{"id":3706,"date":"2026-07-27T02:58:02","date_gmt":"2026-07-27T02:58:02","guid":{"rendered":"https:\/\/vertidronetech.com\/?p=3706"},"modified":"2026-07-27T03:00:15","modified_gmt":"2026-07-27T03:00:15","slug":"troubleshooting-drone-surveys-vertical-accuracy-errors-and-rtk-signal-loss","status":"publish","type":"post","link":"https:\/\/vertidronetech.com\/?p=3706","title":{"rendered":"Troubleshooting Drone Surveys: Vertical Accuracy Errors and RTK Signal Loss"},"content":{"rendered":"\n<div class=\"container\">\n    \n    <p>Real-Time Kinematic (RTK) technology has fundamentally changed the land surveying, civil engineering, and aerial mapping industries by providing centimeter-level horizontal positioning in real time. Despite deploying advanced enterprise hardware, field teams frequently encounter unexpected vertical discrepancies in their 3D terrain models and elevation datasets. A digital elevation model (DEM) or point cloud may perfectly align horizontally with ground control features, yet exhibit systematic vertical shifts of several meters or experience localized elevation dome distortions. Furthermore, operations in complex topography, dense urban environments, or remote corridors present continuous risks of mid-flight telemetry or correction signal interruptions. Understanding the underlying geodesy of vertical datum transformations, photogrammetric camera calibration dynamics, receiver positioning state transitions, and post-processing recovery protocols is essential for GIS managers and flight leads responsible for survey-grade deliverables.<\/p>\n\n    <!-- Table of Contents -->\n    <div class=\"toc\">\n        <h2>Table of Contents<\/h2>\n        <ul>\n            <li><a href=\"#why-does-my-vertical-accuracy-fail-even-with-an-rtk-drone\">1. Why does my vertical accuracy fail even with an RTK drone?<\/a><\/li>\n            <li><a href=\"#what-happens-when-an-rtk-drone-loses-signal-connection-mid-flight\">2. What happens when an RTK drone loses signal connection mid-flight?<\/a><\/li>\n            <li><a href=\"#geodetic-datum-mismatches-ellipsoid-vs-orthometric\">3. Geodetic Datum Mismatches: Ellipsoid Height vs. Orthometric Height<\/a><\/li>\n            <li><a href=\"#camera-calibration-and-lens-distortion-mechanics\">4. Camera Calibration, Focal Length Degeneration, and Model Warping<\/a><\/li>\n            <li><a href=\"#rtk-signal-degradation-and-ppk-recovery-workflows\">5. Mitigating RTK Signal Disruption via PPK and Check Point Verification<\/a><\/li>\n            <li><a href=\"#elevation-error-and-signal-state-matrix\">6. Vertical Error Sources and Mid-Flight Signal Loss Matrix<\/a><\/li>\n            <li><a href=\"#frequently-asked-questions\">7. Frequently Asked Questions (FAQ)<\/a><\/li>\n        <\/ul>\n    <\/div>\n\n    <h2 id=\"why-does-my-vertical-accuracy-fail-even-with-an-rtk-drone\">Why does my vertical accuracy fail even with an RTK drone?<\/h2>\n    <div class=\"featured-snippet-box\">\n        <p><strong>Answer for Featured Snippet:<\/strong> Vertical accuracy often fails with an RTK drone due to **geodetic datum mismatches (confusing Ellipsoid height with Orthometric Geoid height)**, **uncalibrated camera focal length parameters (lens distortion)**, **improper antenna phase center offsets**, or **excessive baseline distance from the correction source**. Addressing these systematic coordinate errors requires applying correct local geoid models (such as GEOID18) and incorporating rigid camera self-calibration or ground check points.<\/p>\n    <\/div>\n    <p>A common misconception in aerial photogrammetry is that equipping a drone with an RTK GNSS receiver automatically guarantees sub-centimeter vertical accuracy across all processed mapping deliverables. While RTK positioning locks the physical location of the drone&#8217;s antenna in space with exceptional precision, converting camera trigger locations into an accurate ground elevation model involves complex geometric, optical, and geodetic steps where errors can easily accumulate.<\/p>\n    <p>The primary cause of large, uniform vertical shifts (often ranging from 15 to 50 meters) is confusing satellite reference systems with local elevation datums. GNSS receivers natively measure altitude relative to a smooth, mathematical ellipsoid surface (such as WGS84 or NAD83). However, civil engineering projects and municipal survey maps rely on orthometric heights (Mean Sea Level), which account for variations in Earth&#8217;s gravitational pull relative to an irregular geoid model (such as GEOID18 or EGM96). If photogrammetry software interprets raw ellipsoidal metadata directly as orthometric height without applying a geoid conversion model, the resulting 3D point cloud will sit significantly higher or lower than actual ground surface benchmarks.<\/p>\n\n    <p>Beyond geodetic coordinate transformations, physical and optical factors frequently degrade vertical precision:<\/p>\n    <ul>\n        <li><strong>Focal Length and Radial Distortion Inaccuracies:<\/strong> Photogrammetry software estimates camera focal length ($f$) during automated bundle block adjustments. If an aerial dataset consists entirely of nadir (straight-down 90-degree) images captured along a flat plane without oblique angles, software algorithms struggle to decouple internal camera focal length parameters from absolute flight altitude. A tiny 0.1% miscalculation in focal length directly manifests as a vertical model error on the ground.<\/li>\n        <li><strong>Antenna Phase Center (APC) Offsets:<\/strong> Satellite positioning data is calculated at the exact phase center of the drone&#8217;s GNSS antenna module. The photogrammetric processing software must offset this position mathematically down to the camera&#8217;s optical focal center (the CMOS sensor). If the physical offset vector (the spatial distance between antenna and lens) is incorrectly configured in the flight control system or processing software, vertical errors will bias the entire model.<\/li>\n        <li><strong>GNSS Satellite Geometry (PDOP\/VDOP):<\/strong> Satellite constellation geometry naturally provides weaker vertical accuracy than horizontal positioning. Vertical Dilution of Precision (VDOP) is typically 1.5 to 3 times worse than Horizontal Dilution of Precision (HDOP) because all visible GPS satellites orbit above the drone&#8217;s horizon, leaving no lower-hemisphere satellite signals to balance the mathematical intersection equations.<\/li>\n    <\/ul>\n\n    <h2 id=\"what-happens-when-an-rtk-drone-loses-signal-connection-mid-flight\">What happens when an RTK drone loses signal connection mid-flight?<\/h2>\n    <div class=\"featured-snippet-box\">\n        <p><strong>Answer for Featured Snippet:<\/strong> When an RTK drone loses its differential correction signal mid-flight, its positioning state degrades from **RTK Fix** (centimeter precision) to **RTK Float** or **Standalone GPS** (meter-level precision). Depending on flight software settings, the aircraft will either hold its flight path while logging raw observations for post-processing, pause and wait for signal recovery, or execute an automated Return-to-Home (RTH) procedure if command telemetry is completely lost.<\/p>\n    <\/div>\n    <p>Maintaining a continuous, real-time differential correction link between an airborne RTK drone and its ground reference source\u2014whether a local physical base station transmitting via radio\/Wi-Fi or a cloud-based NTRIP server streaming over 4G\/5G cellular data\u2014is subject to real-world environmental disruptions. Intermittent cellular dead zones, physical terrain obstructions, electromagnetic interference, or baseline distance extensions can interrupt data packet delivery mid-mission. When signal loss occurs, the drone&#8217;s flight control computer and GNSS module execute automated safety and positioning fallback routines.<\/p>\n    <p>The immediate technical consequence of correction signal loss occurs within the drone&#8217;s onboard GNSS positioning engine. The system transitions through distinct mathematical operational states based on signal quality and loss duration:<\/p>\n\n    <h3>1. Transition from RTK Fix to RTK Float State<\/h3>\n    <p>During normal operations with an active correction stream, the receiver calculates integer carrier phase ambiguities, achieving an &#8220;RTK Fix&#8221; state with horizontal and vertical position uncertainties under 1 to 2 centimeters. If the differential correction stream drops out, the receiver can no longer solve these integer phase ambiguities definitively. It transitions to an &#8220;RTK Float&#8221; state, relying on carrier phase calculations without fixed integer resolution. Positioning uncertainty gradually widens from centimeters to tens of centimeters, and eventually to several meters if the outage persists over several minutes.<\/p>\n\n    <h3>2. Extrapolation Hold and Re-convergence Buffers<\/h3>\n    <p>Modern enterprise flight controllers (such as those found on DJI, Autel, and Quantum Systems platforms) incorporate short-term holding buffers. The flight computer uses internal inertial measurement units (IMUs) combined with historical satellite orbit data to maintain precise positioning during brief 10 to 30-second correction drops. If signal connectivity resumes within this window, the receiver re-establishes an &#8220;RTK Fix&#8221; state almost instantly without disrupting the mapping mission or corrupting photo geolocation accuracy.<\/p>\n\n    <h3>3. Extended Outages, Fail-safe Actions, and Raw Logging<\/h3>\n    <p>If the correction signal remains offline past the buffer window, the aircraft&#8217;s response depends on pre-configured mission parameters:<\/p>\n    <ul>\n        <li><strong>Continuous Mission Execution (PPK Fallback Mode):<\/strong> The drone continues flying its planned survey grid despite losing real-time correction data. Images captured during the outage are tagged with wider spatial uncertainties (Float state) in the metadata file. Crucially, the aircraft continuously logs raw satellite observation files (such as RINEX or proprietary DAT\/BIN files) internally, allowing the surveyor to restore centimeter accuracy after the flight using Post-Processed Kinematic (PPK) software.<\/li>\n        <li><strong>Hover and Re-connect Execution:<\/strong> The aircraft pauses automated navigation, hovers in place at its current altitude, and waits for the NTRIP or base station data link to re-establish within a designated timeout window (e.g., 60 seconds) before resuming the path.<\/li>\n        <li><strong>Automated Return-to-Home (RTH):<\/strong> If the loss of correction signals coincides with a total loss of primary command-and-control (C2) telemetry from the pilot&#8217;s remote controller, the aircraft activates safety protocols, climbing to a designated clear altitude and returning autonomously to its takeoff location.<\/li>\n    <\/ul>\n\n    <h2 id=\"geodetic-datum-mismatches-ellipsoid-vs-orthometric\">Geodetic Datum Mismatches: Ellipsoid Height vs. Orthometric Height<\/h2>\n    <p>Resolving vertical accuracy failures requires a solid understanding of coordinate reference systems (CRS) and the vertical datums that define elevation metrics across professional GIS and land surveying applications.<\/p>\n\n    <p>GNSS technology relies on purely mathematical reference surfaces. The Global Positioning System (GPS) uses the **WGS84** (World Geodetic System 1984) ellipsoid, an idealized, perfectly smooth mathematical oval representing the Earth&#8217;s average shape. The altitude value reported directly by an unadjusted GNSS receiver is the **Ellipsoidal Height ($h$)**\u2014the absolute vertical distance between the physical drone antenna and this smooth mathematical surface.<\/p>\n\n    <p>However, water flow, civil engineering drainage designs, construction grading, and traditional topographical maps do not conform to an artificial mathematical ellipsoid. They respond to gravity. The physical reference surface that reflects gravity is the **Geoid**\u2014an irregular, undulating equipotential surface that closely approximates Mean Sea Level (MSL). Elevation measured relative to the Geoid is defined as **Orthometric Height ($H$)**.<\/p>\n\n    <p>The relationship between these surface models is expressed through the fundamental geodetic equation:<\/p>\n\n    <p style=\"text-align: center; font-weight: bold; font-size: 1.1rem; margin: 25px 0;\">$$h = H + N$$<\/p>\n\n    <p>Where:<\/p>\n    <ul>\n        <li>$$h$$ = Ellipsoidal Height (measured directly by RTK GNSS receivers)<\/li>\n        <li>$$H$$ = Orthometric Height (true elevation relative to Mean Sea Level \/ local geoid)<\/li>\n        <li>$$N$$ = Geoid Undulation \/ Separation (the mathematical height difference between the geoid and the ellipsoid at a specific geographic coordinate)<\/li>\n    <\/ul>\n\n    <p>Because the Earth&#8217;s crust exhibits localized density variations and uneven mass distributions, the Geoid undulation ($N$) varies significantly across geographic regions. Across the continental United States, for example, the separation between the WGS84 ellipsoid surface and the NAVD88 orthometric geoid surface varies from -8 meters to over -35 meters depending on local topography. If a surveyor collects RTK aerial data with ellipsoidal height values and imports them into photogrammetry software configured for local orthometric elevations without supplying a corresponding Geoid grid file (such as GEOID18 or EGM96), the final map elevation will suffer a massive vertical offset equal to $N$.<\/p>\n\n    <h2 id=\"camera-calibration-and-lens-distortion-mechanics\">Camera Calibration, Focal Length Degeneration, and Model Warping<\/h2>\n    <p>In addition to coordinate reference frame mismatches, subtle photogrammetric calculation errors during 3D point cloud generation often introduce significant vertical errors into drone survey datasets.<\/p>\n\n    <h3>1. The Mechanics of the &#8220;Bowl Effect&#8221; (Dome Distortion)<\/h3>\n    <p>When aerial mapping software processes overlapping photos, it uses Structure-from-Motion (SfM) algorithms to estimate both 3D point positions and internal camera parameters (focal length $f$, principal point coordinates $c_x, c_y$, and radial\/tangential lens distortion coefficients $k_1, k_2, k_3, p_1, p_2$). If an aerial mission is flown using purely vertical (nadir) camera angles across flat terrain with uniform image texture, the processing algorithm can struggle to differentiate between a physical shift in flight altitude and a tiny change in camera focal length.<\/p>\n    <p>This mathematical ambiguity leads to a radial error propagation pattern known as the &#8220;bowl effect&#8221; or dome distortion. The center of the processed 3D orthomosaic and DEM shifts vertically upward or downward relative to the outer edges, creating a curved surface from flat ground. While RTK geotagging provides strong constraints, uncalibrated optical parameters can still introduce centimeters or decimeters of non-linear vertical distortion across wide mapping areas.<\/p>\n\n    <h3>2. Cross-Grid Flight Optimization and Oblique Image Integration<\/h3>\n    <p>Field crews can prevent focal length calculation errors and eliminate dome distortions by implementing three flight path optimizations:<\/p>\n    <ul>\n        <li><strong>Cross-Grid Flight Patterns:<\/strong> Instead of flying a single set of parallel flight lines, fly a double-grid (cross-grid) mission where the second pass intersects the first pass at a 90-degree angle. The crossing geometry strengthens tie-point intersections during bundle block adjustments.<\/li>\n        <li><strong>Incorporating Oblique Camera Angles:<\/strong> Tilting the camera gimbal slightly off-nadir (between 15 and 20 degrees) during a portion of the flight introduces varied perspectives of vertical surfaces. This mixed angular data decouples flight altitude calculations from camera focal length estimation, locking in accurate internal calibration parameters.<\/li>\n        <li><strong>Pre-Calibrated Camera Profiles (Kappas):<\/strong> For high-precision projects, perform a rigorous initial camera calibration over an established calibration field with known ground targets. Save these structural lens parameters as a fixed camera profile in your processing software, preventing the automated SfM engine from altering critical focal length values during optimization.<\/li>\n    <\/ul>\n\n    <h2 id=\"rtk-signal-degradation-and-ppk-recovery-workflows\">Mitigating RTK Signal Disruption via PPK and Check Point Verification<\/h2>\n    <p>Ensuring vertical accuracy across enterprise operations requires combining real-time positioning protocols with offline data verification procedures. When RTK correction streams drop out or suffer high latency mid-flight, post-processing workflows allow teams to maintain data integrity.<\/p>\n\n    <h3>1. Post-Processed Kinematic (PPK) Recovery Protocol<\/h3>\n    <p>Post-Processed Kinematic (PPK) workflows serve as the primary backup for RTK signal interruptions. Unlike RTK, which requires a live data link to transmit corrections during flight, PPK resolves positioning mathematics after data collection is complete.<\/p>\n    <p>During flight, the aircraft records three files to its internal storage:<\/p>\n    <ol>\n        <li>Raw, uncorrected satellite observation logs from the drone&#8217;s onboard GNSS receiver (e.g., RINEX or proprietary format files).<\/li>\n        <li>Precision camera exposure time logs, linking each photo frame to an exact GNSS timestamp.<\/li>\n        <li>High-resolution digital images.<\/li>\n    <\/ol>\n    <p>After landing, the GIS analyst imports the drone&#8217;s raw observation log along with continuous observation files from a nearby terrestrial reference station (such as a local base station or a regional public CORS station) into PPK processing software. The software correlates the timestamp of each image frame with post-processed carrier-phase differential equations, updating image EXIF metadata with high-precision centimeter coordinates. This completely replaces float-state metadata logged during mid-flight RTK connection drops.<\/p>\n\n    <h3>2. Independent Ground Check Points for Quality Assurance<\/h3>\n    <p>Even when flying with an active RTK Fix, survey standards require placing independent Ground Check Points (GCPs) across the project area to verify final map precision. Check points are physical targets placed on the ground and surveyed independently using a calibrated GNSS rover rod or total station.<\/p>\n    <p>Unlike Ground Control Points (GCPs)\u2014which are used by photogrammetry software to pull, align, and warp the 3D model during bundle block adjustments\u2014Check Points are kept entirely passive during processing. After the software generates the final point cloud and orthomosaic using the drone&#8217;s RTK positioning data, the software calculates Root Mean Square Error (RMSE) by comparing the estimated 3D position of each Check Point against its independently measured ground survey coordinates:<\/p>\n\n    <p style=\"text-align: center; font-weight: bold; font-size: 1.1rem; margin: 25px 0;\">$$\\text{RMSE}_Z = \\sqrt{\\frac{\\sum_{i=1}^{N} (Z_{\\text{model}, i} &#8211; Z_{\\text{survey}, i})^2}{N}}$$<\/p>\n\n    <p>If the calculated $\\text{RMSE}_Z$ falls within project tolerance limits (typically under 1.5 to 2 times the horizontal GSD), the vertical accuracy of the model is validated. If $\\text{RMSE}_Z$ reveals a uniform vertical offset across all check points, the analyst can apply a single vertical translation adjustment to align the model perfectly with local survey benchmarks without re-processing the entire dataset.<\/p>\n\n    <h2 id=\"elevation-error-and-signal-state-matrix\">Vertical Error Sources and Mid-Flight Signal Loss Matrix<\/h2>\n    <p>The table below summarizes common technical causes of vertical accuracy failures, positional symptoms during mid-flight RTK connection drops, and corrective field\/office solutions.<\/p>\n\n    <table>\n        <thead>\n            <tr>\n                <th>Issue \/ System Event<\/th>\n                <th>Underlying Technical Cause<\/th>\n                <th>Observed Map or Aircraft Symptom<\/th>\n                <th>Corrective Operational Solution<\/th>\n            <\/tr>\n        <\/thead>\n        <tbody>\n            <tr>\n                <td><strong>Datum Model Mismatch<\/strong><\/td>\n                <td>Confusing Ellipsoidal height (WGS84) with Orthometric height (MSL\/Geoid).<\/td>\n                <td>Large, uniform vertical shift across the entire map (typically 5 to 35 meters).<\/td>\n                <td>Apply correct regional Geoid grid file (e.g., GEOID18) in photogrammetry processing settings.<\/td>\n            <\/tr>\n            <tr>\n                <td><strong>Camera Lens Distortion (&#8220;Bowl Effect&#8221;)<\/strong><\/td>\n                <td>Correlated focal length and altitude estimation errors in nadir-only flights.<\/td>\n                <td>Non-linear vertical warping; center of terrain bows up or down relative to edges.<\/td>\n                <td>Fly cross-grid missions, add 15\u00b0 oblique images, or use pre-calibrated camera profiles.<\/td>\n            <\/tr>\n            <tr>\n                <td><strong>Mid-Flight NTRIP Data Drop (&lt; 30 sec)<\/strong><\/td>\n                <td>Temporary cellular coverage gap or network packet latency.<\/td>\n                <td>Receiver enters short holding buffer; temporary increase in position variance.<\/td>\n                <td>None required; modern flight controllers hold position accuracy through brief outages.<\/td>\n            <\/tr>\n            <tr>\n                <td><strong>Extended RTK Signal Outage (&gt; 60 sec)<\/strong><\/td>\n                <td>Total loss of base station radio link or persistent cellular dead zone.<\/td>\n                <td>Receiver transitions from RTK Fix to RTK Float; precision drops to meter-level.<\/td>\n                <td>Process raw satellite observation logs via PPK software post-flight to restore accuracy.<\/td>\n            <\/tr>\n            <tr>\n                <td><strong>Incorrect Antenna Height Offset<\/strong><\/td>\n                <td>Unrecorded distance between ground marker, base tripod, or drone APC to camera sensor.<\/td>\n                <td>Constant vertical error offset equal to the unmeasured physical measurement distance.<\/td>\n                <td>Input precise Antenna Phase Center (APC) measurement values in base station and processing setup.<\/td>\n            <\/tr>\n        <\/tbody>\n    <\/table>\n\n    <h2 id=\"frequently-asked-questions\" class=\"faq-section\">Frequently Asked Questions (FAQ)<\/h2>\n    \n    <div class=\"faq-item\">\n        <div class=\"faq-question\">Q1: Can I fix vertical accuracy errors in an already processed RTK drone model without re-flying the site?<\/div>\n        <div class=\"faq-content\">\n            <p>Yes. If independent Check Points reveal a uniform vertical offset across the entire site (while horizontal alignment remains accurate), the error is likely due to a datum mismatch or missing base antenna height offset. You can apply a vertical translation adjustment in your GIS or CAD software, shifting the entire 3D point cloud or DEM up or down by the exact offset measured at your survey benchmarks. If the error is non-linear (such as a domed surface), you must import the project back into photogrammetry software, apply a geoid model or add 3 to 4 physical control points, and re-run the bundle block adjustment.<\/p>\n        <\/div>\n    <\/div>\n\n    <div class=\"faq-item\">\n        <div class=\"faq-question\">Q2: How many Ground Check Points do I need to verify vertical accuracy on an RTK drone survey?<\/div>\n        <div class=\"faq-content\">\n            <p>For standard mapping sites up to 100 acres, placing 3 to 5 independent Ground Check Points distributed across the high, low, and center elevation regions of the site provides sufficient vertical validation. For larger or longer corridor projects, industry standards (such as ASPRS Guidelines) recommend placing additional check points for every additional 50 to 100 acres, ensuring independent accuracy reporting across varied terrain types.<\/p>\n        <\/div>\n    <\/div>\n\n    <div class=\"faq-item\">\n        <div class=\"faq-question\">Q3: Does flying at higher altitudes increase or decrease vertical elevation errors in drone mapping?<\/div>\n        <div class=\"faq-content\">\n            <p>Flying at higher altitudes increases overall spatial error because it increases Ground Sampling Distance (GSD)\u2014meaning each pixel covers a larger physical area on the ground. Since vertical photogrammetric accuracy is typically 1.5 to 3 times the horizontal GSD, a flight at 400 feet AGL (with a 3 cm\/pixel GSD) will naturally exhibit a wider vertical error range (4.5 to 9 cm) than a lower flight at 200 feet AGL (1.5 cm\/pixel GSD, yielding 2.2 to 4.5 cm vertical error).<\/p>\n        <\/div>\n    <\/div>\n\n    <div class=\"faq-item\">\n        <div class=\"faq-question\">Q4: Is PPK post-processing always more reliable than live RTK for achieving vertical accuracy?<\/div>\n        <div class=\"faq-content\">\n            <p>PPK is generally considered more reliable in challenging field environments because it does not depend on a continuous real-time data link during flight. If cellular signals drop or radio telemetry encounters interference, an RTK drone may record images with lower float-state precision. PPK processes the complete satellite trajectory forward and backward in time after landing, eliminating data gaps caused by mid-flight connectivity drops and delivering consistent vertical precision across the entire dataset.<\/p>\n        <\/div>\n    <\/div>\n\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Real-Time Kinematic (RTK) technology has fundamentally changed the land surveying, civil engineering, and aerial mapping industries by providing centimeter-level horizontal positioning in real time. Despite deploying advanced enterprise hardware, field teams frequently encounter unexpected vertical discrepancies in their 3D terrain models and elevation datasets. A digital elevation model (DEM) or point cloud may perfectly align &hellip; <\/br><a href=\"https:\/\/vertidronetech.com\/?p=3706\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Troubleshooting Drone Surveys: Vertical Accuracy Errors and RTK Signal Loss<\/span> <i class=\"fa fa-long-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":3708,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_angie_page":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"page_builder":"","footnotes":""},"categories":[71],"tags":[],"class_list":["post-3706","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/posts\/3706","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3706"}],"version-history":[{"count":1,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/posts\/3706\/revisions"}],"predecessor-version":[{"id":3707,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/posts\/3706\/revisions\/3707"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=\/wp\/v2\/media\/3708"}],"wp:attachment":[{"href":"https:\/\/vertidronetech.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3706"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3706"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vertidronetech.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3706"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}