PROJECT TITLE :

Extraction of Slum Areas From VHR Imagery Using GLCM Variance

ABSTRACT:

Many cities in the worldwide South face the emergence and growth of highly dynamic slum areas, however usually lack detailed info on these developments. On the market statistical data are commonly aggregated to giant, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base data neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal designed-up (formal) areas in very high spatial and spectral resolution satellite imagery like WorldView-two, OrbView, Quickbird, and Resourcesat. Three geographically completely different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized distinction vegetation index (NDVI) is ready to separate slums and formal areas. The general accuracy achieved is eighty fourp.c in Kigali, eighty seven% in Mumbai, and 88p.c in Ahmedabad. Furthermore, combining spectral data with the GLCM variance inside a random forest classifier leads to a pixel-based mostly classification accuracy of 90percent. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88percent–ninety five% for slum locations depending on the scale parameter.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Resource-aware Feature Extraction in Mobile Edge Computing ABSTRACT: Mobile image recognition services are revolutionizing our everyday lives by providing people with image recognition services that they can access
PROJECT TITLE : Biomedical Relation Extraction With Knowledge Graph-Based Recommendations ABSTRACT: Biomedical Relation Extraction (RE) systems search for and categorize relations between biomedical entities in order to improve
PROJECT TITLE : A Natural Language Process-Based Framework for Automatic Association Word Extraction ABSTRACT: In psychology, word association has been extensively explored for exposing mental representations and relationships
PROJECT TITLE : Automatic Keyword Extraction for Text Summarization A Survey ABSTRACT: Data has been quickly rising in recent years in every sphere, including journalism, social media, banking, education, and so on. Due to the
PROJECT TITLE : Financial Latent Dirichlet Allocation (FinLDA) Feature Extraction in Text and Data Mining for Financial Time Series Prediction ABSTRACT: Many financial time series predictions based on fundamental analysis have

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry