Raster Maps Research Articles

The SmartLandMaps Approach for Participatory Land Rights Mapping

Millions of formal and informal land rights are still undocumented worldwide and there is a need for scalable techniques to facilitate that documentation. In this context, sketch mapping based on printed high-resolution satellite or aerial imagery is being promoted as a fit-for-purpose land administration method and can be seen as a promising way to collect cadastral and land use information with the community in a rapid and cost-effective manner. The main disadvantage of paper-based mapping is the need for digitization to facilitate the integration with existing land administration information systems and the sustainable use of the data. Currently, this digitization is mostly done manually, which is time-consuming and error-prone. This article presents the SmartLandMaps approach to land rights mapping and digitization to address this gap. The recording involves the use of sketches during participatory mapping activities to delineate parcel boundaries, and the use of mobile phones to collect attribute information about spatial units and land rights holders. The digitization involves the use of photogrammetric techniques to derive a digital representation from the annotated paper maps, and the use of computer vision techniques to automate the extraction of parcel boundaries and stickers from raster maps. The approach was deployed in four scenarios across Africa, revealing its simplicity, versatility, efficiency, and cost-effectiveness. It can be regarded as a scalable alternative to traditional paper-based participatory land rights mapping.

Distributed Auto-Clustering for Residential Load Profiling Using AMI Data From the U.S. High Plains

Managing demand and building customized services are essential for power retailers, distribution network operators (DNOs), and load-serving entities (LSEs) in a competitive retail environment. Therefore, understanding residential customer power demand behaviors is essential for accurate load profiling. However, the large volume of fine-grained smart meter data and the wide variety of residential behavior make load profile extraction extremely challenging, even further exacerbated by deterministic clustering’s inherent limitations. This paper develops a distributed auto-clustering approach (DAA) for load profiling using real smart meter data based on a divide-and-conquer scheme to tackle current load profiling challenges. To improve the load profiling accuracy, temperature-based categories (TBCs) are proposed to distinguish days with similar temperatures. A Heterogeneous Distributed Local Sites (HDLS) scheme is developed based on the raster map concept to distribute smart meter data among local sites while considering their size and density. Local sites send the load representatives to a central site for global clustering to significantly reduce clustering complexity and computational burden. To verify the proposed DAA, approximately 11,000 residential 15-minute smart meter data provided by a utility company from the U.S. high plains were utilized in this paper.

Application and Research on Improved Adaptive Monte Carlo Localization Algorithm for Automatic Guided Vehicle Fusion with QR Code Navigation

SLAM (simultaneous localization and mapping) technology incorporating QR code navigation has been widely used in the mobile robotics industry. However, the particle kidnapping problem, positioning accuracy, and navigation time are still urgent issues to be solved. In this paper, a SLAM fused QR code navigation method is proposed and an improved adaptive Monte Carlo positioning algorithm is used to fuse the QR code information. Firstly, the generation and resampling methods of initialized particle swarms are improved to improve the robustness and weights of the swarms and to avoid the kidnapping problem. Secondly, the Gmapping scan data and the data generated by the improved AMCL algorithm are fused using the extended Kalman filter to improve the accuracy and stability of the state estimation. Finally, in terms of the positioning system, Gmapping is used to obtain QR code data as marker positions on static maps, and the improved adaptive Monte Carlo localization particle positioning algorithm is matched with a library of QR code templates, which corrects for offset distances and achieves precise point-to-point positioning under grey-valued raster maps. The experimental results show that the particles encountered with kidnapping can be quickly adjusted in position, with a 68.73% improvement in adjustment time, 64.27% improvement in navigation and positioning accuracy, and 42.81% reduction in positioning time.

A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration

Geochemical survey data provide rich information on geochemical elemental concentrations and their spatial patterns in relation to mineralization or pollution. A geochemical data cube can be generated using geochemical raster maps which can be obtained by interpolating geochemical samples into raster maps. In these maps, each pixel contains a geochemical spectrum that records the characteristics of a specific location. The spatial structure information that captures the relationships between neighboring pixels and the studied pixel is also visible in these raster maps. Therefore, the simultaneous consideration of the geochemical spectrum and spatial patterns is vital for mining a geochemical data cube for mineral exploration. In this study, a hybrid architecture of two deep learning algorithms, consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN), is proposed to extract joint spectrum–spatial features from geochemical survey data for supporting mineral exploration. A 1DCNN, in which the input data were pixels, was used to model the geochemical spectrum characteristics of the studied pixel. This involved all available geochemical major and trace elements and considered both positive and negative geochemical anomalies. A GCN with input data as graphs was used to capture the spatial patterns related to mineralization. A physically constrained deep learning model was then built by adding geological domain knowledge to the proposed hybrid model to improve the performance and interpretation of the hybrid model. A case study for identifying mineralization-related geochemical anomalies in northwestern Hubei Province, China, was employed to validate the proposed physically constrained hybrid deep learning model. Comparative studies with 1DCNN + GCN, 1DCNN, and GCN, the physically constrained hybrid method can effectively extract and fuse geochemical spectrum and spatial features hidden in geochemical survey data, and perform better in geochemical anomaly recognition.

Evaluating the Usability of a Gaze-Adaptive Approach for Identifying and Comparing Raster Values between Multilayers

Raster maps provide intuitive visualizations of remote sensing data representing various phenomena on the Earth’s surface. Reading raster maps with intricate information requires a high cognitive workload, especially when it is necessary to identify and compare values between multiple layers. In traditional methods, users need to repeatedly move their mouse and switch their visual focus between the map content and legend to interpret various grid value meanings. Such methods are ineffective and may lead to the loss of visual context for users. In this research, we aim to explore the potential benefits and drawbacks of gaze-adaptive interactions when interpreting raster maps. We focus on the usability of the use of low-cost eye trackers on gaze-based interactions. We designed two gaze-adaptive methods, gaze fixed and gaze dynamic adaptations, for identifying and comparing raster values between multilayers. In both methods, the grid content of different layers is adaptively adjusted depending on the user’s visual focus. We then conducted a user experiment by comparing such adaptation methods with a mouse dynamic adaptation method and a traditional method. Thirty-one participants (n = 31) were asked to complete a series of single-layer identification and multilayer comparison tasks. The results indicated that although gaze interaction with adaptive legends confused participants in single-layer identification, it improved multilayer comparison efficiency and effectiveness. The gaze-adaptive approach was well received by the participants overall, but was also perceived to be distracting and insensitive. By analyzing the participants’ eye movement data, we found that different methods exhibited significant differences in visual behaviors. The results are helpful for gaze-driven adaptation research in (geo)visualization in the future.

Estimating Groundwater Flow Velocity in Shallow Volcanic Aquifers of the Ethiopian Highlands Using a Geospatial Technique

The shallow volcanic aquifer is the major rural water supply source in the Ethiopian highlands. A significant number of hand pump wells in these aquifers experience a rapid decline in yield and poor performance within a short period of time after construction. Hence, reliable estimation of groundwater flow velocity is important to understand groundwater flow dynamics, aquifer responses to stresses and to optimize the sustainable management of groundwater resources. Here, we propose the geospatial technique using four essential input raster maps (groundwater elevation head, transmissivity, effective porosity and saturated thickness) to investigate groundwater flow velocity magnitude and direction in the shallow volcanic aquifers of the Ethiopian highlands. The results indicated that the high groundwater flow velocity in the Mecha site, ranging up to 47 m/day, was observed in the fractured scoraceous basalts. The Ejere site showed groundwater flow velocity not exceeding 7 m/day in the fractured basaltic aquifer and alluvial deposits. In the Sodo site, the groundwater flow velocity was observed to exceed 22 m/day in the fractured basaltic and rhyolitic aquifers affected by geological structures. The Abeshege site has a higher groundwater flow velocity of up to 195 m/day in the highly weathered and fractured basaltic aquifer. In all study sites, aquifers with less fractured basalt, trachyte, rhyolite, welded pyroclastic, and lacustrine deposits exhibited lower groundwater flow velocity values. The groundwater flow velocity directions in all study sites are similar to the groundwater elevation head, which signifies the local and regional groundwater flow directions. This work can be helpful in shallow groundwater resource development and management for rural water supply.

Vehicle-based LIDAR for Lane Line Detection

A lane line detection method based on adaptive thresholding of raster map is proposed for assisting driving by fitting lane lines in the process of automatic driving in ports. Firstly, the original laser point cloud is divided into regions of interest to retain the point clouds containing lane lines as well as to remove irrelevant point clouds. Secondly, the raster map is divided based on the intensity of the point cloud, and the ground points are filtered as well as the point cloud of the lane line part is retained. Then the lane lines are segmented by adaptive thresholding for the retained grid, and finally RANSAC is used to fit the lane lines. The experiments prove that the method can extract the lane line features well.

Estimation of water-infiltration rate in Mediterranean sandy soils using airborne hyperspectral sensors

The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure. Currently, few studies are based on field SSLs for the prediction of physical soil properties. This study used a field-based protocol to measure soil reflectance data and WIR simultaneously in the field, and generate spectral-based decision tree models to predict WIR solely from field spectral measurements using the SoilPRO® assembly. The obtained models were applied to both airborne hyperspectral (HySpex) and satellite multispectral (Sentinel 2) data on a pixel-by-pixel basis to generate raster maps of WIR. The study areas were located in Macedonia (Greece), and were optimal for mapping WIR because the soil crust was well developed, and sites were characterized by bare soils (no vegetation coverage) with a sandy structure. Whereas the WIR map generated with the satellite data was poor due to the low spatial and spectral resolution of Sentinel 2 (20 m, 9 bands), the results obtained with the airborne hyperspectral HySpex sensor (5 m, 408 bands) were satisfactorily validated in the ground-truth stage with good prediction accuracy due to high spatial and spectral resolution. Validation accuracy of the HySpex observations using all field samples gave R2 = 0.68, whereas the predictions of the ground-truth samples that were not part of the calibration stage (field validation group) of the model gave R2 = 0.59. We concluded that these results are favourable for rapid estimation of soil surface conditions and pave the way for a wider spatial view from orbital hyperspectral remote-sensing sensors.

Research on Full Coverage Path Planning Based on Reinforcement Learning in Nuclear Environment

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of [Formula: see text]-learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, [Formula: see text]-learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the [Formula: see text]-learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain.

Multi-robot raster map fusion without initial relative position

PurposeThe purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.Design/methodology/approachA fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.FindingsTo verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.Originality/valueThis algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

Land surface diversity: A geomorphodiversity index of Italy

AbstractGeomorphodiversity refers to the variety of landforms and morphological processes characterizing the landscape. The definition of an index to quantify geomorphodiversity is a relevant step for multiple fields of Earth sciences, since it is widely accepted that the variability of the geosphere deeply influences the diversity of the biosphere. Such an index should describe the number and type of landforms and geomorphological processes. We propose a quantitative land surface diversity index valid for Italy, considering multiple input quantities to describe geological constraints and geomorphological processes. Critical issues were the selection of moving window size for focal statistics operations, to calculate local diversity of slope, lithology, drainage density and terrain forms in individual raster maps. We compared the index with traditional geomorphological maps, in selected locations, in which information was available. Results show that a minimal set of heterogeneous data is a satisfactory approach to investigate the landscape diversity. Relating processing parameters and terrain spatial characteristics to the dataset resolution is a good choice to assess a reproducible land surface diversity index. Inclusion of drainage density allows the improvement of results in flat areas, in which other factors show trivial results. We argue that the index is relevant for land use management, assessment of ecodiversity, and it may help describing the interaction between abiotic and biotic compartments.

GIS Based Monitoring of Critical Area Upstream Watershed of PB. Soedirman Reservoir, Central Java, Indonesia

Upstream watershed has an important role as a conservation area to protect environmental functions for life. The damage of upstream watersheds causes various environmental problems such as soil erosion, landslides, river sedimentation, flooding, and others. Monitoring of upstream watersheds is important to control potential environmental damage. This study used a geographic information system (GIS) for monitoring the critical area upstream of the study site. The aim of this study is to identify critical areas in the watershed by using GIS. Six indicators presented in raster maps such as annual rainfall, soil type, land use type, land slope, population density, and water quality were used to determine the critical area. These indicators were then quantitatively analyzed by using spatial analysis tools of Arc GIS 10.6. Five classes of the critical category were used in this study namely very low, low, moderate, high, and very high. The result showed that 61.74% of the study site was covered by a low class of critical areas. Meanwhile, 37.89% and 0.36% of the study site were covered by moderate and high class critical areas, respectively. Areas with moderate and high class critical areas were found in the eastern part of the study site. The findings of this study provide a reference for determining priority areas for conservation planning.

A tool for detecting giant kelp canopy biomass decline in the Californias.

Kelp species provide many ecosystem services associated with their three-dimensional structures. Among these, fast-growth, canopy-forming species, like giant kelp Macrocystis pyrifera, are the foundation of kelp forests across many temperate reefs. Giant kelp populations have experienced regional declines in different parts of the world. Giant kelp canopy is very dynamic and can take years to recover from disturbance, challenging comparisons of standing biomass with historical baselines. The Santa Barbara Coastal LTER (SBC LTER), curates a time series of Landsat sensed surface cover and biomass for giant kelp in the west coast of North America. In the last decade, this resource has been fundamental to understanding the species' population dynamics and drivers. However, simple ready-to-use summary statistics aimed at classifying regional kelp decline or recovery are not readily available to stakeholders and coastal managers. To this end, we describe here two simple metrics made available through the R package kelpdecline. First, the proportion of Landsat pixels in decline (PPD), in which current biomass is compared with a historical baseline, and second, a pixel occupancy trend (POT), in which current year pixel occupancy is compared to the time-series long probability of occupancy. The package produces raster maps and output tables summarizing kelp decline and trends over a 0.25 × 0.25° scale. Using kelpdecline, we show how sensitivity analysis on PPD parameter variation can increase the confidence of kelp decline estimates.

Пространственно-временной анализ влажности почв сельскохозяйственных полей

Studies of the spatial-temporal variability of the field soil moisture content of three experimental fields located in the Perm district of the Perm region were carried out. The experimental conditions were pure fallow. The purpose of the work is to perform a spatio-temporal analysis of soil moisture in experimental fields using mathematical-cartographic and remote sensing methods. Field soil moisture was measured by points placed taking into account the heterogeneity of the soil cover and topography conditions. Based on the measurements performed, maps of field soil moisture were created using the “Ordinary Kriging” geostatistical method, using the Geostatistical Analyst module of the ArcGIS 10.1 program. The assessment of the influence of the relief on the moisture was performed using the SRTM global digital elevation model. Indicators calculated were topographic humidity index and closed depressions. Based on the obtained geostatistical layers, the relationship between humidity and Sentinel 2 satellite data was established through the spectral index NDVI. For the initial and final dates of measurements, correlation coefficients 0.8 and 0.9 were obtained. The nature of dependence turned out to be changeable over time. At the beginning of the growing season, the relationship is inverse, which confirms the indicator role of NDVI in moist areas, but starting from the third decade of May, the relationship becomes positive. This is due to the fact that the treatment of soils is not done every day, and seedlings appear earlier in the areas with the greatest moisture. Therefore, during the growing season, the field moisture content through the NDVI is indirectly identified by the value of the plant biomass. For individual measurement dates, a close correlation was established between NDVI raster maps and field humidity (r = 0.86). The resulting dependence can be used to predict soil moisture in similar areas. The results of the research will be used for differentiated sowing of seeds, taking into account different moisture conditions within the experimental fields.

Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse

In an intelligent distribution warehouse, latent AGVs are used for horizontal handling, and forklift AGVs are used for horizontal or vertical handling. Studying the path planning and task assignment problem when the two types of AGVs are mixed can help improve the warehouse operation efficiency and reduce the warehouse operation cost. This paper proposes a two-stage optimization method to solve this problem. In the first stage, the warehouse plan layout is transformed into a raster map, and the shortest path between any two points of the warehouse without conflict with fixed obstacles is planned and stored using the A* algorithm combined with circular rules, and the planned shortest path is called directly in the subsequent stages. In the second stage, to minimize the task completion time and AGV energy consumption, a genetic algorithm combining penalty functions is used to assign horizontal handling tasks to submerged AGVs or forklift AGVs and vertical handling tasks to forklift AGVs. The experimental results show that the method can meet the 24 h operation requirements of an intelligent distribution warehouse and realize the path planning and task assignment of forklift AGVs and latent AGVs. And furthermore, the number of AGVs arranged in the warehouse can be further reduced.

Research on inspection of machine room by mobile robot

The machine room inspection robot, as a special robot, can locate and navigate in the machine room independently, and carry sensors to detect equipment faults and record instrument data instead of the machine room staff. In this paper, the relevant standards and existing inspection robots at home and abroad were investigated and the overall scheme was determined based on the actual machine room environment. The monitoring background employs B/S architecture, allowing staff to deliver inspection tasks and manage robots from any computer. The motion map is created using the GMapping algorithm, and the topology map for inspection is created based on the inspection task and feasible path. For raster map localization, the adaptive Monte Carlo localization algorithm is used. The functional layer software for the robot is designed based on the inspection function to be performed by the robot and the man-machine interaction with the monitoring background. The findings of this paper's research indicate that the inspection robot system can meet the requirements of computer room autonomous inspection.

MENet: Map-enhanced 3D object detection in bird’s-eye view for LiDAR point clouds

Three-dimensional (3D) object detection utilizes numerous onboard sensors to determine the position, size, and motion information of surrounding objects. Recently, some researchers have utilized HD maps in 3D object detection for LiDAR point clouds. However, existing LiDAR–map fusion detection methods simply take the HD map as an additional input and more prior information in the map is underexplored. To address this limitation, we employ the HD map in data augmentation for LiDAR 3D detection to eliminate the bias of distributions between the augmented training and test sets. Furthermore, we propose a map-enhanced 3D object detection network, called MENet, for LiDAR point clouds. Compared with the standard LiDAR-only detector in bird’s eye view (BEV), MENet extends LiDAR-based baselines by using a map encoder to encode the input rasterized HD map into a BEV feature. In addition, it uses a multimodal fuser to aggregate the map BEV feature with the LiDAR BEV feature extracted in the LiDAR stream. MENet achieved a mean average precision (mAP) of 56.90% and nuScenes detection score (NDS) of 63.43%, improving the mAP and NDS of its LiDAR-only backbone by 4.18% and 2.22%, respectively. Although the HD map is necessary at training time, the map-enhanced 3D detector can work robustly and perform better than the original baseline when there was no map input at test time. Experimental results further indicate that the proposed map plugins can improve the performance of both anchor-based and anchor-free models on different datasets. The code will be open-source at https://github.com/WHU-USI3DV/MENet.

A separation and compaction algorithm for the two-open dimension nesting problem using penetration-fit raster and obstruction map

Nesting Problems, which are important subjects in the cutting and packing field, involve convex and nonconvex polygons and are common in several industries. These irregular open dimensional problems have been studied for decades, particularly the variant with one open dimension. However, in real-world applications, situations that are better suited to a two open dimensional model may arise and, in this sense, the literature is very limited. We here propose new separation and compaction algorithms for two-open dimension nesting problem. The paper develops an adaptation of the no-fit polygon to consider the penetration depth of pieces. The approach is based on an iterative compaction scheme, in which the key step is an obstruction map-based separation algorithm. The algorithms proposed found optimal solutions for artificial instances with up to 28 items within a small runtime. The results of benchmark instances indicate that the new algorithm is competitive when compared with other literature algorithms. It improved 14 of 15 benchmark instances when considering literature approaches on two open dimensions. In addition, the new algorithm achieved better occupation for some open dimension instances than the state of the art.

ROS-based Library Book Precise Positioning Guidance Robot

In order to solve the problem of book search and positioning in the library, this paper introduces a library book precise positioning guidance robot based on robot operating system (ROS). The robot has functions such as synchronous positioning and mapping, autonomous navigation and obstacle avoidance. First of all, the library book precision positioning guidance robot uses sensors such as lidar and cameras to realize the perception of the library environment, and at the same time uses image recognition technology to identify books. Secondly, the two-dimensional raster map of the library is drawn by using the SLAM algorithm, and the global and local optimal path planning and obstacle avoidance function are realized by using the Move-base function package, so as to realize the navigation and positioning tasks in the library.

FORECASTING THE ECOLOGICAL STATE OF LANDS IN REGIONS OF THE RUSSIAN FEDERATION FOR SUSTAINABLE DEVELOPMENT

An electronic raster map of the ecological state of lands of Russian regions was prepared for the reference period from 2001 to 2020 based on the characteristics of the primary photosynthesis products derived from Earth remote sensing materials from space in the form of indicators of «productivity», land transformation and dynamics of organic matter content in the soil and their generalizing indicator of sustainable development goals - SDG 15.3.1. Land degradation indicator. T e calculation shows that 2.2 million square kilometers, which constitutes 13% of the territory of Russian Federation, belong to the category of degraded by the end of 2020. 45% of the territory is occupied by land from the stable category, which has not changed over the past 19 years. 41% of the territory belongs to the lands that have improved during the reference period. A statistically reliable nonlinear regular relationship of the indicator of the ecological state of lands of Russian Federation was established in the form of an indicator of the share of non-degraded lands, considering the share of non-established lands with the amount of specif c emissions of pollutants into the atmospheric air from stationary sources located on the territory of the subject. T e threshold value of specif c emissions from stationary sources at the level of the subject of Russian Federation was established, amounting to 1610 kg per sq. km. T e probability of exceeding the threshold value of specif c emissions is 37%. T e risk of increasing land degradation with an increase in emissions is typical for 36% of territorial units of Russian Federation.