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Swarm intelligence (SI) is an Artificial Intelligence technique based on the study of collective behaviour in decentralized, selforganizing systems. It enables relatively simple agents to collectively perform complex tasks, which could not be performed by individual agents separately. Particles can interact either directly or indirectly (through the environment). The key to maintain global, self-organized behaviour is social interaction ie information sharing between the system's individuals. Hence, information sharing is essential in swarm intelligence. In this paper, we highlight how the concept of information sharing in various swarm-based approaches can be utilised as a perspective application towards the elicitation of natural terrain features. The paper provides a mathematical formulation of the concept of information sharing in each of the swarm intelligence techniques of Biogeography based optimization (BBO), Ant Colony Optimization (ACO), Particle Swarm optimization (PSO) and Bee Colony Optimization (BCO) which are the major constituents of the SI techniques that have been used till date for classifying topographical facets over natural terrain.