Miracle-dz/UniFAR-FTUs huggingface dataset:
Radar Remote Sensing to Supplement Pipeline Surveillance Programs through Measurements of Surface Deformations and Identification of Geohazard Risks

This research focused on the quantitative assessment of the surface deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried oil and gas pipeline in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset (SBAS) remote sensing analysis. Both techniques showed that the continuous subsidence was prevailing in the kilometer range of 13–70 of pipelines crossing two seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0–13. Although both PS-InSAR and SBAS measurements were highly consistent in deformation patterns and trends along pipelines, they showed differences in the spatial distribution of ground deformation classes and noisiness of produced results. High dispersion of PS-InSAR measurements caused low regression coefficients with SBAS for the entire pipeline kilometer range of 0–70. SBAS showed better performance than PS-InSAR along buried petroleum and gas pipelines in the following aspects: the complete coverage of the measured points, significantly lower dispersion of the results, continuous and realistic measurements and higher accuracy of ground deformation rates against the GPS historical measurements. As a primary factor of ground deformations, the influence of tectonic movements was observed in the wide scale analysis along 70 km long and 10 km wide section of petroleum and gas pipelines; however, the largest subsidence rates were observed in the areas of agricultural activities which accelerate the deformation rates caused by the tectonic processes. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. This means that both techniques outlined in this paper provide a significant improvement for ground deformation monitoring or can significantly contribute to the assessment of geohazards and preventative countermeasures along petroleum and gas pipelines.
Object-Oriented Open-Pit Mine Mapping Using Gaofen-2 Satellite Image and Convolutional Neural Network, for the Yuzhou City, China

Our society’s growing need for mineral resources brings with it the associated risk of degrading our natural environment as well as impacting on neighboring communities. To better manage this risk, especially for open-pit mine (OM) operations, new earth observation tools are required for more accurate baseline mapping and subsequent monitoring. The purpose of this paper is to propose an object-oriented open-pit mine mapping (OOMM) framework from Gaofen-2 (GF-2) high-spatial resolution satellite image (HSRSI), based on convolutional neural networks (CNNs). To better present the different land use categories (LUCs) in the OM area, a minimum heterogeneity criterion-based multi-scale segmentation method was used, while a mean area ratio method was applied to optimize the segmentation scale of each LUC. After image segmentation, three object-feature domains were obtained based on the GF-2 HSRSI: spectral, texture, and geometric features. Then, the gradient boosting decision tree and Pearson correlation coefficient were used as an object feature information reduction (FIR) method to recognize the distinguishing feature that describe open-pit mines (OMs). Finally, the CNN was used by combing the significant features to map the OM. In total, 105 OM sites were extracted from the interpretation of GF-2 HSRSIs and the boundary of each OM was validated by field work and used as inputs to evaluate the open-pit mine mapping (OMM) accuracy. The results revealed that: (1) the FIR tool made a positive impact on effective OMM; (2) by splitting the segmented objects into two groups, training and testing sets which are composed of 70% of the objects, and validation sets which are formed by the remaining 30% of the objects, then combing the selected feature subsets for training to achieve an overall accuracy (OA) of 90.13% and a Kappa coefficient (KC) of 0.88 of the whole datasets; (3) comparing the results of the state-of-the-art method, support vector machine (SVM), in OMM, the proposed framework outperformed SVM by more than 7.28% in OA, 8.64% in KC, 6.15% in producer accuracy of OM and by 9.31% in user accuracy of OM. To the best of our knowledge, it is the first time that OM information has been used through the integration of multiscale segmentation of HSRSI with the CNN to get OMM results. The proposed framework can not only provide reliable technical support for the scientific management and environmental monitoring of open pit mining areas, but also be of wide generality and be applicable to other kinds of land use mapping in mining areas using HSR images.
Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements

Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations.
InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection

The determination of the depth and boundary of the goaf is of great significance for the detection of illegal mining. However, determining the current location of unknown goafs mainly relies on low-efficiency, time-consuming, and labor-intensive physical detection methods such as geomagnetic field changes. Due to their large coverage and high degree of automation, research on remote sensing methods has been conducted to locate mining activities by monitoring surface deformation. This paper proposes a method that relies on the principle of the probability integration method (PIM) and on synthetic aperture radar interferometry (InSAR) to retrieve the location of an underground goaf. First, the relationship between ground subsidence and the location of the mined-out area was established according to PIM; then, the location of the mined-out area was obtained by the surface deformation acquired by InSAR. The proposed method does not rely on complex nonlinear models and has complete parameters; therefore, it has higher engineering application value. A test site in the Fengfeng mining area and 11 Radarsat-2 images were used to verify the proposed method. The experimental results showed that the average relative error of the proposed method is 6.35%, which is 27.56% higher than that of similar algorithms based on complex nonlinear models. Compared to algorithms that ignore the coal seam dip, the accuracy is improved to 98.27%.
Identifying large ischemic core volume ranges in acute stroke that can benefit from mechanical thrombectomy

Background We aimed to identify the large ischemic core (LIC) volume ranges in acute ischemic stroke patients that can benefit from mechanical thrombectomy (MT). Methods Consecutive patients within 24 hours of onset of anterior circulation ischemic stroke with large vessel occlusion and ischemic core volumes of 70–300 mL were included from our single-center prospective database from March 2014 to December 2019. Subjects were divided into three groups by baseline ischemic core volume (A: 70–100 mL; B: 101–130 mL; C: >130 mL). We compared modified Rankin Scale (mRS) score 0–2 at 3 months and parenchymal hematoma between patients receiving MT and standard medical treatment (SMT), and determined clinically treatable core volume ranges for MT. Results Of 157 patients (86 women; median age, 81 years; median ischemic core volume, 123 mL), 49 patients underwent MT. In Group A (n=52), MT patients (n=31) showed a higher proportion of mRS 0–2 at 3 months (52% vs 5%, P<0.05) versus SMT, respectively. Group B (n=36) MT patients (n=14) also had a higher proportion of mRS 0–2 at 3 months (29% vs 9%, P=0.13) versus SMT, respectively. In Group C (n=69), only four patients received MT. The 95% confidence intervals for the probability of mRS 0–2 at 3 months in patients with MT (n=49) versus SMT (n=108) intersected at 120–130 mL. Conclusions Ischemic core volumes between 70 and 100 mL may benefit from MT. The treatable upper core limit is approximately 120 mL in selected patients with LIC of 70–300 mL.
Literature survey of deep learning-based vulnerability analysis on source code

: Vulnerabilities in software source code are one of the critical issues in the realm of software code auditing. Due to their high impact, several approaches have been studied in the past few years to mitigate the damages from such vulnerabilities. Among the approaches, deep learning has gained popularity throughout the years to address such issues. In this literature survey, the authors provide an extensive review of the many works in the field software vulnerability analysis that utilise deep learning-based techniques. The reviewed works are systemised according to their objectives (i.e. the type of vulnerability analysis aspect), the area of focus (i.e. the focus area of the analysis), what information about source code is used (i.e. the features), and what deep learning techniques they employ (i.e. what algorithm is used to process the input and produce the output). They also study the limitations of the papers and topical trends concerning vulnerability analysis.
A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.
Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing

Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.
Semiautomated Mapping of Benthic Habitats and Seagrass Species Using a Convolutional Neural Network Framework in Shallow Water Environments

Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological processes and lead management actions. In this study, we propose a semiautomated framework for the detection and mapping of benthic habitats and seagrass species using convolutional neural networks (CNNs). Benthic habitat field data from a geo-located towed camera and high-resolution satellite images were integrated to evaluate the proposed framework. Features extracted from pre-trained CNNs and a “bagging of features” (BOF) algorithm was used for benthic habitat and seagrass species detection. Furthermore, the resultant correctly detected images were used as ground truth samples for training and validating CNNs with simple architectures. These CNNs were evaluated for their accuracy in benthic habitat and seagrass species mapping using high-resolution satellite images. Two study areas, Shiraho and Fukido (located on Ishigaki Island, Japan), were used to evaluate the proposed model because seven benthic habitats were classified in the Shiraho area and four seagrass species were mapped in Fukido cove. Analysis showed that the overall accuracy of benthic habitat detection in Shiraho and seagrass species detection in Fukido was 91.5% (7 classes) and 90.4% (4 species), respectively, while the overall accuracy of benthic habitat and seagrass mapping in Shiraho and Fukido was 89.9% and 91.2%, respectively.
Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor

This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of a PAN image with high spatial and low spectral resolution and an MS/MIR image with low spatial and high spectral resolution. The proposed methods can extract an optimal scaling factor, and uses the tactics of appropriately controlling the balance between the spatial and spectral resolutions. KOMPSAT-3A PAN and MIR images were used to test and evaluate the performance of the proposed methods. A qualitative assessment were performed using the image quality index (Q4), the cross correlation index (CC) and the relative global dimensional synthesis error (Spectral/Spatial ERGAS). These tests indicate that the proposed methods preserve the spectral and spatial characteristics of the original MIR and PAN images. Visual analysis reveals that the spectral and spatial information derived from the proposed methods were well retained in the test images. A comparison of the results of the proposed methods with those obtained from applying existing ones such as the Multi Sensor Fusion (MSF) technique or the Guide Filter Based Fusion (GF) show the efficiency of the new fusion process to be superior to the one of the others. The results showed a significant improvement in fusion capability for KOMPSAT-3A MIR imagery.
On the ac magnetizability of a molecule

We present a solution to a long-standing problem in molecular physics, namely a physically acceptable expression for the ac magnetizability of a molecule. To this end we apply general arguments based on spatial transformation properties, intrinsic symmetries, linearity, and the dc limit, to express the magnetizability in terms of a linear combination of molecular polarizability tensors of the relevant (electric octopole–magnetic quadrupole) multipole order. Explicit formulae are given in terms of components of these polarizabilities for molecules of various symmetries.
Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data

More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation.