Machine Learning vs Traditional Classification | SVM and MLC for LULC Mapping #remotesensing

Welcome to Terra Matrix Engineering Consultant In this video, we demonstrate how to generate accurate Land Use Land Cover (LULC) maps using Remote Sensing and ArcGIS. This practical session compares two widely used classification techniques: ✅ Support Vector Machine (SVM) Classification ✅ Maximum Likelihood Classification (MLC) You learn the complete workflow from satellite image preparation to final LULC map generation and accuracy assessment. 📌 Topics Covered: ✔ Introduction to LULC Classification ✔ Landsat Satellite Image Processing ✔ ArcGIS-Based Classification Workflow ✔ Training Sample Collection ✔ Support Vector Machine (SVM) Classification ✔ Maximum Likelihood Classification (MLC) ✔ Accuracy Assessment ✔ Comparison of Machine Learning and Traditional Approaches ✔ Practical Applications of LULC Mapping 🎯 Who Should Watch? • GIS Professionals • Remote Sensing Researchers • Civil Engineers • Geography Students • Environmental Scientists • PhD Scholars • M.Tech and B.Tech Students • GeoAI Enthusiasts 🛠 Software Used: ArcGIS Remote Sensing Data Landsat Satellite Imagery 🌐 Website: www.terramatrix.in 📧 Email: [email protected] 📞 Contact: +91 7008038833 / +91 8658003717 If you find this video useful: 👍 Like 📢 Share 🔔 Subscribe to our channel for more GIS, Remote Sensing, GeoAI, Machine Learning, Google Earth Engine, and Research Training videos. #LULC #ArcGIS #SVM #MachineLearning #RemoteSensing #GIS #GeoAI #Landsat #LandUseLandCover #Geospatial #Classification #MLC #MaximumLikelihood #SatelliteImageProcessing