCI sensor onto its corresponding spatial reference which is synthetically generated by the OLCI/MSI fusion. In this way, the trained model is able to super-resolve operational OLCI products under demand without the need of using MSI data. The experimental part of the work shows the suitability of the proposed approach in the context of the Copernicus programme.
An Exact Error Analysis of Multi-User RC/MRC Based MIMO-NOMA-VLC System With Imperfect SIC

Visible Light Communication (VLC) has appeared as a breakthrough technology for beyond 5G networks by delivering a broad license free spectrum. In this paper, multiple input multiple output (MIMO) technique is implemented with non-orthogonal multiple access (NOMA-VLC) system in order to develop a robust error-free high rate network for a multi-user scenario. In particular, a generalized multi-user MIMO-NOMA-VLC system with <inline-formula> <tex-math notation="LaTeX">$M\times N$ </tex-math></inline-formula> line-of-sight (LOS) links per user is proposed which adopts on-off keying (OOK) modulation for data transmission. A novel closed form expression of probability of error for the proposed system is derived considering a practical scenario of imperfect successive interference cancellation (SIC). This paper also presents the detailed analysis of a three user <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> MIMO-NOMA-VLC system and an exact closed form expression of probability of error of this system is derived. The three user <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> MIMO-NOMA-VLC system completely outperforms the single input single output (SISO) NOMA-VLC system with same set of parameters. The error performance of the proposed system enhances as the number of photo detectors (PDs) at each user increases. A three user <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> MIMO-NOMA-VLC system ensures the best performance at the power allocation co-efficient of 0.3. The error performance of the proposed system is investigated for the parameters of interest, i.e., distance between the transmitting LEDs and distance between the receiving PDs at each user terminal. The derived probability of error expressions are verified with the simulation results.