Sunday, June 9, 2024

Landuse landcover with machine learning using ArcGIS only

Creating land use maps is a complex process often plagued by errors and low accuracy rates. For instance, barren land or riverbeds may be misclassified as urban areas. To address these errors, it typically requires the use of multiple software tools, as a single software package may not be sufficient for all processes, such as post-classification pixel corrections. Often, other software is used alongside ArcGIS to correct these misclassifications because ArcGIS lacks a dedicated tool for pixel correction.

However, in this course, we have successfully managed to perform all tasks using ArcGIS alone. Land use classification is accomplished using machine learning techniques, specifically the Support Vector Machine (SVM) and Random Forest methods, through supervised training. Even post-classification pixel correction is handled within ArcGIS using specific strategies.

If you prefer to rely solely on ArcGIS without using any additional software, this course is designed for you. The first few videos are free. Here is the link.

Link to course:

Things that covered
  • Land use mapping
  • Land cover classification
  • ArcGIS machine learning
  • SVM land use classification
  • Random Forest land use mapping
  • Post-classification pixel correction
  • ArcGIS pixel correction
  • Supervised training ArcGIS
  • Land use errors correction
  • Urban area misclassification corrections
  • ArcGIS land use course
  • Barren land classification corrections 
  • Riverbed misclassification corrections
  • Single software land use mapping
  • ArcGIS only land use mapping
  • High accuracy land cover mapping
  • Machine learning in ArcGIS
  • Land use mapping techniques
  • Land cover classification errors corrections