Developing Geospatial AI Model for Land Record Management System: Study Area Delhi

Abstract

The land record management system in certain regions faces challenges related to corruption and transparency issues. These difficulties are compounded by intermediaries like brokers who can manipulate data and create fraudulent records. The land administration in these areas contends with persistent problems of corruption and inefficiency within a manual, paper-based system that is susceptible to manipulation and fraud. This history of issues undermines fair land use and development, especially impacting vulnerable communities and complicating ownership verification and dispute resolution. Despite initiatives such as the Digital India Land Records Modernization Programme, which signifies progress toward digitization, persistent challenges remain, including legacy inconsistencies and cybersecurity vulnerabilities. This thesis is focused on addressing the specific challenges within Delhi's urban landscape, aiming to explore how emerging technologies like Geo-Spatial Artificial Intelligence (GeoAI) and blockchain can revolutionize land record management and combat corruption. The use of blockchain can also streamline documentation and record-keeping, and increase the efficiency of the real estate market. However, the implementation of blockchain in the Indian land record management system faces challenges such as public key infrastructure, privacy rules, and security issues. Despite these challenges, the use of blockchain technology holds promise for addressing the problems of corruption and lack of transparency in India's land record management system. This research aims to develop a comprehensive Land Information Management System (LIMS) that integrates Geo-Spatial Artificial Intelligence and blockchain technology to enhance transparency, efficiency, and accountability in urban land governance at the community level in Delhi. To achieve this aim, several objectives have been outlined. First, the study seeks to review and assess current land record maintenance and management practices in selected case study areas of Delhi. Second, it aims to identify key parameters for an Extended Urban Land Information System (EULIS) and examine its impact on urban land management. Third, the research evaluates the strengths and limitations of existing urban Land Information Systems (LIS) and proposes enhancements to extend LIS entities. Lastly, the study Geospatial AI Model for Land Record Management System: Study area Delhi vi aims to develop a conceptual model for a GeoAI-powered LIMS augmented with blockchain technology to streamline land administration. The scope of this research extends to designing and implementing a tailored LIMS for community-level areas within Delhi while considering broader implications of adopting GeoAI and blockchain technology in land governance, including scalability, data privacy, and institutional frameworks. Methodologically, this study adopts a multi-faceted approach. A conceptual framework for the LIMS integrating GeoAI and blockchain technology is developed. Various parameters related to land records, urban management, corruption indices, and technological feasibility are considered. The sampling strategy involves targeted engagement with stakeholders from land administration, urban planning, and technology development sectors. Advanced analytical tools are utilized for data processing and model development. Data collection includes gathering spatial and non-spatial data from satellite imagery, government databases, and field surveys. The research findings reveal critical insights into current land management practices and challenges. Through a comprehensive review of literature, stakeholder consultations, and field surveys, the study identifies prevalent issues such as manual record-keeping, processing delays, and susceptibility to manipulation. Key parameters for an effective EULIS (Extended Urban Land Record Management System) tailored to Delhi's context are established, encompassing factors like land use classification, ownership records, transaction history, and spatial planning regulations. The evaluation of existing urban LIS highlights limitations in data integration, accessibility, and real-time updates, leading to recommendations for extending the functionalities of existing systems. Based on these findings, a novel framework for a GeoAI-powered LIMS integrated with blockchain technology is developed. This framework addresses identified challenges and incorporates key parameters for an EULIS. Additionally, a user-friendly mobile application named EULIS is designed to provide stakeholders with access to land information, allow reporting of discrepancies, and facilitate tracking of land transactions. In conclusion, the adoption of GeoAI and blockchain technology holds significant potential to transform land administration, promote sustainable urban development, and enhance spatial planning and policy formulation. Policy interventions are recommended to address corruption and inefficiencies in land Geospatial AI Model For Land Record Management System: Study area Delhi governance. The study acknowledges limitations such as technical constraints and data privacy concerns, suggesting future research directions to overcome these challenges and further refine the proposed LIMS framework. Overall, this research contributes to advancing the discourse on leveraging technology for transparent, efficient, and accountable land management systems in urban contexts like Delhi.