{"id":1778,"date":"2026-11-04T07:41:11","date_gmt":"2026-11-04T07:41:11","guid":{"rendered":"https:\/\/helpwithmygisassignment.com\/blog\/?p=1778"},"modified":"2026-11-04T07:41:33","modified_gmt":"2026-11-04T07:41:33","slug":"yolov8-geospatial-object-detection","status":"publish","type":"post","link":"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/","title":{"rendered":"Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline"},"content":{"rendered":"<div id=\"model-response-message-contentr_385a22cefccd5f03\" class=\"markdown markdown-main-panel md-content enable-luminous-fast-follows enable-updated-hr-color stronger tutor-markdown-rendering\" dir=\"ltr\" aria-busy=\"false\" aria-live=\"polite\">\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Geospatial_Object_Detection_with_YOLOv8_and_High-Resolution_Satellite_Imagery_A_Complete_PyTorch_Pipeline\" >Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Introduction_Overcoming_Aerial_Computer_Vision_Challenges\" >Introduction: Overcoming Aerial Computer Vision Challenges<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Data_Preparation_Pipeline_for_YOLOv8_Geospatial_Object_Detection\" >Data Preparation Pipeline for YOLOv8 Geospatial Object Detection<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#1_Tiling_Large_GeoTIFFs_with_rasterio\" >1. Tiling Large GeoTIFFs with rasterio<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#2_Converting_Bounding_Box_Vectors_to_YOLO_Label_Format\" >2. Converting Bounding Box Vectors to YOLO Label Format<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Model_Training_Inference\" >Model Training &amp; Inference<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#1_Setting_up_YOLOv8_Architecture_in_PyTorch\" >1. Setting up YOLOv8 Architecture in PyTorch<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#2_Fine-Tuning_Hyperparameters_for_Overhead_Imagery\" >2. Fine-Tuning Hyperparameters for Overhead Imagery<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#3_Running_Inference_and_Stitching_Tile_Predictions_into_GeoJSON_Vectors\" >3. Running Inference and Stitching Tile Predictions into GeoJSON Vectors<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Advanced_Geospatial_Extensions_Ecosystem_Integration\" >Advanced Geospatial Extensions &amp; Ecosystem Integration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1 class=\"\" data-path-to-node=\"0\"><span class=\"ez-toc-section\" id=\"Geospatial_Object_Detection_with_YOLOv8_and_High-Resolution_Satellite_Imagery_A_Complete_PyTorch_Pipeline\"><\/span>Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<div>In the modern landscape of remote sensing and spatial data science, extracting actionable intelligence from overhead imagery has transitioned from traditional manual photo interpretation to advanced deep learning automation. <b data-path-to-node=\"1\" data-index-in-node=\"225\">YOLOv8 Geospatial Object Detection Satellite Imagery<\/b> pipelines have become the industry standard for real-time, high-accuracy localization of infrastructure, environmental assets, and urban features.<\/div>\n<div>Whether you want to <b data-path-to-node=\"2\" data-index-in-node=\"20\">train YOLOv8 aerial satellite photos<\/b> for municipal asset management, environmental monitoring, or supply chain auditing, building an end-to-end PyTorch workflow requires bridging standard computer vision paradigms with geospatial mechanics. This comprehensive guide covers the end-to-end architecture, data engineering pipelines, hyperparameter tuning strategies, and post-processing steps needed to <b data-path-to-node=\"2\" data-index-in-node=\"420\">detect objects satellite orthophoto<\/b> rasters and export results directly into spatial vector formats.<\/div>\n<h2 data-path-to-node=\"4\"><span class=\"ez-toc-section\" id=\"Introduction_Overcoming_Aerial_Computer_Vision_Challenges\"><\/span>Introduction: Overcoming Aerial Computer Vision Challenges<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>Applying standard object detection models (originally trained on natural images like COCO or Pascal VOC) to overhead imagery introduces unique technical hurdles that data scientists and remote sensing specialists must overcome:<\/div>\n<ol start=\"1\" data-path-to-node=\"6\">\n<li>\n<div><b data-path-to-node=\"6,0,0\" data-index-in-node=\"0\">Massive Image Dimensions:<\/b> Modern commercial satellite imagery (WorldView, Planet Scope) and high-resolution aerial orthophotos often exceed 20,000 <span class=\"math-inline\" data-math=\"\\times\" data-index-in-node=\"147\">$\\times$<\/span> 20,000 pixels per scene. Feeding these directly into GPU memory causes immediate out-of-memory (OOM) errors.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"6,1,0\" data-index-in-node=\"0\">Extreme Feature Density and Small Objects:<\/b> Objects such as rooftop solar panels, residential swimming pools, or oil storage tanks occupy only a tiny fraction of total pixel area (often <span class=\"math-inline\" data-math=\"10 \\times 10\" data-index-in-node=\"185\">$10 \\times 10$<\/span> to <span class=\"math-inline\" data-math=\"40 \\times 40\" data-index-in-node=\"201\">$40 \\times 40$<\/span> pixels). Standard anchor-based architectures struggle to retain fine-grained spatial gradients across deep downsampling layers.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"6,2,0\" data-index-in-node=\"0\">Arbitrary Orientations:<\/b> Unlike horizontal natural photographs where cars or people maintain a consistent upright orientation, aerial and satellite targets appear rotated at arbitrary 360-degree angles. Standard axis-aligned bounding boxes can capture excessive background clutter or fail to bound elongated features precisely.<\/div>\n<\/li>\n<\/ol>\n<div>To address these constraints, we implement a tiled processing architecture backed by <code data-path-to-node=\"7\" data-index-in-node=\"85\">rasterio<\/code> and fine-tune an anchor-free YOLOv8 PyTorch model equipped with optimized mosaic augmentations and multi-scale feature fusion necks.<\/div>\n<h2 data-path-to-node=\"9\"><span class=\"ez-toc-section\" id=\"Data_Preparation_Pipeline_for_YOLOv8_Geospatial_Object_Detection\"><\/span>Data Preparation Pipeline for YOLOv8 Geospatial Object Detection<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-1847\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_qmdk9pqmdk9pqmdk-1-1024x559.jpg\" alt=\"A data preparation pipeline for YOLOv8 geospatial object detection illustrating GeoTIFF tiling.\" width=\"1024\" height=\"559\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_qmdk9pqmdk9pqmdk-1-1024x559.jpg 1024w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_qmdk9pqmdk9pqmdk-1-300x164.jpg 300w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_qmdk9pqmdk9pqmdk-1-768x419.jpg 768w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_qmdk9pqmdk9pqmdk-1.jpg 1408w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<div>Raw GeoTIFF orthophotos carry embedded Coordinate Reference Systems (CRS) and affine transformation matrices. Breaking these files into manageable sub-chips requires preserving georeferenced metadata so that bounding box detections can later be re-projected back into real-world geographic coordinates (EPSG:4326 or local UTM zones).<\/div>\n<h3 class=\"\" data-path-to-node=\"11\"><span class=\"ez-toc-section\" id=\"1_Tiling_Large_GeoTIFFs_with_rasterio\"><\/span>1. Tiling Large GeoTIFFs with <code data-path-to-node=\"11\" data-index-in-node=\"30\">rasterio<\/code><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>The following production-grade Python script tiles a large GeoTIFF orthophoto into overlapping <span class=\"math-inline\" data-math=\"512 \\times 512\" data-index-in-node=\"95\">$512 \\times 512$<\/span> pixel sub-chips, ensuring edge features are captured without truncation. For foundational <a href=\"https:\/\/helpwithmygisassignment.com\/blog\/cloud-optimized-geotiff-web-map-architecture\/\">raster manipulation techniques<\/a>, refer to our comprehensive <a class=\"ng-star-inserted\" href=\"https:\/\/www.google.com\/search?q=https:\/\/example.com\/python-rasterio-tutorial\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQMA\">Python rasterio tutorial<\/a>.<\/div>\n<div class=\"code-block ng-tns-c745154712-14 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQMQ\">\n<div class=\"formatted-code-block-internal-container ng-tns-c745154712-14\">\n<div class=\"animated-opacity ng-tns-c745154712-14\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c745154712-14 ng-star-inserted\">\n<p><span class=\"ng-tns-c745154712-14\">Python<\/span><\/p>\n<div class=\"buttons ng-tns-c745154712-14 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c745154712-14\"><code class=\"code-container formatted ng-tns-c745154712-14\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-keyword\">import<\/span> os\r\n<span class=\"hljs-keyword\">import<\/span> rasterio\r\n<span class=\"hljs-keyword\">from<\/span> rasterio.windows <span class=\"hljs-keyword\">import<\/span> Window\r\n<span class=\"hljs-keyword\">from<\/span> shapely.geometry <span class=\"hljs-keyword\">import<\/span> box\r\n<span class=\"hljs-keyword\">import<\/span> geopandas <span class=\"hljs-keyword\">as<\/span> gpd\r\n\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">tile_geotiff<\/span>(<span class=\"hljs-params\">image_path, output_dir, tile_size=<span class=\"hljs-number\">512<\/span>, overlap=<span class=\"hljs-number\">64<\/span><\/span>):<\/span>\r\n    os.makedirs(output_dir, exist_ok=<span class=\"hljs-literal\">True<\/span>)\r\n    \r\n    <span class=\"hljs-keyword\">with<\/span> rasterio.<span class=\"hljs-built_in\">open<\/span>(image_path) <span class=\"hljs-keyword\">as<\/span> src:\r\n        width = src.width\r\n        height = src.height\r\n        transform = src.transform\r\n        crs = src.crs\r\n        \r\n        step = tile_size - overlap\r\n        \r\n        <span class=\"hljs-keyword\">for<\/span> y <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">0<\/span>, height, step):\r\n            <span class=\"hljs-keyword\">for<\/span> x <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">0<\/span>, width, step):\r\n                <span class=\"hljs-comment\"># Ensure tile dimensions do not exceed raster bounds<\/span>\r\n                w = <span class=\"hljs-built_in\">min<\/span>(tile_size, width - x)\r\n                h = <span class=\"hljs-built_in\">min<\/span>(tile_size, height - y)\r\n                \r\n                <span class=\"hljs-comment\"># Define spatial window<\/span>\r\n                window = Window(x, y, w, h)\r\n                \r\n                <span class=\"hljs-comment\"># Read raster data for window across all bands<\/span>\r\n                tile_data = src.read(window=window)\r\n                \r\n                <span class=\"hljs-comment\"># Compute affine transform for the specific tile<\/span>\r\n                tile_transform = rasterio.windows.transform(window, transform)\r\n                \r\n                <span class=\"hljs-comment\"># Update metadata for sub-chip<\/span>\r\n                meta = src.meta.copy()\r\n                meta.update({\r\n                    <span class=\"hljs-string\">\"height\"<\/span>: h,\r\n                    <span class=\"hljs-string\">\"width\"<\/span>: w,\r\n                    <span class=\"hljs-string\">\"transform\"<\/span>: tile_transform\r\n                })\r\n                \r\n                tile_filename = os.path.join(output_dir, <span class=\"hljs-string\">f\"tile_x<span class=\"hljs-subst\">{x}<\/span>_y<span class=\"hljs-subst\">{y}<\/span>.tif\"<\/span>)\r\n                <span class=\"hljs-keyword\">with<\/span> rasterio.<span class=\"hljs-built_in\">open<\/span>(tile_filename, <span class=\"hljs-string\">\"w\"<\/span>, **meta) <span class=\"hljs-keyword\">as<\/span> dst:\r\n                    dst.write(tile_data)\r\n                    \r\n    print(<span class=\"hljs-string\">f\"Successfully tiled <span class=\"hljs-subst\">{image_path}<\/span> into <span class=\"hljs-subst\">{output_dir}<\/span>\"<\/span>)\r\n\r\n<span class=\"hljs-comment\"># Example invocation<\/span>\r\n<span class=\"hljs-comment\"># tile_geotiff(\"orthophoto_nairobi.tif\", \".\/tiles\", tile_size=512, overlap=64)<\/span>\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 data-path-to-node=\"14\"><span class=\"ez-toc-section\" id=\"2_Converting_Bounding_Box_Vectors_to_YOLO_Label_Format\"><\/span>2. Converting Bounding Box Vectors to YOLO Label Format<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>YOLOv8 requires normalized bounding box text files corresponding to each image chip. Each line in a label text file represents an object annotation in the format:<\/div>\n<div data-path-to-node=\"16\">\n<div class=\"math-block\" data-math=\"\\text{class\\_id} \\quad x_{\\text{center}} \\quad y_{\\text{center}} \\quad w \\quad h\">$$\\text{class\\_id} \\quad x_{\\text{center}} \\quad y_{\\text{center}} \\quad w \\quad h$$<\/div>\n<\/div>\n<div>Where all coordinates and dimensions are normalized relative to the image width and height (<span class=\"math-inline\" data-math=\"[0.0, 1.0]\" data-index-in-node=\"92\">$[0.0, 1.0]$<\/span><span class=\"\">).<\/span><\/div>\n<div class=\"code-block ng-tns-c745154712-15 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQMg\">\n<div class=\"formatted-code-block-internal-container ng-tns-c745154712-15\">\n<div class=\"animated-opacity ng-tns-c745154712-15\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c745154712-15 ng-star-inserted\">\n<p><span class=\"ng-tns-c745154712-15\">Python<\/span><\/p>\n<div class=\"buttons ng-tns-c745154712-15 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c745154712-15\"><code class=\"code-container formatted ng-tns-c745154712-15\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\r\n<span class=\"hljs-keyword\">from<\/span> shapely.geometry <span class=\"hljs-keyword\">import<\/span> box\r\n<span class=\"hljs-keyword\">import<\/span> rasterio.transform\r\n\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">vector_to_yolo_labels<\/span>(<span class=\"hljs-params\">vector_geojson, tile_tif, output_txt<\/span>):<\/span>\r\n    gdf = gpd.read_file(vector_geojson)\r\n    \r\n    <span class=\"hljs-keyword\">with<\/span> rasterio.<span class=\"hljs-built_in\">open<\/span>(tile_tif) <span class=\"hljs-keyword\">as<\/span> src:\r\n        bounds = src.bounds\r\n        transform = src.transform\r\n        inv_transform = ~transform\r\n        img_w, img_h = src.width, src.height\r\n        \r\n        tile_poly = box(bounds.left, bounds.bottom, bounds.right, bounds.top)\r\n        \r\n        <span class=\"hljs-comment\"># Filter vectors intersecting the tile<\/span>\r\n        intersecting = gdf[gdf.intersects(tile_poly)]\r\n        \r\n        yolo_annotations = []\r\n        <span class=\"hljs-keyword\">for<\/span> _, row <span class=\"hljs-keyword\">in<\/span> intersecting.iterrows():\r\n            geom = row.geometry\r\n            <span class=\"hljs-comment\"># Convert polygon to bounding box<\/span>\r\n            minx, miny, maxx, maxy = geom.bounds\r\n            \r\n            <span class=\"hljs-comment\"># Convert spatial coordinates to pixel coordinates<\/span>\r\n            px_min, py_max = rasterio.transform.xy(transform, [miny], [minx], offset=<span class=\"hljs-string\">'ul'<\/span>)\r\n            px_max, py_min = rasterio.transform.xy(transform, [maxy], [maxx], offset=<span class=\"hljs-string\">'lr'<\/span>)\r\n            \r\n            <span class=\"hljs-comment\"># Alternatively use inverse transform directly<\/span>\r\n            <span class=\"hljs-comment\"># Pixel space conversion<\/span>\r\n            col_min, row_max = ~transform * (minx, miny)\r\n            col_max, row_min = ~transform * (maxx, maxy)\r\n            \r\n            x_min = <span class=\"hljs-built_in\">max<\/span>(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-built_in\">min<\/span>(col_min, col_max))\r\n            x_max = <span class=\"hljs-built_in\">min<\/span>(img_w, <span class=\"hljs-built_in\">max<\/span>(col_min, col_max))\r\n            y_min = <span class=\"hljs-built_in\">max<\/span>(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-built_in\">min<\/span>(row_min, row_max))\r\n            y_max = <span class=\"hljs-built_in\">min<\/span>(img_h, <span class=\"hljs-built_in\">max<\/span>(row_min, row_max))\r\n            \r\n            <span class=\"hljs-keyword\">if<\/span> x_max &lt;= x_min <span class=\"hljs-keyword\">or<\/span> y_max &lt;= y_min:\r\n                <span class=\"hljs-keyword\">continue<\/span>\r\n                \r\n            <span class=\"hljs-comment\"># Calculate YOLO normalized format<\/span>\r\n            x_center = ((x_min + x_max) \/ <span class=\"hljs-number\">2.0<\/span>) \/ img_w\r\n            y_center = ((y_min + y_max) \/ <span class=\"hljs-number\">2.0<\/span>) \/ img_h\r\n            w = (x_max - x_min) \/ img_w\r\n            h = (y_max - y_min) \/ img_h\r\n            \r\n            class_id = row.get(<span class=\"hljs-string\">'class_id'<\/span>, <span class=\"hljs-number\">0<\/span>)\r\n            yolo_annotations.append(<span class=\"hljs-string\">f\"<span class=\"hljs-subst\">{class_id}<\/span> <span class=\"hljs-subst\">{x_center:<span class=\"hljs-number\">.6<\/span>f}<\/span> <span class=\"hljs-subst\">{y_center:<span class=\"hljs-number\">.6<\/span>f}<\/span> <span class=\"hljs-subst\">{w:<span class=\"hljs-number\">.6<\/span>f}<\/span> <span class=\"hljs-subst\">{h:<span class=\"hljs-number\">.6<\/span>f}<\/span>\"<\/span>)\r\n            \r\n        <span class=\"hljs-keyword\">with<\/span> <span class=\"hljs-built_in\">open<\/span>(output_txt, <span class=\"hljs-string\">'w'<\/span>) <span class=\"hljs-keyword\">as<\/span> f:\r\n            f.write(<span class=\"hljs-string\">\"\\n\"<\/span>.join(yolo_annotations))\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h2 data-path-to-node=\"20\"><span class=\"ez-toc-section\" id=\"Model_Training_Inference\"><\/span>Model Training &amp; Inference<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>With our data formatted into standard PyTorch-compatible directory structures (<code data-path-to-node=\"21\" data-index-in-node=\"79\">images\/<\/code> and <code data-path-to-node=\"21\" data-index-in-node=\"91\">labels\/<\/code>), we can initialize and train our YOLOv8 architecture.<\/div>\n<div><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1779\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1.png\" alt=\"\" width=\"399\" height=\"250\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1.png 399w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1-300x188.png 300w\" sizes=\"auto, (max-width: 399px) 100vw, 399px\" \/><\/div>\n<div class=\"attachment-container search-images md-has-block-component\"><\/div>\n<h3 class=\"\" data-path-to-node=\"25\"><span class=\"ez-toc-section\" id=\"1_Setting_up_YOLOv8_Architecture_in_PyTorch\"><\/span>1. Setting up YOLOv8 Architecture in PyTorch<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>We utilize the <code data-path-to-node=\"26\" data-index-in-node=\"15\">ultralytics<\/code> library to configure a custom YOLOv8 model tailored for three distinct remote sensing classes: solar panels (<code data-path-to-node=\"26\" data-index-in-node=\"136\">0<\/code>), swimming pools (<code data-path-to-node=\"26\" data-index-in-node=\"156\">1<\/code>), and storage tanks (<code data-path-to-node=\"26\" data-index-in-node=\"179\">2<\/code>). Create a configuration file named <code data-path-to-node=\"26\" data-index-in-node=\"217\">dataset.yaml<\/code>:<\/div>\n<div class=\"code-block ng-tns-c745154712-16 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQNw\">\n<div class=\"formatted-code-block-internal-container ng-tns-c745154712-16\">\n<div class=\"animated-opacity ng-tns-c745154712-16\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c745154712-16 ng-star-inserted\">\n<p><span class=\"ng-tns-c745154712-16\">YAML<\/span><\/p>\n<div class=\"buttons ng-tns-c745154712-16 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c745154712-16\"><code class=\"code-container formatted ng-tns-c745154712-16\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-attr\">path:<\/span> <span class=\"hljs-string\">\/path\/to\/dataset<\/span>\r\n<span class=\"hljs-attr\">train:<\/span> <span class=\"hljs-string\">images\/train<\/span>\r\n<span class=\"hljs-attr\">val:<\/span> <span class=\"hljs-string\">images\/val<\/span>\r\n\r\n<span class=\"hljs-attr\">names:<\/span>\r\n  <span class=\"hljs-attr\">0:<\/span> <span class=\"hljs-string\">solar_panel<\/span>\r\n  <span class=\"hljs-attr\">1:<\/span> <span class=\"hljs-string\">swimming_pool<\/span>\r\n  <span class=\"hljs-attr\">2:<\/span> <span class=\"hljs-string\">storage_tank<\/span>\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 data-path-to-node=\"28\"><span class=\"ez-toc-section\" id=\"2_Fine-Tuning_Hyperparameters_for_Overhead_Imagery\"><\/span>2. Fine-Tuning Hyperparameters for Overhead Imagery<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>Aerial imagery demands specific hyperparameter adjustments. Because overhead targets lack the standard horizontal bias of natural imagery, aggressive rotation, scaling,<span class=\"\"> and HSV augmentations are crucial to prevent overfitting.<\/span><\/div>\n<div class=\"code-block ng-tns-c745154712-17 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQOA\">\n<div class=\"formatted-code-block-internal-container ng-tns-c745154712-17\">\n<div class=\"animated-opacity ng-tns-c745154712-17\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c745154712-17 ng-star-inserted\">\n<p><span class=\"ng-tns-c745154712-17\">Python<\/span><\/p>\n<div class=\"buttons ng-tns-c745154712-17 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c745154712-17\"><code class=\"code-container formatted ng-tns-c745154712-17\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-keyword\">from<\/span> ultralytics <span class=\"hljs-keyword\">import<\/span> YOLO\r\n\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">train_yolov8_aerial<\/span>():<\/span>\r\n    <span class=\"hljs-comment\"># Load pretrained YOLOv8 medium model for robust feature extraction<\/span>\r\n    model = YOLO(<span class=\"hljs-string\">\"yolov8m.pt\"<\/span>)\r\n    \r\n    <span class=\"hljs-comment\"># Execute training with custom hyperparameters optimized for aerial imagery<\/span>\r\n    results = model.train(\r\n        data=<span class=\"hljs-string\">\"dataset.yaml\"<\/span>,\r\n        epochs=<span class=\"hljs-number\">100<\/span>,\r\n        imgsz=<span class=\"hljs-number\">512<\/span>,\r\n        batch=<span class=\"hljs-number\">16<\/span>,\r\n        device=<span class=\"hljs-number\">0<\/span>,\r\n        workers=<span class=\"hljs-number\">8<\/span>,\r\n        optimizer=<span class=\"hljs-string\">\"SGD\"<\/span>,\r\n        lr0=<span class=\"hljs-number\">0.01<\/span>,\r\n        lrf=<span class=\"hljs-number\">0.01<\/span>,\r\n        momentum=<span class=\"hljs-number\">0.937<\/span>,\r\n        weight_decay=<span class=\"hljs-number\">0.0005<\/span>,\r\n        warmup_epochs=<span class=\"hljs-number\">3.0<\/span>,\r\n        box=<span class=\"hljs-number\">7.5<\/span>,\r\n        cls=<span class=\"hljs-number\">0.5<\/span>,\r\n        dfl=<span class=\"hljs-number\">1.5<\/span>,\r\n        <span class=\"hljs-comment\"># Specialized augmentations for aerial perspectives<\/span>\r\n        degrees=<span class=\"hljs-number\">180.0<\/span>,     <span class=\"hljs-comment\"># Full rotation invariance for overhead objects<\/span>\r\n        translate=<span class=\"hljs-number\">0.1<\/span>,\r\n        scale=<span class=\"hljs-number\">0.5<\/span>,         <span class=\"hljs-comment\"># Scale variation for varying satellite zoom levels<\/span>\r\n        shear=<span class=\"hljs-number\">0.0<\/span>,\r\n        perspective=<span class=\"hljs-number\">0.0<\/span>,\r\n        flipud=<span class=\"hljs-number\">0.5<\/span>,        <span class=\"hljs-comment\"># Vertical flips make sense in overhead imagery<\/span>\r\n        fliplr=<span class=\"hljs-number\">0.5<\/span>,\r\n        mosaic=<span class=\"hljs-number\">1.0<\/span>,        <span class=\"hljs-comment\"># Strong mosaic augmentation for small object context<\/span>\r\n        mixup=<span class=\"hljs-number\">0.1<\/span>,\r\n        copy_paste=<span class=\"hljs-number\">0.1<\/span>\r\n    )\r\n    \r\n    print(<span class=\"hljs-string\">\"Training complete. Best weights saved to runs\/detect\/train\/weights\/best.pt\"<\/span>)\r\n\r\n<span class=\"hljs-keyword\">if<\/span> __name__ == <span class=\"hljs-string\">\"__main__\"<\/span>:\r\n    <span class=\"hljs-comment\"># train_yolov8_aerial()<\/span>\r\n    <span class=\"hljs-keyword\">pass<\/span>\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 class=\"\" data-path-to-node=\"31\"><span class=\"ez-toc-section\" id=\"3_Running_Inference_and_Stitching_Tile_Predictions_into_GeoJSON_Vectors\"><\/span>3. Running Inference and Stitching Tile Predictions into GeoJSON Vectors<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>Once trained,<span class=\"\"> running inference across massive spatial scenes requires iterating over all tiles,<\/span><span class=\"\"> performing non-max suppression (NMS),<\/span><span class=\"\"> and transforming pixel bounding boxes back into geographic coordinates.<\/span> We then export these detections into unified GeoJSON vectors.<\/div>\n<div class=\"code-block ng-tns-c745154712-18 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQOg\">\n<div class=\"formatted-code-block-internal-container ng-tns-c745154712-18\">\n<div class=\"animated-opacity ng-tns-c745154712-18\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c745154712-18 ng-star-inserted\">\n<p><span class=\"ng-tns-c745154712-18\">Python<\/span><\/p>\n<div class=\"buttons ng-tns-c745154712-18 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c745154712-18\"><code class=\"code-container formatted ng-tns-c745154712-18\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-keyword\">import<\/span> glob\r\n<span class=\"hljs-keyword\">import<\/span> json\r\n<span class=\"hljs-keyword\">from<\/span> shapely.geometry <span class=\"hljs-keyword\">import<\/span> box <span class=\"hljs-keyword\">as<\/span> shapely_box, mapping\r\n<span class=\"hljs-keyword\">import<\/span> geopandas <span class=\"hljs-keyword\">as<\/span> gpd\r\n\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">predict_and_stitch<\/span>(<span class=\"hljs-params\">weights_path, tile_dir, output_geojson<\/span>):<\/span>\r\n    model = YOLO(weights_path)\r\n    features = []\r\n    \r\n    tile_paths = glob.glob(os.path.join(tile_dir, <span class=\"hljs-string\">\"*.tif\"<\/span>))\r\n    \r\n    <span class=\"hljs-keyword\">for<\/span> tile_path <span class=\"hljs-keyword\">in<\/span> tile_paths:\r\n        <span class=\"hljs-keyword\">with<\/span> rasterio.<span class=\"hljs-built_in\">open<\/span>(tile_path) <span class=\"hljs-keyword\">as<\/span> src:\r\n            transform = src.transform\r\n            crs = src.crs\r\n            \r\n            <span class=\"hljs-comment\"># Run inference on individual tile<\/span>\r\n            results = model.predict(tile_path, conf=<span class=\"hljs-number\">0.45<\/span>, iou=<span class=\"hljs-number\">0.5<\/span>, verbose=<span class=\"hljs-literal\">False<\/span>)\r\n            boxes = results[<span class=\"hljs-number\">0<\/span>].boxes\r\n            \r\n            <span class=\"hljs-keyword\">for<\/span> box_tensor <span class=\"hljs-keyword\">in<\/span> boxes:\r\n                xyxy = box_tensor.xyxy[<span class=\"hljs-number\">0<\/span>].cpu().numpy() <span class=\"hljs-comment\"># [x1, y1, x2, y2] in pixels<\/span>\r\n                conf = <span class=\"hljs-built_in\">float<\/span>(box_tensor.conf[<span class=\"hljs-number\">0<\/span>].cpu().numpy())\r\n                cls_id = <span class=\"hljs-built_in\">int<\/span>(box_tensor.cls[<span class=\"hljs-number\">0<\/span>].cpu().numpy())\r\n                \r\n                x1, y1, x2, y2 = xyxy\r\n                \r\n                <span class=\"hljs-comment\"># Transform pixel coordinates to geospatial coordinates<\/span>\r\n                ul_x, ul_y = transform * (x1, y1)\r\n                lr_x, lr_y = transform * (x2, y2)\r\n                \r\n                <span class=\"hljs-comment\"># Create shapely polygon for bounding box<\/span>\r\n                geom = shapely_box(<span class=\"hljs-built_in\">min<\/span>(ul_x, lr_x), <span class=\"hljs-built_in\">min<\/span>(ul_y, lr_y), <span class=\"hljs-built_in\">max<\/span>(ul_x, lr_x), <span class=\"hljs-built_in\">max<\/span>(ul_y, lr_y))\r\n                \r\n                features.append({\r\n                    <span class=\"hljs-string\">\"geometry\"<\/span>: mapping(geom),\r\n                    <span class=\"hljs-string\">\"properties\"<\/span>: {\r\n                        <span class=\"hljs-string\">\"class_id\"<\/span>: cls_id,\r\n                        <span class=\"hljs-string\">\"class_name\"<\/span>: model.names[cls_id],\r\n                        <span class=\"hljs-string\">\"confidence\"<\/span>: conf,\r\n                        <span class=\"hljs-string\">\"source_tile\"<\/span>: os.path.basename(tile_path)\r\n                    }\r\n                })\r\n                \r\n    <span class=\"hljs-comment\"># Create GeoDataFrame and save to GeoJSON<\/span>\r\n    gdf = gpd.GeoDataFrame.from_features(features, crs=crs)\r\n    gdf.to_file(output_geojson, driver=<span class=\"hljs-string\">\"GeoJSON\"<\/span>)\r\n    print(<span class=\"hljs-string\">f\"Inference complete. Saved <span class=\"hljs-subst\">{<span class=\"hljs-built_in\">len<\/span>(gdf)}<\/span> detections to <span class=\"hljs-subst\">{output_geojson}<\/span>\"<\/span>)\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h2 data-path-to-node=\"35\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-1843\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_bv64sxbv64sxbv64-1024x559.jpg\" alt=\"Visualizing bounding box results and vector stitching in a YOLOv8 geospatial object detection workflow.\" width=\"1024\" height=\"559\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_bv64sxbv64sxbv64-1024x559.jpg 1024w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_bv64sxbv64sxbv64-300x164.jpg 300w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_bv64sxbv64sxbv64-768x419.jpg 768w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/11\/Gemini_Generated_Image_bv64sxbv64sxbv64.jpg 1408w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/h2>\n<h2 data-path-to-node=\"35\"><span class=\"ez-toc-section\" id=\"Advanced_Geospatial_Extensions_Ecosystem_Integration\"><\/span>Advanced Geospatial Extensions &amp; Ecosystem Integration<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>For advanced remote sensing operations, object detection outputs often feed into broader spatial workflows:<\/div>\n<ul data-path-to-node=\"37\">\n<li>\n<div><b data-path-to-node=\"37,0,0\" data-index-in-node=\"0\">Semantic Segmentation Refinement:<\/b> Pair YOLOv8 bounding box coordinates with <a class=\"ng-star-inserted\" href=\"https:\/\/www.google.com\/search?q=https:\/\/example.com\/sam-2-building-footprints\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQOw\">SAM 2 building footprint extraction<\/a> models to derive precise vector polygons rather than bounding boxes.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"37,1,0\" data-index-in-node=\"0\">Urban Dynamics &amp; Flow:<\/b> Aggregate detected vehicular assets over time using <a class=\"ng-star-inserted\" href=\"https:\/\/www.google.com\/search?q=https:\/\/example.com\/gnn-traffic-prediction\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwiewbLuqtyXAxUAAAAAHQAAAAAQPA\">Graph Neural Networks for traffic prediction<\/a> models to model metropolitan congestion.<\/div>\n<\/li>\n<\/ul>\n<h2 data-path-to-node=\"39\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>Implementing a robust YOLOv8 geospatial object detection pipeline in PyTorch bridges the gap between state-of-the-art computer vision and enterprise GIS analysis. By properly handling large orthophoto tiling via <code data-path-to-node=\"40\" data-index-in-node=\"212\">rasterio<\/code>, configuring rotation-invariant augmentations, and re-projecting bounding box predictions back into georeferenced vector formats, remote sensing teams can automate large-scale asset inventory with exceptional speed and precision.<\/div>\n<div><\/div>\n<div>Want to master advanced computer vision workflows? Bookmark this guide on <b data-path-to-node=\"10,0,1,0\" data-index-in-node=\"75\">YOLOv8 geospatial object detection<\/b>, or <a href=\"https:\/\/helpwithmygisassignment.com\/blog\/\">reach out to our team<\/a> if you need expert help with your advanced GIS assignments.<\/div>\n<div><a href=\"https:\/\/helpwithmygisassignment.com\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcSx9AL9qnLS1Y0hrEvjtgSy2Pro--taBEO7M187nijUzA&amp;s=10\" alt=\"Order now expert assistance for YOLOv8 geospatial object detection assignments and projects.\" width=\"606\" height=\"505\" \/><\/a><\/div>\n<div><\/div>\n<div class=\"attachment-container unknown md-has-block-component\"><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline In the modern landscape of remote sensing and spatial data science, extracting actionable intelligence from overhead imagery has transitioned from traditional manual photo interpretation to advanced deep learning automation. YOLOv8 Geospatial Object Detection Satellite Imagery pipelines have become the industry standard for &#8230; <a title=\"Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline\" class=\"read-more\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/yolov8-geospatial-object-detection\/\" aria-label=\"Read more about Geospatial Object Detection with YOLOv8 and High-Resolution Satellite Imagery: A Complete PyTorch Pipeline\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":1847,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1778","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1778","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/comments?post=1778"}],"version-history":[{"count":8,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1778\/revisions"}],"predecessor-version":[{"id":1849,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1778\/revisions\/1849"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/media\/1847"}],"wp:attachment":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/media?parent=1778"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/categories?post=1778"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/tags?post=1778"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}