The Sustainable Development Goals (SDGs), comprising of 17 Global Goals, serve as a global framework for addressing various facets of sustainable development. Several of these goals emphasize the crucial role of land management and equitable land distribution in achieving sustainable development objectives. ISO 19152, known as the Land Administration Domain Model (LADM), plays a pivotal role in land administration systems globally. It provides a standardized framework for land management, including land tenure, marine georegulation, valuation, and spatial plan information. This paper explores the integration of land administration indicators within the ISO 19152 standard, aligning them with the United Nations Agenda 2030 SDGs. The process involves a systematic approach to selecting and developing these indicators. In the indicator selection phase, firstly, we establish the foundational lexicon linked to LADM then extract lexemes from SDGs indicators, analyze their semantic relationships, and evaluate their alignment with LADM; secondly, we meticulously evaluated chosen indicators by analyzing their SDG indicator metadata, focusing on the “Method of Computation" section to align these indicators with LADM's basic classes; thirdly, categorizing them based on their association with LADM. This categorization ranges from indicators with no direct correlation to those with full computational interdependence, specifically, they are: Non-Association (Category 0), Full Computational Association (Category 1), Partial Computational Association (Category 2), Indirect Association (Category 3), Association with Other International Standards (Category 4). Following indicator selection, our approach to indicator development is summarized. This entails expressing information from UN SDG "Method of Computation" documents in UML class diagrams, adding operation names and parameters to the most relevant class, and specifying implementation methods for each operation. An in-depth analysis of SDG Indicator 1.4.2 demonstrates the feasibility of deriving indicators entirely from LADM data. Finally, the paper discusses potential future work, including the integration of semantic networks and ontologies for keyword extraction, further exploration of Category 1 Indicators, and practical implementation through case studies, data collection, indicator testing, validation, and reflection.