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Place recognition is widely used for global localization technology. However, the existing place recognition solutions are limited by the requirement for the same type of sensors to be used in both the localization process and the mapping process. Therefore, the existing heterogeneous 3D map cannot be used for place recognition directly with the existing methods, leading to underutilization of information. In addition, most of the existing global feature descriptors used in place recognition solutions are still not highly descriptive and perform poorly under changed viewpoint scenes. To resolve these challenges, this paper presents a place recognition solution using virtual light detection and ranging (LiDAR) and polar grid height coding image (PGHCI) descriptors in the existing heterogeneous 3D map. First, virtual LiDAR is proposed to generate a series of virtual scans that are similar to the real scan of the localization sensor from the existing map, overcoming the limitation of the existing place recognition methods. Next, a novel PGHCI descriptor for place recognition is generated, and a method that overcomes the recognition difficulty of changed viewpoints in the same scene is presented. Two weighted distances for similarity estimation are analyzed, and the performance of the PGHCI descriptor with different parameters is evaluated. Finally, the performance of the PGHCI descriptor and the solution proposed in the paper is evaluated on several popular benchmark datasets and our own dataset. Comprehensive experiments demonstrated that the PGHCI descriptor has higher descriptiveness and is robust with respect to the changed viewpoint scene, as shown by the comparison of precision-recall (PR) curves using datasets with multiple scenes. The proposed place recognition solution has 100% success rates in the evaluation exclude under occluded conditions, showing that it is feasible to achieve robust place recognition using a heterogeneous 3D map.

This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an unmanned aircraft system (UAS), and the crop health status was assessed through the green area index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs used machine learning pipelines with several regression algorithms (multiple linear models, support vector machines, random forests, and artificial neural networks) along with a feature selection strategy. The random forests algorithm was shown to be the best algorithm for GAI prediction with an average relative root mean square error of 10.86% and a mean absolute error of 0.67. The resulting GAI maps and the regression feature space were classified with random forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented.

Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared sensors, with the Keeling curve as an early indicator of the anthropogenic contribution to global atmospheric carbon dioxide. The increasing CO2 levels are alarming and have led to international agreements that promote cleaner industrial activities. However, any change in global behavior would not immediately cause detectable changes in the MLO data; the extent to which global and long-term trends are conflated with local and short-term variations remains unclear. Hence the current article verifies the performance of the sampling and measurement systems of MLO, using existing data published within the months of October and November 2020, which comply with the temporal continuity requirements of chronostatistics. It has been determined that the components of the MLO air including carbon dioxide are well mixed due to their particular location. Beyond this, the variographic analysis distinguishes between small (10%) variability contributions due to sampling, including graphical depictions of MLO data. Coupled with the precision of the method being better than 0.2 ppm, it has been determined that the sampling and measurement protocols are highly suitable to meet the objective of representing CO2 fluctuations over time. The variographic application also manages to quantify short-term variabilities resulting from the local processes of the region where the observatory is located. The results support the furthering of multiscaled temporal analysis of atmospheric CO2, and potentially the incorporation of CO2 variographic parameters into empirical and semiempirical climate models.

Visibility is a key factor influencing animal behavior in forest ecosystems. Fine-scale visibility in forested areas has been measured by ground-based approaches at the plot level, using site-specific methods that have limited spatial coverage. Here we examine airborne laser scanning (ALS) as a novel tool to quantify fine-scale visibility in the temperate forests of Germany at a landscape scale. We validate the (vertically-derived) ALS-derived visibility measures using proven (horizontally-derived) terrestrial laser scanning (TLS) estimates of true visibility. Our results indicate that there is a good agreement between the visibility resulting from ALS and TLS with an R2 ranging from 0.53 to 0.84 and a normalized RMSE varying from 15.92% to 11.81% at various plot sizes, with the highest accuracy achieved using a plot size of 35 × 35 m. Our study demonstrates for the first time that ALS can be successfully applied to quantify fine-scale visibility in temperate forests at a landscape level. This approach holds potential for studying the spatial behavior of animals (e.g., habitat selection and predator–prey relationships) in forest ecosystems.

Forest cover requires large scale and frequent monitoring as an indicator of biodiversity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS‐RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS‐RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS‐RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes.

Modern virtual reality (VR) technology has garnered significant attention in the geographic visualization community for its ability to immerse users within geospatial data sets. While immersion within one-to-one models of reality offers unique and powerful perspectives from which to view spatial data, VR also allows users to transcend the physical limitations of the real world, thereby allowing them to visualize, experience, and interact with spatial data at any scale, in any virtual environment, at any time. This paper presents a collection of 3D data-driven geovisualization case studies, implemented in an immersive virtual GIScience data visualization space (IVEVA). IVEVA was purposefully designed and developed to highlight the differences in spatial data type, the challenges associated with spatial data visualization in an immersive virtual environment, and the importance of adhering to the established design heuristics of cartography, human–computer interaction, and extended reality (XR) development. Through this process, we offer our observations on how well each data type fits this medium of visualization and interpretation, and how the design heuristics play out for an immersive virtual environment that extends the practicable space in which GIScience and visual analytics are performed. Finally, we offer our perspectives, from designing and developing this prototype, on the future for immersive interface-based GIScience.

[Introduction]: "Numerous crowdsourcing and social media platforms such as CrowdSpring, Idea Bounty, DesignCrowd, Facebook, Twitter, Flickr, Weibo, WeChat, and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful information for detecting traffic patterns, mitigating security risks and other types of time-critical events, discovering social structures characteristics, predicting human movement, etc. Crowdsourcing, also known as volunteered geographic information (VGI), has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies (Chen et al., 2016; Niu & Silva, 2020). However, analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general, the inherent noisy data that requires high-performance cost of preprocessing, and the heterogeneity in quality and quantity of sources. In particular, geosocial media data and their derived metrics can provide valuable insights and policy strategies, but they require a deep understanding of what the metrics actually measure (Zook, 2017). All of these underpin complex assessments, not mentioning the ethnic and privacy issues. Therefore, new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms."

Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.

This paper presents a finite element (FE) method of modelling reinforced concrete beams. The proposed model takes into account the phenomena characteristic of reinforced concrete structures, such as the interaction between two materials (concrete and steel), the cracking caused by mechanical loads and the variation of the Young's modulus under increasing load. A relevant numerical FE analysis was carried out in the ABAQUS system using the concrete damaged plasticity (CDP) model. The character of Young's modulus variation due to increasing stress intensity level was taken from the author's own research. The results of the FE calculations were compared with the results yielded by the author's numerical bar model.

International audience

In the case of underground network infrastructure it can be seen that objects functioning in the second technical condition, according to DWA-ATV 143-2, and subjected to rehabilitation with the use of close-fit trenchless technologies are capable of withstanding external loads. The main external load that is taken into account in engineering calculations in the case of conduits in the second technical condition is external groundwater pressure. In order to compare design parameters obtained with the use of various calculation methods, a comparative analysis was conducted in order to determine the values of critical pressure. The calculations were carried out using popular engineering algorithms. In addition, analyses using the Finite Element Method and Abaqus software as a computational tool were carried out for the purpose of verifying laboratory tests. This paper aims to broaden knowledge concerning the possibility of performing control numerical analyses for close-fit liners installed in pipelines that are in the second technical condition according to DWA-ATV 143-2. The analyses were carried out on ten 3D models. The models were parameterized in order to reflect the CIPP samples in the most accurate way. The computational models were built based on assumptions, which are commonly used in this type of scientific analysis, regarding material parameters and their interactions. The direct value of the performed engineering calculations and numerical analyses is the extension of knowledge in the field of strength parameters that are obtained by various material groups of close-fit liners. Comparative analysis of the results of laboratory tests and numerical analyses, and the conclusions that result from them, constitute the basis for the optimization of the design process and the individual approach to issues related to the use of liners that strengthen underground pipelines.

The article proposes the methodology of designing dams of dry flood control reservoirs. The algorithm is developed so as to meet all the requirements given in the Eurocode 7 and, at the same time, to be efficient in terms of necessary calculation time. Furthermore, the presented numerical procedure enables the optimization of design solutions, e.g. the depth and length of the anti-filtration barrier, by means of parametric analyses. The approach assumes the use of numerical methods, in particular, finite element (FE) analysis. Three-dimensional (3D) reconstruction of the terrain topography and subsoil layer arrangement performed in step (1) sets the base for further analyses. In step (2), the filtration phenomena are assessed based on the 3D analysis of a transient groundwater flow. In step (3), the state of displacement is evaluated and the stability is verified for all the relevant phases of construction and operation of the facility, in particular, in the course of simulated flood detention. The analyses in step (3) are carried out on 2D models corresponding to the design cross-sections of the dam. This significantly reduces the computation time (compared to 3D analysis) and, at the same time, provides a safe estimate of factor of safety. The performance of the proposed algorithm is shown on the numerical examples of the computations concerning the dam of Szalejów Górny dry anti-flood reservoir located in Poland.

The Tunnel Contour Quality Index (TCI) is an index established by Kim and Bruland for an effective management of a tunnel contour quality. It is estimated on a basis of measurements of two contour profiles within a single blasting round, using a laser profiler. However, the representativeness of measurement results obtained that way for the assessment of a contour quality of the entire blasting round is disputable. Terrestrial laser scanning (TLS) technology, combined with available numerical surface modeling tools, enables development of three-dimensional models of a monitored surface. The article reports results of TCI calculations based on TLS data. The presented TLS technique is based not only on selected cross-sections of the tunnel contour but also on the description of the morphology of the tunnel contour surface. The case study concerns measurements of the “Mały Luboń” tunnel niche, located in Naprawa, Poland. The TCI values for three blasting rounds were determined in accordance with Kim and Bruland's guidelines and were compared to TCI values determined with the proposed TLS technique. On a basis of this comparison, it can be concluded that the results obtained with the TLS technique are more reliable and representative for description of the contour quality of the entire blasting round than results obtained with the laser profiling technique.

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the minimum detectable change. To analyse geomorphic change in point cloud data, the multiscale model-to-model cloud comparison (M3C2) approach is commonly applied, which provides a statistical significance test. This test assumes planar surfaces and a uniform registration error. For natural surfaces, the planarity assumption does not necessarily apply, in which cases the value of minimal detectable change (Level of Detection) is overestimated. To overcome these limitations, we quantify an uncertainty information for each 3D point by propagating the uncertainty of the measurements themselves and of the alignment uncertainty to the 3D points. This allows the calculation of 3D covariance information for the point cloud, which we use in an extended statistical test for equality of multivariate means. Our method, called M3C2-EP, gives a less biased estimate of the Level of Detection, allowing a more appropriate significance threshold in typical cases. We verify our method in two simulated scenarios, and apply it to a time series of terrestrial laser scans of a rock glacier at two different timespans of three weeks and one year. Over the three-week period, we detect significant change at 12.5% fewer 3D locations, while quantifying additional 25.2% of change volume, when compared to the reference method of M3C2. Compared with manual assessment, M3C2-EP achieves a specificity of 0.97, where M3C2 reaches 0.86 for the one-year timespan, while sensitivity drops from 0.72 for M3C2 to 0.60 for M3C2-EP. Lower Levels of Detection enable the analysis of high-frequency monitoring data, where usually less change has occurred between successive scans, and where change is small compared to local roughness. Our method further allows the combination of data from multiple scan positions or data sources with different levels of uncertainty. The combination using error propagation ensures that every dataset is used to its full potential.

In this paper, we propose a novel approach that reconstructs the indoor scene in a structure-aware manner and produces two meshes with different levels of abstraction. To be precise, we start from the raw triangular mesh of indoor scene and decompose it into two parts: structure and non-structure objects. On the one hand, structure objects are defined as significant permanent parts in the indoor environment such as floors, ceilings and walls. In the proposed algorithm, structure objects are abstracted by planar primitives and assembled into a polygonal structure mesh. This step produces a compact structure-aware watertight model that decreases the complexity of original mesh by three orders of magnitude. On the other hand, non-structure objects are movable objects in the indoor environment such as furniture and interior decoration. Meshes of these objects are repaired and simplified according to their relationship with respect to structure primitives. Finally, the union of all the non-structure meshes and structure mesh comprises the scene mesh. Note that structure mesh and scene mesh preserve various levels of abstraction and can be used for different applications according to user preference. Our experiments on both LIDAR and RGBD data scanned from simple to large scale indoor scenes indicate that the proposed framework generates structure-aware results while being robust and scalable. It is also compared qualitatively and quantitatively against popular mesh approximation, floorplan generation and piecewise-planar surface reconstruction methods to demonstrate its performance.

The interaction between a moving submarine and seawater generates characteristic wakes on the sea surface, enabling indirect detection of undersea objects via airborne remote sensing. Here, we demonstrate the feasibility of using visible-light polarization imaging to observe submarine wakes. The key links in the imagine chain are considered separately. These include the polarization patterns of skylight, the elevations and slopes of submarine wakes and sea waves, and the changes in the sea surface polarized bidirectional reflectance characteristics due to modulation of gravity–capillary waves by the wake’s velocity field. A complete model of the airborne optical polarization imaging process is constructed and images are simulated via ray tracing. All theories proposed are verified by a series of terrestrial observation experiments. The results show that both the sea surface roughness modulation by the wake’s velocity field and the sea surface slope formed by wake elevation play significant roles in the imaging process. The wake features in the Stokes vector linear polarization component (Q, U) images are effectively enhanced, and the environmental adverse effect on these images is smaller than that on the intensity images. The degree of linear polarization (DoLP) and angle of polarization (AoP) images exhibit acceptable contrast under certain zenith and azimuth angles. Thus, our analysis confirms that airborne optical polarization imaging has considerable potential for observing wakes and other small- and medium-scale ocean dynamic processes.

Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial “big data”. With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories.

International audience; This paper investigates automatic digitization with complete coverage of large and complex environments using a TLS or a mobile scanner. We propose an adaptive multi-objective view-planner that can operate in an unknown environment to provide guidance for the human operator and ease the scanning task or by a mobile robot for an automatic exploration of the environment. The proposed view-planner is adapted to environments where the sensor is operating on a flat surface such as office spaces, urban areas, open fields or in some cultural heritage applications. First, we propose an adaptive gap-based method to extract occluded areas in a point cloud, which is completely automated and does not require extensive computations in a large environment such as ray-tracing or level-set methods. Then, we introduce a novel exploration strategy that uses specific regions of the environment called "Conservative-Cells" to drastically reduce the number of sensing positions to achieve complete digitization of the environment. Both methods were validated with simulated and real point clouds. The proposed approach has been applied to a scanner carried by a mobile robot, then to data acquired by a TLS used by a human operator in a large, complex environment. Experimental results on both TLS and mobile robot show that our view-planning approach is effective in finding a sequence of positions that leads to a complete reconstruction of the environment. Moreover, the proposed approach shows efficient performance in terms of coverage rate and computational time compared to others view-planning approaches as well as the results of an experienced human operator in a large, complex environment.

Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement. This is a well established field, with many techniques being proposed over the years, usually based on pixel or region-level comparisons involving sensor-specific thresholds and neighborhood statistics. In this paper, we address the problem of active fire detection using deep learning techniques. In recent years, deep learning techniques have been enjoying an enormous success in many fields, but their use for active fire detection is relatively new, with open questions and demand for datasets and architectures for evaluation. This paper addresses these issues by introducing a new large-scale dataset for active fire detection, with over 150,000 image patches (more than 200 GB of data) extracted from Landsat-8 images captured around the world in August and September 2020, containing wildfires in several locations. The dataset was split in two parts, and contains 10-band spectral images with associated outputs, produced by three well known handcrafted algorithms for active fire detection in the first part, and manually annotated masks in the second part. We also present a study on how different convolutional neural network architectures can be used to approximate these handcrafted algorithms, and how models trained on automatically segmented patches can be combined to achieve better performance than the original algorithms - with the best combination having 87.2% precision and 92.4% recall on our manually annotated dataset. The proposed dataset, source codes and trained models are available on Github (https://github.com/pereira-gha/activefire), creating opportunities for further advances in the field, 23 pages, 17 figures

Tree-crown and height are primary tree measurements in forest inventory. Convolutional neural networks (CNNs) are a class of neural networks, which can be used in forest inventory; however, no prior studies have developed a CNN model to detect tree crown and height simultaneously. This study is the first-of-its-kind that explored training a mask region-based convolutional neural network (Mask R-CNN) for automatically and concurrently detecting discontinuous tree crown and height of Chinese fir (Cunninghamia lanceolata (Lamb) Hook) in a plantation. A DJI Phantom4-Multispectral Unmanned Aerial Vehicle (UAV) was used to obtain high-resolution images of the study site, Shunchang County, China. Tree crown and height of Chinese fir was manually delineated and derived from this UAV imagery. A portion of the ground-truthed tree height values were used as a test set, and the remaining measurements were used as the model training data. Six different band combinations and derivations of the UAV imagery were used to detect tree crown and height, respectively (Multi band-DSM, RGB-DSM, NDVI-DSM, Multi band-CHM, RGB-CHM, and NDVI-CHM combination). The Mask R-CNN model with the NDVI-CHM combination achieved superior performance. The accuracy of Chinese fir’s individual tree-crown detection was considerable (F1 score = 84.68%), the Intersection over Union (IoU) of tree crown delineation was 91.27%, and tree height estimates were highly correlated with the height from UAV imagery (R2 = 0.97, RMSE = 0.11 m, rRMSE = 4.35%) and field measurement (R2 = 0.87, RMSE = 0.24 m, rRMSE = 9.67%). Results demonstrate that the input image with an CHM achieves higher accuracy of tree crown delineation and tree height assessment compared to an image with a DSM. The accuracy and efficiency of Mask R-CNN has a great potential to assist the application of remote sensing in forests.

To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. However, recent advancements in satellite remote sensing, including the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) offer new tools for generating satellite derived bathymetry (SDB). The key payload onboard ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS), a micro-pulse, photon-counting LiDAR system, simultaneously emitting six separate 532 nm beams at 10 kHz pulse rate. However, despite its high resolution, the major limitation for bathymetry is that ICESat-2 only provides along-track height profiles, leaving observation gaps between the parallel ground tracks. Merging ICESat-2 observations with optical multispectral imagery, as demonstrated herein, provides an effective solution for deriving a full scene of water depth in light of the spectral attenuation behavior. This study aims to combine ICESat-2 and Sentinel-2 optical data to derive shallow water bathymetry (depth

Image line segment detection is a fundamental problem in computer vision and remote sensing. Although numerous state-of-the-art methods have shown great performance for straight line segment detection, line segment detection for distorted images without undistortion is still a challenging problem. Besides, there is a lack of a unified line segment detection framework for both distorted and undistorted images. To address these two problems, we propose a novel learning-based Unified Line Segment Detection method (i.e., ULSD) for distorted and undistorted images in this paper. Specifically, we first propose a novel equipartition point-based Bezier curve representation to model arbitrary distorted line segments. Then the line segment detection is tackled by equipartition point regression with an end-to-end trainable neural network. Consequently, the proposed ULSD is independent of camera distortion parameters and does not need any undistortion preprocessing. In the experiments, the proposed method is firstly evaluated on the pinhole, fisheye, and spherical image datasets, respectively, as well as trained and tested on the mixed dataset with differently distorted images. The experimental results on each distortion model show that the proposed ULSD is more competitive than the state-of-the-art methods for both accuracy and efficiency, especially for the results of the unified model trained on the mixed datasets, thus demonstrating the effectiveness and generality of the proposed ULSD to real-world scenarios. The source code and datasets are available at https://github.com/lh9171338/ULSD-ISPRS.

To improve the accuracy of wood and leaf points classification for individual tree, this paper proposed a separation method based on mode points evolution from terrestrial LiDAR point clouds. In the proposed method, the Mean Shift method was used to first acquire the mode points, which were then adopted as nodes to build a network graph for the individual tree. By path retracing and calculating the visiting frequency of each node, the wood seed nodes were detected. To obtain more wood nodes, the wood seed nodes were evolved based on three constraints, namely the shortest path length of the evolved nodes to the base node should be smaller, the evolved nodes should not belong to the leaf nodes that have been detected by path retracing and the verticality of the evolved nodes should be similar as the wood seed nodes. After wood nodes evolution, the segments corresponding to each wood seed node were merged together to obtain the final wood points. The proposed method has been evaluated using nine tree samples with seven different tree species. Experimental results showed that the proposed method can achieve an average wood and leaf classification accuracy of 0.892. The average F1 score for wood was 0.871, while the average F1 score for leaf was 0.900. Compared to two other famous wood and leaf classification methods, the proposed method can achieve better classification results.

The technology of the schemes development automation for areal coverage with aerial and space photography materials is proposed. Within its framework, a method for automatic coordinate referencing of images on the Earth’s ellipsoid surface with the subsequent determination of nomenclature sheets of topographic maps on a composite table was substantiated and implemented. The proposed method is based on the algorithms for calculating the azimuths and lengths of lines connecting the projections of the photographing points (inverse geodetic problem) and the coordinates of the images corner points’ projections (direct geodetic problem). The formulas for solving the geodetic tasks of the images coordinate referencing over long distances are obtained as a result of transforming and integrating the equations of geodetic lines described by the Claireau equation though F. Bessel’s method. It can be used when planning and performing aerial and space surveys, as well as to determine the cartographic base in order to select the starting points for photogrammetric condensation of the geodetic network. The technology is focused on the automation of phototriangulation technical design procedures and is good for linking images to the corresponding sections of electronic and digital maps.

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

The Altai Mountains are one of the most impressive and valuable archaeological areas in the world. Kurgans (burial mounds) of ancient civilizations, which are scattered across the vast Altai area, are an exceptionally valuable source of information for archaeology. These precious archaeological resources, which sometimes have been preserved intact in the permafrost underground for over two millennia, are now under various threats, such as natural disasters, farmland expansion, touristic development, and most notably global warming. A detailed map or inventory of the mounds is essential but is still not available. In this study, we test the deep convolutional neural network (CNN) technique for automatic detection of stone mounds from high-resolution satellite images in four regions in the Altai Mountains. We propose three improvement techniques to increase the performance of off-the-shelf object detection methods that are originally proposed for daily-life objects. Our results demonstrate that it is feasible to apply CNN to detect stone mounds, and the detection results are good enough to capture their spatial distribution. CNN-based object detection can largely narrow down the search area for archaeologists in yet un-surveyed regions, and is therefore useful for preparing field survey campaigns and directing archaeological fieldwork. We also applied the method to an un-surveyed Altai Mountain area and successfully discovered stone mounds that are yet undocumented. Our method can potentially be applied to construct an inventory for all stone mounds present in the whole Altai Mountain region.

High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.

Urban trees exhibit a wide range of ecosystem services that have long been unveiled and increasingly reported. The ability to map tree species and analyze tree health conditions would become vividly essential. Remote sensing techniques, especially hyperspectral imaging, are being evolved for species identification and vegetation monitoring from spectral reponse patterns. In this study, a hyperspectral library for urban tree species in Hong Kong was established comprising 75 urban trees belonging to 19 species. 450 bi-monthly images were acquired by a terrestrial hyperspectral camera (SPECIM-IQ) from November 2018 to October 2019. A Deep Neural Network classification model was developed to identify tree species from the hyperspectral imagery with an overall accuracy ranging from 85% to 96% among different seasons. Representative spectral reflectance curves of healthy and unhealthy conditions for each species were extracted and analyzed. The hyperspectral phenology models were developed to achieve high accuracy and optimization of data acquisition. The bi-monthly canopy signatures and vegetation indices revealed different seasonality patterns of evergreen and deciduous species in Hong Kong. We explored the utility of terrestrial hyperspectral remote sensing and Deep Neural Network for urban tree species identification and characterizing. This provides a unique baseline to understand hyperspectral characteristics and seasonality of urban tree species in Hong Kong that can also contribute to hyperspectral imaging and database development elsewhere in the world.

Indoor map is a fundamental element of indoor location-based services (ILBS). However, traditional indoor mapping techniques are labor-intensive and time-consuming. The advancement of smartphones offers great opportunities for crowdsourcing-based indoor mapping, which is one of the most promising applications due to its low cost and flexibility. Over the last decade, many crowdsourcing-based indoor mapping solutions using smartphones have been proposed. This article provides a systematic review of these works. Different from former surveys, we classify the indoor mapping process by the stage of map construction. In particular, we highlight the two key steps, geospatial-element acquisition, and indoor-map construction, and provide state-of-the-art techniques on these topics. Then, we systematically review the crowdsourcing-based indoor mapping solutions under grid-based, landmark-based, and semantic maps. In addition to covering the principles, benefits, and challenges, these systems are compared in terms of sensors, participation, output, experimental environment, and reported accuracy. Besides these existing performance criteria, we extract quantitative performance criteria that are suitable to evaluate crowdsourcing-based indoor mapping solutions. Finally, we present open issues and future research directions.

Taking cities as objects being observed, urban remote sensing is an important branch of remote sensing. Given the complexity of the urban scenes, urban remote sensing observation requires data with a high temporal resolution, high spatial resolution, and high spectral resolution. To the best of our knowledge, however, no satellite owns all the above characteristics. Thus, it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation. In this study, we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model, filling the gap of no existing urban remote sensing framework. In this study, we present four applications to elaborate on the specific applications of the proposed model: 1) a spatio-temporal fusion model for synthesizing ideal data, 2) a spatio-spectral observation model for urban vegetation biomass estimation, 3) a temporal-spectral observation model for urban flood mapping, and 4) a spatio-temporal-spectral model for impervious surface extraction. We believe that the proposed model, although in a conceptual stage, can largely benefit urban observation by providing a new data fusion paradigm.

Deep learning methods have achieved the state-of-the-art performance of object detection and tracking in natural images, such as keypoint-based detectors and appearance/motion-based trackers. However, for small and blurry moving vehicles in satellite videos, keypoint-based detectors cause the missed detection of keypoints and incorrect keypoint matching. In terms of multi-object tracking, it is difficult to track the crowded similar vehicles stably only by using the appearance or motion information. To address these problems, a novel deep learning framework is proposed for moving vehicle detection and tracking in the satellite videos. It is comprised of the cross-frame keypoint-based detection network (CKDNet) and spatial motion information-guided tracking network (SMTNet). In CKDNet, a customized cross-frame module is designed to assist the detection of keypoints by exploiting complementary information between frames. Furthermore, CKDNet improves keypoint matching by incorporating size prediction around the keypoints and defining the soft mismatch suppression for out-of-size keypoint pairs. Based on high-quality detection, SMTNet can track the densely-packed vehicles effectively by constructing two-branch long short-term memories. It extracts not only spatial information of the same frame by considering the relative spatial relationship of neighboring vehicles, but also motion information among consecutive frames by calculating the movement velocity. Especially, it regresses virtual positions for missed or occluded vehicles and keeps on tracking these vehicles while they reappear. Experimental results on Jilin-1 and SkySat satellite videos demonstrate the effectiveness of the proposed detection and tracking methods.

Semantic labeling is an essential but challenging task when interpreting point clouds of 3D scenes. As a core step for scene interpretation, semantic labeling is the task of annotating every point in the point cloud with a label of semantic meaning, which plays a significant role in plenty of point cloud related applications. For airborne laser scanning (ALS) point clouds, precise annotations can considerably broaden its use in various applications. However, accurate and efficient semantic labeling is still a challenging task, due to the sensor noise, complex object structures, incomplete data, and uneven point densities. In this work, we propose a novel neural network focusing on semantic labeling of ALS point clouds, which investigates the importance of long-range spatial and channel-wise relations and is termed as global relation-aware attentional network (GraNet). GraNet first learns local geometric description and local dependencies using a local spatial discrepancy attention convolution module (LoSDA). In LoSDA, the orientation information, spatial distribution, and elevation information are fully considered by stacking several local spatial geometric learning modules and the local dependencies are learned by using an attention pooling module. Then, a global relation-aware attention module (GRA), consisting of a spatial relation-aware attention module (SRA) and a channel relation-aware attention module (CRA), is presented to further learn attentions from the structural information of a global scope from the relations and enhance high-level features with the long-range dependencies. The aforementioned two important modules are aggregated in the multi-scale network architecture to further consider scale changes in large urban areas. We conducted comprehensive experiments on three ALS point cloud datasets to evaluate the performance of our proposed framework. The results show that our method can achieve higher classification accuracy compared with other commonly used advanced classification methods. For the ISPRS benchmark dataset, our method improves the overall accuracy (OA) to 84.5 % and the average F 1 measure ( AvgF 1 ) to 73.6 %, which outperforms other baselines. Besides, experiments were conducted using a new ALS point cloud dataset covering highly dense urban areas and a newly published large-scale dataset.

Automatic change detection from remotely sensed imagery is extremely important for many applications, including land use mapping. In recent years, a growing number of researchers have applied capable deep-learning methods to the research on change detection. The majority of deep learning-based change detection methods currently perform pixel-by-pixel classification at the original image scale, but they can hardly avoid the false changes caused by strong parallax effects and projected shadows, without considering the totality of changed objects/regions. In this study, we propose an object-level change detection framework to detect changed geographic entities (such as newly built buildings or changed artificial structures) by paying more attention to the overall characteristics and context association of changed object instances. The detected changed objects are represented as bounding boxes, which are simple, regular, and convenient to use in object feature extraction. In terms of data handling, a special data augmentation method for change detection called Alternative-Mosaic is proposed to effectively accelerate model training and improve model performance. For the model, we propose a one-stage change detection network called dual correlation attention-guided detector (DCA-Det) to detect the changed objects. In particular, we feed the dual-temporal images into a weight-shared backbone network to extract the change features of different scales. The change features on the same scale are further refined, and then the features between different scales are fused by the correlation attention-guided feature fusion neck. Finally, the change detection heads output the prediction results of the changed objects/regions of different scales. Experiments were conducted on public LEVIR building change detection and aerial imagery change detection (AICD) datasets. The quantitative evaluation and visualization results proved the superiority and robustness of our framework. Our DCA-Det can obtain state-of-the-art performance on object-level metrics (99.50% AP IoU = . 50 and 79.72% AP IoU = . 50 : . 05 : . 95 ) on the AICD-2012 dataset.

Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset., Comment: 35 pages. Published by ISPRS Journal of Photogrammetry and Remote Sensing

Semantic segmentation of 3D Light Detection and Ranging (LiDAR) indoor point clouds using deep learning has been an active topic in recent years. However, most deep neural networks on point clouds conduct multi-level feature fusion via a simple U-shape architecture, which lacks enough capacity on both classification and localization in the segmentation task. In this paper, we propose a Neural Architecture Search (NAS) method to search a Feature Pyramid Network (FPN) module for 3D indoor point cloud semantic segmentation. Specifically, we aim to automatically find an effective feature pyramid architecture as a feature fusion neck in a designed novel pyramidal search space covering all information communication paths for multi-level features. The searched FPN module, named SFPN, contains the most important connections among all the potential paths to fuse representations at different levels. Our proposed SFPN is generic and effective as well as capable to be added to existing segmentation networks to augment the segmentation performance. Extensive experiments on ScanNet and S3DIS show that consistent and remarkable gains of segmentation performance can be achieved by different classical networks combined with SFPN. Specially, PointNet++-SFPN achieves mIoU gains of 7.8% on ScanNet v2 and 4.7% on S3DIS, and PointConv-SFPN achieves 4.5% and 3.7% improvement respectively on the above datasets.

Change detection in remote sensing images aims to accurately determine any significant land surface changes based on acquired multi-temporal image data, being a pivotal task of remote sensing image processing. Over the past few years, owing to its powerful learning and expression ability, deep learning has been widely applied in the general field of image processing and has demonstrated remarkable potentials in performing change detection in images. However, a majority of the existing deep learning-based change detection mechanisms are modified from single-image semantic segmentation algorithms, without considering the temporal information contained within the images, thereby not always appropriate for real-world change detection. This paper proposes a High-Resolution Triplet Network (HRTNet) framework, including a dynamic inception module, to tackle such shortcomings in change detection. First, a novel triplet input network is introduced, which is capable of learning bi-temporal image features, extracting the temporal information reflecting the difference between images over time. Then, a network is employed to extract high-resolution image features, ensuring the learned features preserving high-resolution characteristics with minimal reduction of information. The paper also proposes a novel dynamic inception module, which helps improve the feature expression ability of HRTNet, enriching the multi-scale information of the features extracted. Finally, the distances between feature pairs are measured to generate a high-precision change map. The effectiveness and robustness of HRTNet are verified on three popular high-resolution remote sensing image datasets. Systematic experimental results show that the proposed approach outperforms state-of-the-art change detection methods.

The extensive amount of Earth observation satellite images available brings opportunities and challenges for land mapping in global and regional scales. These large data sets have motivated the use of satellite image time series analysis coupled with machine learning techniques to produce land use and cover class maps. To be successful, these methods need good quality training samples, which are the most important factor for determining the accuracy of the results. For this reason, training samples need methods for quality control of class noise. In this paper, we propose a method to assess and improve the quality of satellite image time series training data. The method uses self-organizing maps (SOM) to produce clusters of time series and Bayesian inference to assess intra-cluster and inter-cluster similarity. Consistent samples of a class will be part of a neighborhood of clusters in the SOM map. Noisy samples will appear as outliers in the SOM. Using Bayesian inference in the SOM neighborhoods, we can infer which samples are noisy. To illustrate the methods, we present a case study in a large training set of land use and cover classes in the Cerrado biome, Brazil. The results prove that the method is efficient to reduce class noise and to assess the spatio-temporal variation of satellite image time series training samples.&nbsp

Terrestrial laser scanning (TLS) is established as a viable means for precision measurement and the need for systematic error modelling and instrument self-calibration is well recognized. While additional parameter (AP) models and procedures for their estimation from signalized target fields have been developed, the first-order design (FOD) of TLS self-calibration networks remains an active area of research aiming to improve AP quality. The conventional FOD approach of numerical simulation carries a heavy computational burden. This paper reports a new method for TLS self-calibration FOD that avoids the high computational effort and can predict AP precision in closed form. Its basis is a relatively simple analytical model of the distribution of spherical coordinate observations, specifically the elevation angle. The accuracy of predicted AP precision is quantified by comparison of precision estimates from a more complex and detailed observation distribution model and from self-calibration. Results from 25 datasets demonstrate the high accuracy (arc second or better) of the closed-form approach. A new observation distribution model is then developed to optimize the geometric design of TLS self-calibration networks. An ideal observation distribution based on the versine function and a corresponding target field configuration that enhance AP precision are established. Testing was performed on five additional, very dense TLS self-calibration datasets. Each dataset was subsampled so as to replicate the observation distributions corresponding to conventional network design and the proposed design. The results show that up to 55% improvement in AP precision, obtained from self-calibration, can be achieved with the new design and these results agree with versine-distribution model predictions within 14–16%.

The thick cloud coverage phenomenon severely disturbs optical satellite observation missions (covering approximately 40–60% areas in the global scale). Therefore, the manner by which to eliminate thick cloud in remote sensing imagery is greatly significant and indispensable. In this study, we combine the deep spatio-temporal prior with low-rank tensor singular value decomposition (DP-LRTSVD) for thick cloud removal in multitemporal images. On the one hand, DP-LRTSVD utilizes the low-rank characteristic of multitemporal images via the third-order tensor SVD and completion. On the other hand, DP-LRTSVD employs the deep spatio-temporal feature expression ability by 3D convolutional neural network. The proposed framework can effectively eliminate thick cloud in multitemporal images through combining the model-driven and data-driven strategies. Moreover, DP-LRTSVD outperforms on thick cloud removal in the simulated and real multitemporal Sentinel-2/GF-1 experiments compared with model-driven or data-driven methods. In contrast with most methods that can only use a single reference image for thick cloud removal, the proposed method can simultaneously eliminate thick cloud in time-series images.

Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Filed (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches.

Vernacular architecture’s use of passive building techniques has been developed in response to the prevailing climatic conditions of the past. Today, buildings are designed with complete indifference to the climate and materials. Energy consumption in buildings is mainly connected to space cooling, supply of hot water, lighting, cooking and, use of appliances. Space cooling and lighting are directly influenced by the design of the building. Energy codes should be concerned with ‘sufficiency’ as well as ‘efficiency’. A more economic and ecological approach to building design is needed.

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAIB) next to green LAI (LAIG). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAIG and LAIB, providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAIG: R2 = 0.7, RMSE = 0.67 m2/m2; LAIB: R2 = 0.62, RMSE = 0.43 m2/m2). Thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAIG and LAIB can be achieved. To demonstrate the capability of LAIB to identify when crops start senescing, S2 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAIB product permits the detection of harvest (i.e., sudden drop in LAIB) and the determination of crop residues (i.e., remaining LAIB), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAIG and LAIB estimates, and then compared to the LAI derived from S2 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.

An experimental and modeling comparison of optimal running conditions and energy consumption of shell and tube condenser and surface condenser which can be used for water distillation in conjunction with a Parabolic Trough Collector (PTC)-type solar energy system is reported in this research paper. A PTC of dimensions 4.5 m × 4.8 m with the aperture area of 20.3 m2 was used in this study. Further, 15 number of modeling equations were utilized in this research. The calculated distilled water production capacity of the system was 55.6 $$\mathrm{l}$$ /day, by considering solar irradiation, sun available time, and solar energy harnessing system efficiency as 0.9 kW m−2, 4 h, and 50%, respectively. The cooling water input temperature was about 32 °C throughout the research period. For the surface condenser, the optimum flow rate was found to be 8.5 $$\mathrm{l}$$ /min while it was not possible to find any optimum or safe running flow rate for the traditional condenser up to the flow rate of 60 $$\mathrm{l}$$ /min, due to the occurrence of sudden vacuumed pulses of about 85 Hg mm. These vacuumed pulses arose due to pressurized steam pushing out the blocked condensate layer from tubes. The traditional condenser was modified by inclining it by 5° to horizontal and dipping the condensate outlet in a water bath. With that modification, the optimum flow rate was found to be 30 $$\mathrm{l}$$ /min. The performance of surface condenser, the production rate of the condensate was 4% higher, and the consumption of the cooling water was 28% lower as compared to the modified traditional condenser. The effect of its sump level on the performance was also studied, and it revealed the condensation level in the sump of the surface condenser should be maintained in between ¼ and ¾ from the total height of the sump.

In this paper, a modified thermodynamic equation to predict interfacial tension (IFT) at oil/water interfaces with the presence of ionic surfactants and salt is considered. The surfactant activity coefficient is optimized based on the Extended UNIQUAC model according to the experimental results and is used in the modified model. In this study, the effect of salt with the presence of surfactants such as DTAB, M12 L12, SDBS and PFSA in the water/oil system has been considered to investigate the interfacial tension. According to the results of this study, the proposed equation can present a reasonable description of oil/water IFT measurements in the presence of cationic or anionic surfactant solutions and in a range of added salt.

The latest release of MODFLOW 6, the current core version of the MODFLOW groundwater modeling software, debuted a new package dubbed the "mover" (MVR). Using a generalized approach, MVR facilitates the transfer of water among any arbitrary combination of simulated features (i.e., pumping wells, stream, drains, lakes, etc.) within a MODFLOW 6 simulation. Four "rules" controlling the amount of water transferred from a providing feature to a receiving feature are currently available. In this way, MVR can represent natural connections between features, for example, streams entering or exiting lakes, and perhaps more interestingly, it also can transfer water among simulated features to more accurately simulate water management. An example model representative of an agricultural setting demonstrates some of the available MVR connections. For example, an irrigation event that transfers surface water from an irrigation delivery ditch to multiple cropped areas demonstrates a "one-to-many" connection that is possible within MVR. Conversely, irrigation or precipitation runoff from multiple fields may be routed to a particular stream segment using "many-to-one" MVR connections. MVR supports many additional connection types, several of which are demonstrated by the included example problem.

In the paper, the use of cartographic research method for the analysis and generalization of materials describing extreme events associated with rarely recurring high water and flooding in river basins of the Upper Ob is proposed. Basing on statistical data obtained from municipalities of the adjacent regions (Altai Krai, Republic of Altai, Kemerovo, Novosibirsk and Tomsk oblasts), including the information provided by territorial bodies of the RF Emergency Situations Ministry, we created a database for assessing actual and probable damage to population and economy of the study territories. Actual damage is generalized and analyzed in the context of river basins and municipalities of the entities, i.e. subjects of the Russian Federation. In order to assess probable damage, cartographic sources, including open data from the Internet as well as modern software tools were involved. The estimation of probable damage to population and economic objects was performed by the example of settlements (including those suffered from extreme flooding of 2014) located in the Charysh basin (Altai Krai). For that GIS projects and a series of damage evaluation maps were created. River basins of the Upper Ob regions were ranked according to recurrence frequency of extreme events and the extent of actual damage to population and economy there in 2004–2018. On the basis of these results, recommendations to prevent emergencies caused by the negative impact of water were developed.

Our study aimed to assess future climate changes in the region of Casablanca-Settat using downscaled outputs of two General Circulation Models (GCMs): the Canadian model (canESM2) and the National Centre for Meteorological Research (France) model (CNRM-CM5). These future climate changes were assessed through precipitation, minimum and maximum daily temperatures on annual and seasonal scales for three weather stations located at Casablanca-Settat region (Morocco). The temperature and precipitation changes are evaluated for two future periods: 2036–2065 (near term) and 2071–2100 (long term) relative to the present climate (1972–2001) using two emission scenarios RCP4.5 and RCP8.5. The results, evaluated according to the two above mentioned scenarios with respect to the baseline period 1972–2001, show a significant decrease for future projections of total rainfall, with percentages varying from one model to another. This decrease in precipitations affects all seasons. Concerning temperatures, the two models agree on a significant increase especially more marked during the summer and autumn. The decrease of rainfall and the increase of temperatures are in good agreement with the forecasts of the Intergovernmental Panel on Climate Change (IPCC, Climate change 2014: synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, 2014).

This essay projects how a plague may be transformed into post-infection resilience. Peering into the future after Covid-19 has subsided, global heating and the burgeoning demands of the population ...

Flowing bottom hole pressure (FBHP) is an indication of the fluid pressure in a porous reservoir under production. FBHP is obtained using a permanent gauge, well-testing analysis, or developing a mechanistic/correlation model. These conventional methods are costly and can be unreliable in the case of mechanistic/correlation approach. In line with this, the present study proposes the application of M5 prime for evaluating the FBHP of a well having a multiphase flow system. The proposed M5 prime FBHP model was further compared with backpropagation neural network (BPNN), generalised regression neural network (GRNN), radial basis function neural network (RBFNN), least-squares support vector machine (LSSVM), and group method of data handling (GMDH) to ascertain its performance. The M5 prime was identified as the superior technique as it attained the highest coefficient of determination (R2), correlation coefficient (R), the variance accounted for (VAF), and performance index (PI) values of 0.985, 0.99, 98.5, and 1.9, respectively, during testing. From the analysis of the contribution of each input variable, it was discovered that the tubing head pressure (THP) was the most dominant variable followed by the oil production flow rate (Qo), with the gas rate (Qg) having the least influence on the M5 prime outcome. Therefore, conclusion can be drawn that the proposed M5 prime model represents the best performing method as it can generate a more robust FBHP estimation while accounting for a superior variance of the measured data.