Vegetation indices play a predominant role in the field of Remote processing systems which assimilate vital multispectral images. The digital numbers identify the spectral information in one or more spectral bands. It focuses mainly on two or more spectral regions and obtains different types of surfaces like vegetation, built-up, bare soil and water area. Different types of vegetation can be studied and analyzed using LANDSAT images. In this paper, comparison has been made on ten major vegetation indices such as RVI, DVI, NDVI, TNDVI, NDWI, MNDWI, NDBI, UI, SAVI, and NDMI using different spectral bands and different features are detected and extracted with the help of ArcGIS and MATLAB tools. This study reveals better classification accuracy.

Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues on OpenStreetMap (OSM), a particular case of VGI in recent times. This research examines the hypothesis of evaluating the proficiency of the contributor to assess the credibility of the data contributed. The proposed framework consists of two phases, namely, an exploratory data analysis phase and a learning phase. The former explores OSM data history to perform feature selection, resulting in “OSM Metadata” summarized using principal component analysis. The latter combines unsupervised and supervised learning through K-means for user-clustering and multi-class logistic regression for user classification. We identified five major classes representing user-proficiency levels based on contribution behavior in this study. We tested the framework with India OSM data history, where 17% of users are key contributors, and 27% are unexperienced local users. The results for classifying new users are satisfactory with 95.5% accuracy. Our conclusions recognize the potential of OSM metadata to illustrate the user’s contribution behavior without the knowledge of the user’s profile information.