National Geospatial-Intelligence Agency Research Articles

Analysis of maritime piracy trends and patterns using spatial autocorrelation in Africa

ABSTRACT Many studies have analyzed causation factors for piracy, but failed to quantitatively examine how piracy trends in regions have changed. The research aimed to establish the changing patterns of maritime piracy in Africa. The study utilized world piracy data from the National Geospatial Intelligence Agency, which was analyzed using spatial auto-correlation tools. Getis-Ord Gi* statistic identified piracy clusters and hot spot comparison tool compared the hot spot layers in East and West Africa. Autocorrelation analysis results suggest changing frequency of piracy incidents in the study areas before and after 2012. The mean Getis-Ord Gi* values for East Africa were 0.35946 and 0.07839 before and after 2012, illustrating agreater change. However, West Africa mean values were 0.60917 and 0.43408 pre and post 2012, suggesting minimal change. The comparison analysis results generated a smaller similarity value for East Africa (0.43350) and a larger index for West Africa (0.9867). West Africa recorded many incidents in the study period; hence, piracy intensity was almost similar. The study revealed high similarities in piracy events in West Africa, and low similarities in East Africa pre and post 2012. These have implications for understanding the changing dynamics of maritime piracy in Africa, and asserting spatial analysis.

Моделювання впливу кліматичних змін на продуктивність агросектора

The article proposes a conceptual approach to modeling the impact of climate change on the productivity of the agrarian sector, substantiates the factors of climate change, and forms the information base of the study. Specifically, rainfall data was taken from the Climate Hazards Group InfraRed Precipitation with Station database (CHIRPS), which is a quasi-global dataset of precipitation amounts over 30+ years. CHIRPS incorporates 0.05° resolution satellite imagery together with data from local stations to create gridded rainfall time series to analyze trends and monitor seasonal drought. In order to obtain rainfall data for each of the selected countries, the Large-Scale International Borders (LSIB) database was used. This data is taken from two other datasets: the LSIB Lines Vector File and the World Vector Shoreline (WVS) from the National Geospatial-Intelligence Agency (NGA). Additional precipitation information was obtained using the Google Earth Engine after performing procedures with a sample of geolocation data for each country, obtaining actual precipitation data for the observation period, exporting the data and processing them to bring the results to the annual figure. The formed information base on the factors of climate change has allowed to develop models of productivity of grain production in the countries that are the largest wheat producers in the world. The results of the development of models led to the conclusion that climate change affects crop yields in countries that are leading producers of cereals. The obtained results can be used to predict changes in yield and production depending on climatic parameters such as temperature and precipitation, as well as to determine the optimal and extreme values of climatic factors.

On global maritime oil piracy: an association rules analysis

AbstractMaritime piracy is a global problem that disrupts oil flows and products. This research attempts to identify potential association rules between the features of hostile acts against tankers. To that end, the Apriori algorithm is used. We use data from the National Geospatial-Intelligence Agency and modify them in a basket case style. Essential features of an attack are examined, such as the closeness to the coasts, the cruising status of the tankers, and the time neighboring other attacks. Despite the perception we have of them, it turns out that pirates follow conservative strategies. It is found that success is strongly coupled with the safety of the attack by striking near the coast and on anchored tankers. The produced association rules include different time-neighboring conditions and no values regarding the attack month. This implies no strong preference for starting an attack, close or far from a previous episode, and no seasonality.

Discovering maritime-piracy hotspots: a study based on AHP and spatio-temporal analysis

Shipping is the backbone of international trade and oil companies want their oil tankers to arrive safely. The safety and security of international shipping of crucial elements such as oil has always been important aspect in the landscape of piracy. The implications of piracy attacks are linked with loss of cargo or personnel, economic and environmental catastrophe. Despite maritime piracy being a major concern to international trade, no comprehensive study considers the causing factors and spatio-temporal patterns that influence the choice of attack areas. Thus, this research expands our understanding on the areas where piracy mainly occurs, and its underlying causes. To achieve these objectives, AHP and spatio-temporal analysis applied by using data from the National Geospatial-Intelligence Agency. The results indicate that territorial waters are preferable areas; thus, pirates prefer to attack the ships close to the coastline fewer times near ports, and rarely in international waters. This is in line with the spatio-temporal analysis results that show that pirates except for the Arabian sea prefer to hit close to the coastline of countries that face political instability, lack of properly functioning government and extreme poverty. Moreover, pirates in certain areas are influenced by the activity and the information from other pirates, which can be used as tool from the authorities e.g., derive information from pirates that have been arrested. Overall, this study contributes on the literature of maritime piracy, and it could be used to enhance security and build tailored defense strategies in perilous water areas.

Measuring What Matters

For nearly eighty years, the National Geospatial-Intelligence Agency (NGA) has done its mapping—everything from the surface of the moon to the compound hiding Osama bin Laden—from the city of St. Louis. In 2017, this federal agency pitted three regional sites against each other in the competition to host their new billion-dollar headquarters. Two years later, a site of nearly one hundred acres of “underdeveloped brownfields” in North St. Louis was selected to go from “eyesore to economic boon” as the host of this moated and militarized spy headquarters. 1 This supposedly catalytic development eradicated nearly half of what was left of the St. Louis Place neighborhood following decades of erasure from urban renewal, malignant neglect, and unethical real estate transactions. While the project was touted as a massive win for St. Louis due primarily to preserving city income tax dollars, this essay examines how existing measures of success that conflate economic growth (or even simply economic preservation) with progress fail to capture the real social, environmental, and economic costs that development can bring. It considers how the difficult-to-measure aspects of city sociality—rootedness, optimism, opportunity, and social resilience, for example—are critical in recognizing and elevating the importance of spatial justice in our design decisions and design pedagogy. This paper explores the true costs of the NGA relocation to North St. Louis by measuring a broad range of social and economic factors ignored by the city’s assessment. It also considers a second adjacent project, the proposed Brickline Greenway, and explores how its more inclusive objectives begin taking small steps towards repair. Speculative design work from the Master of Urban Design studio at Washington University in St. Louis provides alternative mapping and design solutions that further this paradigm shift.

Detection of Anomalies in Traffic Flows with Large Amounts of Missing Data

Anomaly detection plays an important role in traffic operations and control. Missingness in spatial-temporal datasets prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of large amounts of data. This paper proposes an anomaly detection scheme for the 2021 Algorithms for Threat Detection (ATD) challenge based on Gaussian process models that generate features used in a logistic regression model which leads to high prediction accuracy for sparse traffic flow data with a large proportion of missingness. The dataset is provided by the National Science Foundation (NSF) in conjunction with the National Geospatial-Intelligence Agency (NGA), and it consists of thousands of labeled traffic flow records for 400 sensors from 2011 to 2020. Each sensor is purposely downsampled by NSF and NGA in order to simulate missing completely at random, and the missing rates are 99%, 98%, 95%, and 90%. Hence, it is challenging to detect anomalies from the sparse traffic flow data. The proposed scheme makes use of traffic patterns at different times of day and on different days of week to recover the complete data. The proposed anomaly detection scheme is computationally efficient by allowing parallel computation on different sensors. The proposed method is one of the two top performing algorithms in the 2021 ATD challenge.

Transforming between WGS84 Realizations

The World Geodetic System 1984 (WGS84) has been consistent with the International Terrestrial Reference Frame (ITRF) since 1994. After the original release of WGS84 in 1987 by the Defense Mapping Agency (since superseded by the National Geospatial-Intelligence Agency, NGA), there have been six subsequent realizations of the frame, designated WGS84 (G730), WGS84 (G873), WGS84 (G1150), WGS84 (G1674), WGS84 (G1762), and WGS84 (G2139). Users of WGS84 often fail to document metadata indicating the realization and epoch related to their data collections. As a result, when transforming between realizations or between other reference frames, the transformation can lead to incorrect results. We aim to bring awareness to the nature of WGS84 and the ambiguity in how it is defined. An internally consistent set of transformation parameters is derived that allows for transformation between the WGS84 realizations and other global reference frames while accounting for nuances and inconsistencies in the published literature. The derivations are based on existing (though not easily discoverable) information. Transformation accuracy is evaluated through comparison to other software and NGA-published GPS tracking station coordinates. After accounting for plate tectonic motion, the estimated uncertainties average ±0.03 m horizontally and ±0.08 m vertically for transformations involving all realizations other than the original WGS84. Uncertainties are at the meter level for transformations that include the original WGS84. If plate motion is ignored, the horizontal uncertainties can increase by several decimeters, depending on location within a plate.

Making progress in determining quality: positional accuracy of the new 1:25,000 scale digital cartography

Abstract. The digital cartographic coverage at 1:25,000 that the Military Geographic Institute is creating has been worked on using international standards, so that it constitutes a standardized and interoperable tool, for the various areas of activity in Chile. In this context, the ISO TC 211 standards and the TDS (Topographic Data Store) data model developed by the National Geospatial-Intelligence Agency (NGA) are being used.Apart from using these standards, efforts have been aimed, from an early stage, at the determination of the quality of this product, starting this process with the study for a methodology to measure Positional Accuracy. The method defined conforms to the NSSDA test; for this, points measured in the terrain especially for this control are used, also the elimination of points that are out of range under the Chauvenet Criteria. Finally, the positional accuracy is declared in the metadata.

Subsurface geological model of Malay basin using free air anomaly

The aim of gravity survey is to assist in the detection and delineation of subsurface geological features such as salt domes and faults. In this study, free air anomaly (FAA) data was adopted for mapping and modelling process to delineate subsurface geological features and basement depth in Malay Basin. FAA is the measured gravity anomaly after a free air correction is applied, and it is used for elevation correction. The data of FAA in this study is obtained from Earth Gravitational Model (EGM) 2008 released by the National Geospatial-Intelligence Agency (NGA)-EGM Development Team. Oasis Montaj software was used in the mapping and modelling process whereby the base map which constructed by the Oasis Montaj is used to form the FAA map of Malay Basin. Typically, the positive anomaly is associated with the high-density intrusion at the base of the crust, while in contrast (negative anomaly), it is related to the sedimentary basin in the upper crust. On top of that, the regional-residual anomaly, total horizontal derivative (THD) and 3D Euler Deconvolution enhanced maps were produced and interpreted to acquire comprehensive insight of subsurface geological features. To conclude, this study showed 5% deviation as compared to previous reported works and the deepest basement depth encountered is 14.5 km.

Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.

GENERIC LINEAR ARRAY SCANNER MODELING OF SPECTRAL IMAGING SYSTEMS CONTAINING LIMITED METADATA

Abstract. The National Geospatial-Intelligence Agency (NGA) designed the Generic Linear Array Scanner (GLAS) model for geopositioning images from both airborne and spaceborne linear array scanning systems, including pushbroom, whiskbroom, and panoramic sensors. Providers of hyperspectral imagery (HSI) historically have not populated products with high fidelity metadata to support downstream photogrammetric processing. To demonstrate recommended metadata population and exploitation using the GLAS model, NGA has generated example HSI products using data collected by NASA’s EO-1 Hyperion sensor and provided courtesy of the U.S. Geological Survey. This paper provides novel techniques for: 1) generating reasonably accurate initial approximations for GLAS metadata as a function of per-image metadata consisting of only timing information and the latitude and longitude values of the four corners of the image; and 2) identifying a vector of adjustable parameters and reasonable values for its a priori error covariance matrix that enable corrections to the metadata during a bundle adjustment. The paper describes applying these techniques to fourteen overlapping Hyperion images of the Alps, running a bundle adjustment as a function of tie points and optional ground control points, and demonstrating superior results to the previous polynomial based approach as quantified by the 3D errors at several ground check points.

Varied Incident Rates of Global Maritime Piracy: Toward a Model for State Policy Change

Maritime piracy is a problem that plagues our navigable seas. This study provides a quantitative understanding of factors that influence the frequency of yearly maritime piracy occurrences. Using a nonprobability sampling technique, 11 countries with reported cases of maritime piracy were purposefully selected. Open sourced data from the National Geospatial Intelligence Agency, the World Bank, the United Nations, the National Consortium for the Study of Terrorism and Responses to Terrorism, and the Center for Systemic Peace were utilized in this study. Using regression features, as well as socioeconomic and geopolitical data from these countries over a longitudinal period of 34 years (1985–2018), a time-series cross-sectional design provides an in-depth understanding of factors that affect the frequency of piracy occurrences. The four-factor model concluded that state weakness, total population size, gross domestic product, and total fish catch tonnage are found to be associated with the frequency of pirating activity worldwide. Policy implications and measures to mitigate piracy are also discussed.

Assessment of ArcGIS based extraction of geoidal undulation compared to National Geospatial Intelligence Agency (NGA) model – A case study

Abstract Global Navigation Satellite System (GNSS) and remote sensing Digital Elevation Models (DEMs) represent earth’s surface elevation with reference to ellipsoid and orthometric heights. Proper estimation of the geoid (difference of ellipsoid and orthometric heights) is necessary before comparing data referenced to the different vertical datum. In this paper, an error in estimating EGM96 orthometric height is highlighted, verified by NGA/NASA developed model and MATLAB®. A significant error was found in the ArcGIS derived EGM96 orthometric heights range between ±6.9 meters. In addition, interpolation of low-resolution geoid data also produces significant biases depending on geographic location and the number of the interpolation data point. The bias was maximum negative in the central part of Tibetan Plateau and Himalaya. Therefore, estimation of orthometric height similar to NGA/NASA model precision is necessary for comparison of DEMs for natural resources management, 3D modelling and glaciers mass balance mainly in the mountainous regions.

Accuracy assessment of the PGM17 global geopotential model: a case study of Egypt and Northeast Africa

Global geopotential models play a fundamental role in height transformation to convert the Global Navigation Satellite Systems (GNSS)-based ellipsoidal heights into orthometric heights in several geosciences applications. The U.S. National Geospatial-Intelligence Agency (NGA) is preparing to release the Earth Geopotential Model 2020 (EGM2020) in the near future. Prior to that official release, two primary geopotential models (PGM17 and PGM18) will be distributed to selective members for assessment in different regions worldwide. Within the activities of that working group, the new PGM17 geopotential model has been obtained and judged over a regional scale in northeast Africa, and over a national basis of Egypt. The assessment has been carried out using terrestrial gravity and GPS/leveling datasets within a GIS environment. The utilized dataset consists of 231 terrestrial gravity points in northeast Africa, 247 measured gravity points in Egypt, and 978 GNSS/leveling stations in Egypt. Three other GGMs have been included in the comparison stage, namely GECO, EIGEN-6C4, and EGM2008. For each model, grids of gravity anomalies and geoidal undulations have been computed and, then, compared against the corresponding values at known terrestrial data. Next, statistical measures of mean, range, and standard deviations of the discrepancies have been analyzed. For northeast Africa area, the accomplished results indicated that the PGM17 attains 100% improvements over the old EGM2008, while the GECO, and EIGEN-6C4 models resulted in improvements of 11%, and 1% respectively. Accordingly, it can be concluded that PGM17 represents the gravitational field over northeast Africa in a more enhanced manner. On a national scale, the GGMs assessment has been performed, over Egypt, in two steps: using gravity data, and using GNSS/leveling data. The findings emphasize that PGM17 noticeably enhance the performance of EGM2008, when compared against terrestrial gravity stations in Egypt, by almost 55%. Furthermore, the assessment over GPS/leveling stations concluded that the overall improvements of PGM17 are approximately 54%. Other GGMs did not produce a comparable improvement level in both case studies. Thus, it can be concluded that PGM17 is significantly precise than the other investigated GGMs in representing the gravitational field over Egypt in particular and northeast Africa in general.

Accuracy of Geopotential Models Used in Smartphone Positioning in the Territory of Poland

Effective realisation of both land and sea transport has always required the determination of safe and fast routes. In the era of technological progress and modern technologies, the classic methods of navigation and paper maps have been practically replaced by satellite positioning and marine electronic maps. The degree of occurring changes is evident in the widespread use of mobile phones and tablets in car navigation and recreation. Among the determined geolocation data in these fields, only the ellipsoidal longitude and latitude of the receiver are most commonly used. Knowledge of these coordinates is sufficient to present the position on the maps available online or offline. The third of the coordinates, ellipsoidal height, finds particular application in some areas of recreation, such as mountain climbing or cycling to present the differences in elevation along the track. However, the ellipsoidal heights related to the reference ellipsoid, excluding some regions of the Earth, are not identical to the normal heights used widely in many European countries. The conversion of ellipsoidal heights into normal heights takes place through the use of geopotential models that are currently commonly implemented in the GNSS modules of smartphones. Geopotential models, which are discrete sets of quasigeoid spacing values from the reference ellipsoid, allow interpolation of the local undulation value added later on to the determined ellipsoidal height of the receiver. The publication will present the results of the conducted comparative analysis of geopotential models of selected telephones with the EGM2008 model developed by The National Geospatial- Intelligence Agency (NGA). The analysis will concern the results of measurements carried out in selected regions of Poland. The cohesion of both geopotential models will be shown, which directly translates into the correctness of the heights computed by the phone receiver. The conducted research proves the occurrence of significant distortions in the calculated values of undulations. The examined issue, in the perspective of the rapid development of personal navigation and the increasing accuracy of the receivers of mobile phones, is an important factor affecting the credibility of the positioning results.

Geo-Localization of Target in 3-Dimensional Coordinate Using SRTM Data

This paper deals with Estimation of 3-Dimensional Geo-Location Coordinate (Latitude, Longitude and Elevation) of a given target location using the image of a Target captured from a Gimballed Camera mounted on an Unmanned Aerial Vehicle (UAV). The 2-Dimensional Geo-Location Coordinate (Latitude and Longitude) is estimated by the application of Geo-Location Algorithm based on the knowledge of target pixel location in the image. The Target Elevation is estimated by Elevation Estimation Algorithm using the 2-Dimensional Geo-Location Coordinate of target and Elevation Database such as NASA Shuttle Radar Topography Mission (SRTM) Database, National Geospatial-Intelligence Agency (NGA) SRTM Database and Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2. This paper illustrates the Target Geo-Location Coordinate Estimation for two different experimental setup: UAV Simulation Setup and a Real Time Setup. The Geo-Location Estimation result obtained from the Simulation Setup is found to have localization error around 0.3 m, while the estimates from the Real Time Setup are found to have a higher localization error of around 2.3 m. The elevation result obtained using Elevation Estimation Algorithm for SRTM 1-Arc Second database is proved to be more accurate but this comes with a downside of having longer processing time when compared with other databases. NGA 3-Arc Second database provides the result with the shortest computation period.

The piracy terrorism paradigm: an interlinking relationship

ABSTRACTNon-state actors (i.e. pirates and terrorists) are an international security concern. Prior studies have examined the political and economic impact that pirates and terrorists inflict on our society. Few studies, however, have attempted to answer whether there is an association between them. The purpose of this study is to provide empirical support on the relationship between pirates and terrorists. Longitudinal data collected from the National Geospatial Intelligence Agency (NGIA) and the Global Terrorism Database (GTD) provided the researcher with incidents of maritime piracy and terrorism 1985–2014 in three countries: Indonesia, Nigeria, and Somalia. A Pearson's R bivariate statistical tool was selected to determine if these incidents co-vary. The model summaries illustrate that acts of piracy and terrorism positively co-varied, with two models inferring statistical significance. Policy implications and recommendations will be discussed. These recommendations include increasing the number of naval patrols and strengthening local institutions.

Short note: the experimental geopotential model XGM2016

As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017. doi: 10.1007/s10712-016-9406-y). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set of \(15^\prime \times 15^\prime \) gravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.

Quantification of land cover and land use within the rural complex of the Democratic Republic of Congo

The rural complex is the inhabited agricultural land cover mosaic found along the network of rivers and roads in the forest of the Democratic Republic of Congo. It is a product of traditional small-holder shifting cultivation. To date, thanks to its distinction from primary forest, this area has been mapped as relatively homogenous, leaving the proportions of land cover heterogeneity within it unknown. However, the success of strategies for sustainable development, including land use planning and payment for ecosystem services, such as Reduced Emissions from Deforestation and Degradation, depends on the accurate characterization of the impacts of land use on natural resources, including within the rural complex. We photo-interpreted a simple random sample of 1000 points in the established rural complex, using 3106 high resolution satellite images obtained from the National Geospatial-Intelligence Agency, together with 406 images from Google Earth, spanning the period 2008–2016. Results indicate that nationally the established rural complex includes 5% clearings, 10% active fields, 26% fallows, 34% secondary forest, 2% wetland forest, 11% primary forest, 6% grasslands, 3% roads and settlements and 2% commercial plantations. Only a small proportion of sample points were plantations, while other commercial dynamics, such as logging and mining, were not detected in the sample. The area of current shifting cultivation accounts for 76% of the established rural complex. Added to primary forest (11%), this means that 87% of the rural complex is available for shifting cultivation. At the current clearing rate, it would take ~18 years for a complete rotation of the rural complex to occur. Additional pressure on land results in either the cultivation of non-preferred land types within the rural complex (such as wetland forest), or expansion of agriculture into nearby primary forests, with attendant impacts on emissions, habitat loss and other ecosystems services.

Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery

Very high resolution (VHR) satellite data is experiencing rapid annual growth, producing petabytes of remotely sensed data per year. The WorldView constellation, operated by DigitalGlobe, images over 1.2billionkm2 annually at <2m spatial resolution. Due to computation, data cost, and methodological concerns, VHR satellite data has mainly been used to produce needed geospatial information for site-specific phenomena. This project produced a VHR spatiotemporally explicit wall-to-wall cropland area map for the rainfed residential cropland mosaic of the Tigray Region, Ethiopia, which is comprised mostly of smallholder farms. Moderate resolution satellite data do not have adequate spatial resolution to capture the total area occupied by smallholder farms, i.e., farms with agricultural fields of ≤45×45m in dimension. In order to accurately map smallholder cropped area over a large region, hundreds of VHR images spanning two or more years are needed. Sub-meter WorldView-1 and WorldView-2 segmentation results were combined with median phenology amplitude from Landsat 8 data to map cropped area. Over 2700 VHR WorldView-1, -2 data were obtained from the U.S. National Geospatial-Intelligence Agency (NGA) via the NextView license agreement and were processed from raw imagery to produce a smallholder crop map in ~1week using a semi-automated method with the large computing capacity of the Advanced Data Analytics Platform. We estimated cropped area in Tigray to be 46% with a commission error of 5%±10% and omission error of 15%±12%. This methodology is extensible to other regions with similar vegetation texture and can easily be expanded to run on much larger regions.