Remote Sensing

Phenology-Informed Identification of Opium Poppy Cultivation: Explainable Feature Extraction from Multispectral Time Series Data

Machine learning classification achieving 98.7% F1-score for opium poppy detection with rigorous spatial cross-validation and explainable AI analysis.

avatar
Prashanth Reddy Putta

Satellite-based Rabi rice paddy field mapping in India: A case study on Telangana state

Phenology-driven framework achieving 93.3% accuracy in mapping 732,345 hectares of rice cultivation across Telangana, India.

avatar
Prashanth Reddy Putta
Paddy Field Mapping using NDVI Thresholding and Machine Learning featured image

Paddy Field Mapping using NDVI Thresholding and Machine Learning

Published Research: This project evolved into a peer-reviewed publication achieving 93.3% accuracy in mapping 732,345 hectares of rice cultivation across Telangana, India. In this …

Multi-Sensor Land Cover Classification in the Pavia Region Using Google Earth Engine featured image

Multi-Sensor Land Cover Classification in the Pavia Region Using Google Earth Engine

This project implements a land cover classification system for the Pavia region in Italy, utilizing a fusion of Sentinel-1 and Sentinel-2 satellite data within the Google Earth …