Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

High Accuracy Map Market Outlook

The global high accuracy map market size was valued at approximately USD 2.4 billion in 2023 and is projected to reach around USD 12.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This impressive growth is primarily driven by advancements in autonomous vehicle technology and increasing demand for precise geospatial data across various sectors. The rapid urbanization and increased investment in smart city projects worldwide are also significant factors contributing to market growth.

One of the primary growth factors fueling the high accuracy map market is the burgeoning development of autonomous vehicles. As the automotive industry continues to innovate, the need for high precision maps that provide detailed and real-time data on road conditions, traffic, and obstacles becomes more crucial. High accuracy maps enable autonomous vehicles to navigate safely and efficiently, reducing the likelihood of accidents and improving overall transportation systems. This demand is anticipated to surge further as governments and corporations strive to deploy autonomous vehicle fleets for both personal and commercial use.

Another significant driver of market growth is the increasing implementation of high accuracy maps in infrastructure development and urban planning. As cities expand and develop, the need for accurate and detailed geographic information systems (GIS) becomes essential for efficient planning and management. High accuracy maps provide critical data for designing and maintaining roads, bridges, utilities, and other infrastructure projects. The integration of high precision mapping technology in smart city initiatives further accelerates the adoption of these systems, enabling better resource management and enhanced quality of life for urban populations.

The agricultural sector is also contributing to the expanding high accuracy map market. Precision agriculture relies heavily on accurate geospatial data to optimize farming practices, enhance crop yields, and ensure sustainable resource use. High accuracy maps enable farmers to monitor field conditions, assess soil health, and implement targeted interventions, leading to increased productivity and reduced environmental impact. As the global demand for food continues to rise, the adoption of advanced mapping technologies in agriculture is expected to grow, driving further market expansion.

Regionally, North America holds a significant share of the high accuracy map market, driven by technological advancements and substantial investments in autonomous vehicle research and development. The presence of leading technology companies and a robust infrastructure network further facilitate market growth in this region. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing smart city projects, and rising adoption of advanced mapping technologies across various industries. Europe also remains a key player in the market, supported by strong governmental initiatives and a focus on sustainable development.

Component Analysis

The high accuracy map market can be segmented by component into software, hardware, and services. The software segment, encompassing map creation, data processing, and visualization tools, plays a critical role in the market. The demand for sophisticated mapping software is driven by the need for real-time data processing and the integration of multiple data sources to create comprehensive and precise maps. Companies are continually developing advanced software solutions that leverage artificial intelligence and machine learning to enhance the accuracy and functionality of high precision maps.

The hardware segment includes various devices and sensors used in capturing geospatial data, such as GPS units, LiDAR sensors, and high-resolution cameras. As the demand for high accuracy maps grows, the need for advanced hardware capable of capturing detailed and precise data also increases. Innovations in sensor technology and the development of more compact and cost-effective devices are contributing to the growth of this segment. The hardware segment is crucial for the initial data collection phase, which lays the foundation for accurate map creation.

Services encompass a wide range of offerings, including consulting, system integrati

The global High Accuracy Map market size is valued at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is primarily driven by the increasing adoption of autonomous vehicles, the growing demand for location-based services, and the rising popularity of intelligent transportation systems. The automotive industry is a major end-user of high-accuracy maps, as they are essential for the development of autonomous vehicles. The increasing adoption of smartphones and tablets has led to a surge in the demand for location-based services. These services require high-accuracy maps to provide users with precise location information. In addition, the rising popularity of intelligent transportation systems is also contributing to the growth of the high-accuracy map market. Intelligent transportation systems use high-accuracy maps to improve traffic flow, reduce congestion, and enhance safety. The key players in the high-accuracy map market include HERE Global B.V., Momenta, Emapgo, TomTom, Zenrin, Hyundai Mnsof, Baidu, AutoNavi, Navinfo, KOTEI Information Technology, Careland, Huawei, KuanDeng Technology, Leador, Beijing Lingtu Software Technology Co., Ltd., and ZTEmap. These players are investing heavily in research and development to improve the accuracy and precision of their maps.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 67.06 percent, this approach shows strong potential for generating crop type maps of current year in September.

Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data.

The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).

Files:

agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8) AGBstack.tif = covariates used for predicting AGB aggArea.tif = coarse grid used for simulation in the model-based methods ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids) OCSstack.tif = covariates used for predicting OCS strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples TOTmask.tif = mask of the area covered by the covariates

Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:

Name

Description

Source

Note

ai

Aridity Index

https://chelsa-climate.org/downloads/

    Version 2.1

bio1

Mean annual air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio5

Mean daily maximum air temperature of the warmest month [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio7

Annual range of air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio12

Annual precipitation [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio15

Precipitation seasonality [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

gdd10

Growing degree days heat sum above 10°C

    https://chelsa-climate.org/downloads/
    Version 2.1

clay

Clay content [g/kg] of the 0-5cm layer

https://soilgrids.org/

Only used for AGB

sand

Sand content [g/kg] of the 0-5cm layer

    https://soilgrids.org/
    as above

pH

Acidity (Ph(water)) of the 0-5cm layer

    https://soilgrids.org/
    as above

glc2017

Landcover 2017

https://land.copernicus.eu/global/products/lc, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water

Categorical variable

dem

Elevation

https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem

cosasp

Cosine of slope aspect

Computed with the terra package from elevation

    Computed @25m resolution; next aggregated to 0.5km

sinasp

Sine of slope aspect

    Computed with the terra package from elevation
    as above

slope

Slope

    Computed with the terra package from elevation
    as above

TPI

Topographic position index

    Computed with the terra package from elevation
    as above

TRI

Terrain ruggedness index

    Computed with the terra package from elevation
    as above

TWI

Topographic wetness index

Computed with SAGA from 500m resolution (aggregated) dem

gedi

Forest height

https://glad.umd.edu/dataset/gedi

Zone: NAFR

xcoord

X coordinate

Using a mask created from the other covariates

ycoord

Y coordinate

    Using a mask created from the other covariates

Dcoast

Distance from coast

Using a land mask created from the other covariates

ABSTRACT Land use capability is one of the most widespread technical-interpretative classification systems, however, regional adaptations may be necessary because different attributes may affect it. For these adaptations, the Minas Gerais soil map was used as the starting point for this study. The criteria to define the land use capability were adapted to management levels with small (level A) and medium (level B) application of capital and modern technology (level C). The aim of the present study was to map land use capability for Minas Gerais state, Brazil, following the criteria adapted to different levels of management and measure the accuracy of the resulting maps. The system of land use capability is widely used by INCRA in evaluations of rural properties. Erosion criterion was replaced by erodibility. The information was handled in a geographic information system. For validation, soil profiles from regional pedological surveys were sampled, classified, and its land use capability was compared to the land use capability shown on the map according to the different management levels. In spite of the small scale of the soil map, the maps of land use capability exhibited adequate accuracy: 73% (management level A), 71% (B), and 50% (C). Therefore, it can be applied in initial phases of regional planning studies, in which the level of details required is reduced (for example, in ecological-economic zoning). More detailed analyses still depend on detailed field surveys, as advocated by the system of land use capability.

Inventory survey on Kirwin National Wildlife Refuge to develop a current baseline vegetation map. The completed vegetation map will aid in facilitating evaluation of management alternatives, aid in the prioritization of management activities, and contribute to monitoring progress toward achieving Comprehensive Conservation Plan (CCP) objectives. This reference houses the final vegetation shapefiles, as well as the accuracy assessment point shapefiles, as a single zipped folder.

The high-accuracy map market is experiencing robust growth, driven by the increasing demand for advanced driver-assistance systems (ADAS) and autonomous driving technologies. The market's expansion is fueled by the need for precise and up-to-date map data to enable safe and efficient navigation in complex urban and rural environments. The integration of high-accuracy maps into vehicles is crucial for features like lane-keeping assist, adaptive cruise control, automated parking, and ultimately, fully autonomous driving. Different map update frequencies, such as second-level and minute-level updates, cater to varying needs, with minute-level updates particularly crucial for dynamic environments and real-time traffic management. The market is segmented by application (commercial and passenger vehicles) and update frequency, reflecting the diverse requirements of different vehicle types and functionalities. Key players in this market are investing heavily in research and development to improve map accuracy, coverage, and update frequency, leading to increased competition and innovation. The market's growth is projected to continue at a significant Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). Geographic variations exist, with North America and Asia-Pacific expected to dominate the market due to the early adoption of autonomous vehicle technologies and extensive investment in infrastructure development. However, Europe and other regions are catching up rapidly, spurred by increasing government regulations supporting autonomous driving initiatives. Restraints to market growth include the high cost of creating and maintaining high-accuracy maps, the need for extensive data infrastructure, and potential challenges related to data security and privacy. Nevertheless, the long-term prospects for the high-accuracy map market remain extremely positive, driven by the accelerating adoption of autonomous driving and the ongoing development of connected car technologies.

Each cell shows the precision and recall for mapping simulated reads with varying amounts of polymorphism. SHRiMP was able to accurately map >46% of all reads with either 4 SNPs or 5 bp indels, despite the large number of sequencing errors in our dataset (up to 7% towards the end of the read).

Results and data associated with Lark et al. 2021: Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer.

Accuracy data for original, non-aggregated CDL classes are located in the following files:

'NASS_CDL_National_20XX_original_accuracy' files contain each year's data and calculation results for original, non-aggregated CDL classes

'Nationwide_original_CDL_accuracy_2008to2017_w_ref_area' is the nationwide reference producers and users accuracy for average crops, average non-crops, and the individual classes for all years.

'State_level_annual_ref_acreage' is the percent of reference acreage for both crops and non-crops by each state and national total for all years, used for the area-weighting calculations.

Accuracy data for aggregated CDL classes are located in the following files:

'NASS_CDL_National_20XX_superclass_accuracy' files contain tables for each year that include the total and reference acreage of the datasets, the users and producers superclass accuracies for the individual classes, and consolidated crop and consolidated non-crop accuracy, as well as the raw and intermediate data used to calculate these results.

'Annual_CDL_superclass_accuracy_by_state_crop_noncrop' contains the average crop and non-crop superclass accuracies (i.e. the consolidated crop and consolidated non-crop accuracy) for the nation and for the individual states for all years.

Citation: Lark TJ, Schelly IH, Gibbs HK. Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer. Remote Sensing. 2021; 13(5):968. https://doi.org/10.3390/rs13050968

GWAS for six traits for SNPs from the UK Biobank arrays set, six traits for SNPs from the HapMap set, and three traits for SNPs from the ARIC study set.

Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or "best" hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present. Here we developed a generalized software toolkit "AlignerBoost", which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads. We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds. AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known SNPs. AlignerBoost’s algorithm is computationally efficient, and can process one million alignments within 30 seconds on a typical desktop computer. AlignerBoost is implemented as a uniform Java application and is freely available at https://github.com/Grice-Lab/AlignerBoost.

Noise mapping allows the characterization of environmental variables, such as noise pollution or soundscape, depending on the task. Strategic noise mapping (as per Directive 2002/49/EC, 2002) is a tool intended for the assessment of noise pollution at the European level every five years. These maps are based on common methods and procedures intended for human exposure assessment in the European Union that could be also be adapted for assessing environmental noise pollution in natural parks. However, given the size of such areas, there could be an alternative approach to soundscape characterization rather than using human noise exposure procedures. It is possible to optimize the size of the mapping grid used for such work by taking into account the attributes of the area to be studied and the desired outcome. This would then optimize the mapping time and the cost. This type of optimization is important in noise assessment as well as in the study of other environmental variables. This study compares 15 models, using different grid sizes, to assess the accuracy of the noise mapping of the road traffic noise at a landscape scale, with respect to noise and landscape indicators. In a study area located in the Manzanares High River Basin Regional Park in Spain, different accuracy levels (Kappa index values from 0.725 to 0.987) were obtained depending on the terrain and noise source properties. The time taken for the calculations and the noise mapping accuracy results reveal the potential for setting the map resolution in line with decision-makers' criteria and budget considerations.

Data to the article "Accuracy of Recording Linear Erosion using an Unmanned Aerial Vehicle (UAV)" (https://doi.org/10.1371/journal.pone.0329286)Data content: DEMs and orthomosaics of croplands as a result of photogrammetry using DJI P4 RTK. Measuring points of terrestrial surveys.

The high-accuracy mapping market is experiencing robust growth, driven by the increasing demand for advanced driver-assistance systems (ADAS), autonomous vehicles, and location-based services. The market's complexity necessitates highly precise maps for safe and efficient navigation, fueling its expansion. Let's assume a 2025 market size of $5 billion (a reasonable estimate given the involvement of major tech and automotive players) and a Compound Annual Growth Rate (CAGR) of 15% for the forecast period of 2025-2033. This implies significant market expansion, potentially reaching a value exceeding $15 billion by 2033. Key drivers include the proliferation of connected cars, the advancement of artificial intelligence (AI) for map creation and enhancement, and stringent government regulations promoting road safety. Trends point towards increased use of crowdsourcing, 3D mapping technologies, and the integration of high-accuracy maps with cloud-based platforms. While the market faces restraints such as high initial investment costs for map creation and maintenance, and challenges related to data security and privacy, the overall outlook remains highly positive. The segmentation within the market likely includes different map types (e.g., HD maps, vector maps), deployment methods (cloud-based, on-device), and applications (autonomous driving, navigation). The competitive landscape is characterized by a mix of established players like HERE Global B.V., TomTom, and Zenrin, and emerging technology companies, leading to constant innovation and competitive pricing. The continued integration of high-accuracy maps into various sectors like logistics, robotics, and smart cities promises further growth. The market is experiencing increased consolidation, with larger companies acquiring smaller specialists to expand their capabilities and market share. Furthermore, the growing focus on sustainable transportation and the development of infrastructure for autonomous vehicle deployment will significantly bolster market growth in the coming years. The high-accuracy mapping market is positioned for substantial growth, driven by technological advancements and the increasing reliance on location data across numerous industries. This growth trajectory is expected to continue, making it an attractive sector for investment and innovation.

The high-precision real-time mapping market is experiencing robust growth, driven by the increasing demand for autonomous vehicles, advanced driver-assistance systems (ADAS), and location-based services. The market's expansion is fueled by continuous technological advancements in sensor technology, improved data processing capabilities, and the development of sophisticated algorithms for creating and updating highly accurate maps. Key players like TomTom, Daimler, Google, HERE Technologies, and Mobileye are heavily investing in research and development, leading to innovative solutions that improve map accuracy, real-time updates, and data security. The market is segmented based on application (autonomous vehicles, ADAS, robotics, etc.), mapping technology (LiDAR, camera, GPS), and region. While the precise market size for 2025 is unavailable, considering a conservative CAGR of 15% (a reasonable estimate given industry growth trends), and assuming a 2024 market size of approximately $1.5 billion, we can project a 2025 market value near $1.7 billion. This growth trajectory is expected to continue through 2033, driven by increasing adoption across various sectors. The market faces challenges related to data privacy concerns, the high cost of map creation and maintenance, and the need for robust infrastructure to support real-time data transmission. However, these obstacles are being addressed through the development of efficient data processing techniques, cloud-based solutions, and the implementation of secure data management protocols. The increasing collaboration between mapping companies and automotive manufacturers is further streamlining the integration of high-precision maps into vehicles. Regional variations in market penetration exist, with North America and Europe currently leading in adoption, but emerging markets in Asia-Pacific are projected to witness significant growth in the coming years due to increasing investments in infrastructure and technology. The long-term forecast anticipates substantial market expansion, driven by the continued advancement of autonomous driving technology and the broadening application of real-time mapping across various industries.

For fire managers and industry professionals, monitoring and leading wildfire prevention efforts as well as reactionary efforts require accurate and operable fuel type maps to achieve effective management. Fuel type map classification accuracy has been seen to have varying values across industries and applications (> 10 %), and the consequences of these misclassifications in fuel type mapping has yet to be determined. The objective of this research was to explore the effects of mapping error on fire behavior metrics, burn probability, fire intensity, and rate of spread in the southern interior forest region of British Columbia which experiences dry weather and extreme fire conditions. Utilizing a fuel type map with 250 m resolution produced through an artificial neural network as a base case that has assumed 100% accuracy; induced error at varying levels of intensity (10%, 20%, 30%) was applied by selecting C-7 (conifer plantation/Ponderosa pine-Douglas-fir) pixels, and reassigning them to fuel types C-2 (Boreal Spruce) and M-1/2 (Boreal mixedwood) which have been commonly misrepresented in classification. With three levels of error and a base case for comparison, simulations were conducted through a spatial fire simulation software, Burn-P3, to determine effects. Clear trends were found to show that there was not a noticeable change in fire behavior metrics between the base case and 10 % error but that a relative inflection point was found between 10 percent and 20 percent. It was found that fire behavior metrics increased in intensity and spatial reach when fuel type mapping error increased. Recommendations for future research such as a complete evaluation of all error classes between 0% and 100%, as well as the implementation of map accuracy assessments are given to aid wildfire management efforts.

The high-precision mapping market is experiencing significant growth, driven by the increasing demand for autonomous vehicles, advanced driver-assistance systems (ADAS), and location-based services. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This robust growth is fueled by several key factors. The proliferation of autonomous vehicles necessitates highly accurate and detailed maps for safe and efficient navigation, significantly boosting demand. Furthermore, advancements in sensor technology, such as LiDAR and radar, are improving the quality and resolution of map data, enabling more sophisticated applications. The continuous development of high-definition (HD) maps, which provide centimeter-level accuracy, is another crucial driver. These maps are essential for features like lane-level navigation, precise object detection, and improved localization, all crucial for autonomous driving. However, the market faces certain restraints. High development and maintenance costs associated with creating and updating high-precision maps are a significant hurdle for market entry. Data privacy concerns and regulatory complexities related to the collection and usage of geographic data also pose challenges. Despite these challenges, the long-term prospects remain positive. The ongoing integration of high-precision maps into various sectors, including logistics, robotics, and smart cities, is expected to fuel market expansion. Segmentation within the market includes different map types (HD maps, 3D maps), applications (autonomous driving, ADAS, mapping services), and deployment models (cloud-based, on-premise). Key players like HERE, TomTom, Intel, NVIDIA, and Mobileye are driving innovation and competition within this dynamic landscape. The market's evolution is characterized by strategic partnerships, mergers and acquisitions, and continuous technological advancements aimed at improving map accuracy, coverage, and real-time updates.

a The root mean square error of prediction (RMSEP) indicates the overall accuracy, the R² indicates the precision, and the slope indicates the trueness of the models. The significance of the adjusted-R² was tested with a F test (*** p

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.