Author

Abinash Silwal

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Open access Sep 2026

Understanding Land Use/Land Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India

Machine learning (ML)-based classification provides an effective approach for extracting land use/land cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu...

Sanjeev Kumar Jha, Nirbhav, Atul Saini et al. · Atmosphere · 0 citations

Open access Aug 2026

Effects of Riparian Land Use and Land Cover on Water Quality Along the Kansas River: Seasonal and Spatial Dynamics

Riparian land use and land cover (LULC) exerts scale- and season-dependent controls on surface water quality, yet its influence in regulated agricultural–urban rivers is poorly characterized. We combined seasonal t-tests, one-way ANOVA, and redundancy analysis (RDA) at three riparian buffer scales (500, 1000, and 2000...

Gaurav Parajuli, Abinash Silwal, Yogesh Regmi et al. · Ecologies · 0 citations

Open access Aug 2026

Mitigating Class Imbalance and False-Negative Supervision in Remote Sensing Semantic Segmentation Using Object-Centric Patch Sampling

Object-centric patch sampling, a model-independent strategy that anchors each training patch to the geometric centroid of an annotated object, is proposed, a model-independent strategy that improves training data quality at its source and integrates seamlessly into any deep learning pipeline without architectural modif...

Yogesh Regmi, Sandeep Gautam, Gaurav Parajuli et al. · Remote Sensing · 0 citations