Remote Monitoring of the Swachh Bharat Mission Using Satellite Imagery and Machine Learning: A Technological Lens on Sanitation Progress in India

The Swachh Bharat Mission (SBM), launched in 2014, represents one of the world's most ambitious national sanitation campaigns, aimed at eradicating open defecation and improving sanitation infrastructure across India. Despite its unprecedented scale and significance, consistent and verifiable monitoring remains a formidable challenge, particularly in remote rural areas where traditional survey methods face logistical constraints and reliability issues. This study presents a novel technological framework that leverages satellite remote sensing integrated with machine learning algorithms, specifically Convolutional Neural Networks (CNNs), to provide independent, scalable, and real-time monitoring of SBM's progress.

Using high-resolution imagery from Landsat and Sentinel-2 satellite platforms spanning 2014-2025, this research demonstrates the efficacy of advanced geospatial technologies in detecting toilet infrastructure, waste treatment facilities, and sanitation-related land-use changes across diverse geographical contexts. The methodology employs deep learning models trained on verified ground-truth data to classify and track sanitation infrastructure with high precision and recall rates.

The findings reveal significant potential for satellite-based monitoring to bridge the gap between reported and actual sanitation progress, particularly in identifying discrepancies between official Open Defecation Free (ODF) declarations and ground realities. This research contributes to the broader discourse on evidence-based governance, technological determinism in public policy, and spatial justice in development interventions. The framework offers policymakers, researchers, and civil society organizations a robust tool for accountability, resource allocation, and strategic decision-making, while supporting global Sustainable Development Goal  on sanitation and hygiene.