Miracle-dz/UniFAR-FTUs huggingface dataset:
Aerial and Ground Based Sensing of Tolerance to Beet Cyst Nematode in Sugar Beet

The rapid development of image-based phenotyping methods based on ground-operating devices or unmanned aerial vehicles (UAV) has increased our ability to evaluate traits of interest for crop breeding in the field. A field site infested with beet cyst nematode (BCN) and planted with four nematode susceptible cultivars and five tolerant cultivars was investigated at different times during the growing season. We compared the ability of spectral, hyperspectral, canopy height- and temperature information derived from handheld and UAV-borne sensors to discriminate susceptible and tolerant cultivars and to predict the final sugar beet yield. Spectral indices (SIs) related to chlorophyll, nitrogen or water allowed differentiating nematode susceptible and tolerant cultivars (cultivar type) from the same genetic background (breeder). Discrimination between the cultivar types was easier at advanced stages when the nematode pressure was stronger and the plants and canopies further developed. The canopy height (CH) allowed differentiating cultivar type as well but was much more efficient from the UAV compared to manual field assessment. Canopy temperatures also allowed ranking cultivars according to their nematode tolerance level. Combinations of SIs in multivariate analysis and decision trees improved differentiation of cultivar type and classification of genetic background. Thereby, SIs and canopy temperature proved to be suitable proxies for sugar yield prediction. The spectral information derived from handheld and the UAV-borne sensor did not match perfectly, but both analysis procedures allowed for discrimination between susceptible and tolerant cultivars. This was possible due to successful detection of traits related to BCN tolerance like chlorophyll, nitrogen and water content, which were reduced in cultivars with a low tolerance to BCN. The high correlation between SIs and final sugar beet yield makes the UAV hyperspectral imaging approach very suitable to improve farming practice via maps of yield potential or diseases. Moreover, the study shows the high potential of multi- sensor and parameter combinations for plant phenotyping purposes, in particular for data from UAV-borne sensors that allow for standardized and automated high-throughput data extraction procedures.
Decomposition and Conceptualization to Support System Dynamics Behavior Modeling

With the increasing need for data-based decision making, social systems and the ecosystems; practitioners and decision makers need guidance in their decision making, as is offered by data-based models and a systematic generation of simulation tools. Overtly, relations between data and practice have been under-conceptualized. Data modelers and decision makers tend to lack a mutual understanding of each other’s knowledge systems which has led to huge knowledge gaps. Assimilation of modeling methods therefore is vital. Modeling methods use a specific way of thinking, rules and directions on how to model different aspects of systems. These rules and directions are what we refer to as constructs. Conceptualizing model relations and formulations requires significant domain knowledge and understanding of the constructs. In this article, we use the decomposition mechanism to better conceptualize and understand the System Dynamics (SD) model behavior, and show how using a natural language based domain modeling method (Object-Role Modeling, ORM) helps in dealing with complex SD models. Through applying the decomposition mechanism, we are able to better understand the underlying concepts of the stock and flow diagram and update behaviors of ORM objects. To achieve this, we use examples and an SD model derived from a case “Intrapartum process in Ugandan hospitals” to study the object behaviors. The main results of this article include: a theoretical founding of integrating ORM with SD; quantitative analysis at the level of ORM reasoning; and transformation rules from ORM into SD.
Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the practical use of SAR data in geotechnical landslides campaigns, that includes an appropriate dataset selection taking into account the scenario characteristics, a visibility analysis, and considerations when comparing advanced differential SAR interferometry (A-DInSAR) results with other monitoring techniques. We have determined that Sentinel-2 satellite optical images are suited for performing high resolution land cover classifications, which results in the achievement of qualitative visibility maps. We also concluded that A-DInSAR is a very powerful and versatile tool for detailed scale landslide monitoring, although in combination with other instrumentation techniques.
Large-Scale Accurate Reconstruction of Buildings Employing Point Clouds Generated from UAV Imagery

High-density point clouds are valuable and detailed sources of data for different processes related to photogrammetry. We explore the knowledge-based generation of accurate large-scale three-dimensional (3D) models of buildings employing point clouds derived from UAV-based photogrammetry. A new two-level segmentation approach based on efficient RANdom SAmple Consensus (RANSAC) shape detection is developed to segment potential facades and roofs of the buildings and extract their footprints. In the first level, the cylinder primitive is implemented to trim point clouds and split buildings, and the second level of the segmentation produces planar segments. The efficient RANSAC algorithm is enhanced in sizing up the segments via point-based analyses for both levels of segmentation. Then, planar modelling is carried out employing contextual knowledge through a new constrained least squares method. New evaluation criteria are proposed based on conceptual knowledge. They can examine the abilities of the approach in reconstruction of footprints, 3D models, and planar segments in addition to detection of over/under segmentation. Evaluation of the 3D models proves that the geometrical accuracy of LoD3 is achieved, since the average horizontal and vertical accuracy of the reconstructed vertices of roofs and footprints are better than (0.24, 0.23) m, (0.19, 0.17) m for the first dataset, and (0.35, 0.37) m, (0.28, 0.24) m for the second dataset.
Ground-Based Differential Interferometric Radar Monitoring of Unstable Mountain Blocks in a Coastal Environment

In this paper, we present the results of eight years of continuous monitoring with a ground-based, interferometric, real-aperture radar of two unstable mountain blocks at Tafjord on the western coast of Norway. A real-time, interferometric, ground-based radar has the capability to provide high accuracy range measurements by using the phase of the transmitted signal, thus achieving sub-millimeter accuracy when a sufficient signal-to-noise level is present. The main challenge with long term monitoring is the variations in radio refractivity caused by changes in the atmosphere. The range variations caused by refractive changes in the atmosphere are corrected using meteorological data. We use triangular corner reflectors as references to improve the signal-to-clutter ratio and improve the accuracy of the measurements. We have also shown that by using differential interferometry, a significant part of the variation caused by radio refractivity variations is removed. The overall reduction in path length variation when using differential interferometry varies from 27 to 164 times depending on the radar-to-reflector path length. The measurements reveal cyclic seasonal variations, which are coherent with air temperature. The results show that radar measurements are as accurate as data from in situ instruments like extensometers and crack meters, making it possible to monitor inaccessible areas. The total measured displacement is between 1.2 mm and 4.7 mm for the two monitored mountain blocks.
A Level Set Method for Infrared Image Segmentation Using Global and Local Information

: Infrared image segmentation plays a signiﬁcant role in many burgeoning applications of remote sensing, such as environmental monitoring, trafﬁc surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.
Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest

: The photochemical reﬂectance index (PRI) is calculated from vegetation narrowband reﬂectance in two bands in the visible part of the spectrum. Variations in PRI are associated with changes in the xanthophyll cycle pigments which regulate the light use efﬁciency of vegetation. Correlations have been found between remotely-sensed PRI and various photosynthetic productivity parameters at the scales from leaves to landscapes. Environmental satellites can provide only an instantaneous value of this index at the time of overpass. The diurnal course of needle (leaf) PRI needs to be known in order to link the instantaneous values robustly with photosynthetic parameters at time scales exceeding one day. This information is not currently available in the scientiﬁc literature. Here we present the daily cycle of Scots pine needle and canopy PRI in a southern boreal forest zone in the presence of direct solar radiation during the peak growing season of two consecutive years. We found the PRI of the needles which are exposed to direct radiation to have a distinct diurnal cycle with constant or slightly increasing values before noon and a daily minimum in the afternoon. The cycle in needle PRI was not correlated with that in the incident photosynthetic photon ﬂux density (PPFD). However, when PPFD was above 1000 µ mol m − 2 s − 1 , approximately between 8 a.m. and 5 p.m. , needle PRI was correlated with the light use efﬁciency (LUE), measured with shoot chambers. The timing of the minimum needle PRI coincided with the minimum canopy value, as measured by an independent sensor above the canopy, but the correlation between the two variables was not signiﬁcant. Our ﬁeld results corroborate the applicability of needle PRI in monitoring the daily variation in LUE. However, to apply this to remote sensing of seasonal photosynthetic productivity, the daily cycle of leaf PRI needs to be known for the speciﬁc vegetation type.
Microrelief Associated with Gas Emission Craters: Remote-Sensing and Field-Based Study

Formation of gas emission craters (GEC) is a new process in the permafrost zone, leading to considerable terrain changes. Yet their role in changing the relief is local, incomparable in the volume of the removed deposits to other destructive cryogenic processes. However, the relief-forming role of GECs is not limited to the appearance of the crater itself, but also results in positive and negative microforms as well. Negative microforms are rounded hollows, surrounded by piles of ejected or extruded deposits. Hypotheses related to the origin of these forms are put forward and supported by an analysis of multi-temporal satellite images, field observations and photographs of GECs. Remote sensing data specifically was used for interpretation of landform origin, measuring distances and density of material scattering, identifying scattered material through analysis of repeated imagery. Remote-sensing and field data reliably substantiate an impact nature of the hollows around GECs. It is found that scattering of frozen blocks at a distance of up to 293 m from a GEC is capable of creating an impact hollow. These data indicate the influence of GEC on the relief through the formation of a microrelief within a radius of 15–20 times the radius of the crater itself. Our study aims at the prediction of risk zones.
SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks

In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the traditional procedure: detection, discrimination, classification and recognition. Each step is supposed to provide optimal processing results for the next step, but this is difficult to implement in real-life applications because of speckle noise and inefficient connection among these procedures. To solve this problem, the RCNN is applied to large scene SAR target recognition, which can detect the objects while recognizing their classes based on its regression method and the sharing network structure. However, size of the input images to RCNN is limited so that the classification could be accomplished, which leads to a problem that RCNN is not able to handle the large scene SAR images directly. Thus, before the RCNN, a fast sliding method is proposed to segment the scene image into sub-images with suitable size and avoid dividing targets into different sub-images. After the RCNN, candidate regions on different slices are predicted. To locate targets on large scene SAR images from these candidate regions on small slices, the Non-maximum Suppression between Regions (NMSR) is proposed, which could find the most proper candidate region among all the overlapped regions. Experiments on 1476 × 1784 simulated MSTAR images of simple scenes and complex scenes show that the proposed method can recognize all targets with the best accuracy and fastest speed, and outperform the other methods, such as constant false alarm rate (CFAR) detector + support vector machine (SVM), Visual Attention+SVM, and Sliding-RCNN.
Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

This paper presents a method for synthetic aperture radar (SAR) image segmentation by draing upon a reaction–diffusion (RD) level set evolution (LSE) equation. The well-known RD theory consists of two main parts: reaction and diffusion terms. We first constructed the reaction term using an energy functional, which integrates the gamma statistical distribution with region–edge information from SAR images that can simultaneously suppress speckle noise and drive the active contour toward the object boundaries. Then, we used partial differential equation-based LSE to solve the proposed energy functional. Finally, a diffusion term was introduced into the LSE to ensure stability of the level set function and regularize the segmented region. The experimental results of both simulated and real SAR images showed that the proposed model has good robustness against a speckle noise as well as higher segmentation efficiency and accuracy than some existing models.
Accurate Control of a Pan-tilt System Based on Parameterization of Rotational Motion

A pan-tilt camera system has been adopted by a variety of fields since it can cover a wide range of region compared to a single fixated camera setup. Yet many studies rely on factory-assembled and calibrated platforms and assume an ideal rotation where rotation axes are perfectly aligned with the optical axis of the local camera. However, in a user-created setup where a pan-tilting mechanism is arbitrarily assembled, the kinematic configurations may be inaccurate or unknown, violating ideal rotation. These discrepancies in the model with the real physics result in erroneous servo manipulation of the pan-tilting system. In this paper, we propose an accurate control mechanism for arbitrarily-assembled pan-tilt camera systems. The proposed method formulates pan-tilt rotations as motion along great circle trajectories and calibrates its model parameters, such as positions and vectors of rotation axes, in 3D space. Then, one can accurately servo pan-tilt rotations with pose estimation from inverse kinematics of their transformation. The comparative experiment demonstrates out-performance of the proposed method, in terms of accurately localizing target points in world coordinates, after being rotated from their captured camera frames.
Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy

This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based supervised classification scheme to produce accurate crop type mapping in a smallholder agricultural zone in Navarra, Spain. The scheme makes use of supervised classifiers Support Vector Machines (SVMs) and Random Forest (RF) to discriminate among the various crop types, based on a large variable space of Sentinel-2 imagery and Vegetation Index (VI) time-series. The classifiers are separately applied at three different levels of crop nomenclature hierarchy, comparing their performance with respect to accuracy and execution time. SVM provides optimal performance and proves significantly superior to RF for the lowest level of the nomenclature, resulting in 0.87 Cohen’s kappa coefficient. Experiments were carried out to assess the importance of input variables, where top contributors are the Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral bands, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) indices, sensed during advanced crop phenology stages. The scheme is finally applied to a Lansat-8 OLI based equivalent variable space, offering 0.70 Cohen’s kappa coefficient for the SVM classification, highlighting the superior performance of Sentinel-2 for this type of application. This is credited to Sentinel-2’s spatial, spectral, and temporal characteristics.
A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification

Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accuracy, in this paper we propose an end-to-end fast dense spectral–spatial convolution (FDSSC) framework for HSI classification. The FDSSC framework uses different convolutional kernel sizes to extract spectral and spatial features separately, and the “valid” convolution method to reduce the high dimensions. Densely-connected structures—the input of each convolution consisting of the output of all previous convolution layers—was used for deep learning of features, leading to extremely accurate classification. To increase speed and prevent overfitting, the FDSSC framework uses a dynamic learning rate, parametric rectified linear units, batch normalization, and dropout layers. These attributes enable the FDSSC framework to achieve accuracy within as few as 80 epochs. The experimental results show that with the Indian Pines, Kennedy Space Center, and University of Pavia datasets, the proposed FDSSC framework achieved state-of-the-art performance compared with existing deep-learning-based methods while significantly reducing the training time.
Smartphone Restriction and Its Effect on Subjective Withdrawal Related Scores

Excessive smartphone use has been associated with a number of negative consequences for the individual and the environment. Some similarities can be observed between excessive smartphone usage and several behavioural addictions, and continual usage constitutes one of several characteristics included in addiction. In the extreme high end of the distribution of smartphone usage, smartphone restriction might be expected to elicit negative effects for individuals. These negative effects may be regarded as withdrawal symptoms traditionally associated with substance-related addictions. To address this timely issue, the present study examined scores on the Smartphone Withdrawal Scale (SWS), the Fear of Missing Out Scale (FoMOS) and the Positive and Negative Affect Schedule (PANAS) during 72 h of smartphone restriction. A sample of 127 participants (72.4% women), aged 18–48 years (M = 25.0, SD = 4.5), were randomly assigned into one of two conditions: a restricted condition (experimental group, n = 67) or a control condition (control group, n = 60). During the restriction period participants completed the aforementioned scales three times a day. The results revealed significantly higher scores on the SWS and FoMOS for participants allocated to the restricted condition than those assigned to the control condition. Overall the results suggest that smartphone restriction could cause withdrawal symptoms.