Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine.

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

A Comparative Evaluation of the Effectiveness of Added Auxiliary Features, Occlusal Surface Modifications, and Reduction of Total Occlusal Convergence on the Resistance of Full Veneer Metal Crowns on a Molar Tooth with Inadequate Resistance Form: An in vitro Study.

INTRODUCTION: Single crowns or fixed partial dentures retainers usually get dislodged due to inadequate resistance form. Hence, it is prudent to evaluate features of a tooth preparation, which can prevent these failures. AIM: To evaluate the effect of auxiliary features, occlusal surface modifications, and total occlusal convergence (TOC) on the resistance of a full veneer crown. MATERIALS AND METHODS: An ivorine mandibular molar tooth was prepared with features of inadequate resistance form, i.e., 2.5 mm axial wall height and TOC of 20°. Seven auxiliary preparation features were subsequently added one by one to it. They were mesiodistal grooves, buccolingual and mesiodistal grooves, buccolingual grooves, mesiodistal boxes, occlusal inclined planes, 8° reduced TOC in the cervical aspect, and mesiodistal grooves added to 8° reduced TOC in the cervical aspect. Ten dies with their respective crowns were prepared for each group. Resistance testing of all the samples was performed on the INSTRON testing machine. RESULTS: Modification of the overtapered die preparation by reducing the TOC to 8° in the cervical 1.5 mm of the axial wall and then subsequently adding mesiodistal grooves to the reduced TOC cervically offered the greatest resistance to dislodgment statistically. CONCLUSION: For an overtapered preparation, reducing the TOC to 8° in the cervical aspect and subsequently adding proximal grooves can provide maximum resistance form.