Fill Gaps in Spatial Data with Spatial Interpolation ! How do we create smooth, continuous maps from scattered data points? That’s where Spatial Interpolation comes in, predicting unknown values based on known ones to create a complete picture. Let’s dive into some powerful techniques used in image processing and remote sensing: Trend Surface Analysis - Great for seeing broad trends, this method uses a smooth surface to capture general patterns. Inverse Distance Weighting (IDW) - Values closer together are more alike! IDW uses nearby points to estimate unknowns, perfect for clustered data. Global Polynomial - Ideal for large areas, this method fits a polynomial to highlight big-picture patterns without detailed local changes. Kriging - A geostatistical powerhouse, Kriging adapts to both distance and clustering, making it super accurate for complex data. Natural Neighbor - Relies on the closest points around your target, creating a smooth, natural surface for irregular data. Spline Interpolation - Creates smooth curves for a realistic surface, making it ideal for terrain modeling. Each of these techniques has unique strengths, depending on the data and purpose. Ready to learn more about Interpolation Techniques? Read Attached document! 📢 Coming up next: Stay tuned! I’ll cover each of these techniques in detail over the next few days, making it simple, one step at a time! 👉 If this post helped you understand Interpolation in Spatial Data, feel free to repost and share the knowledge! Follow Shehroz Khan and click the 🔔 to stay informed!