Domain-agnostic data retrieval has lately become essential amidst the availability of large-scale data from different types of sensors. However, the unavailability of a sufficient amount of samples of certain classes during training curtails the utility of existing retrieval models in remote sensing (RS) applications. Here, we propose a novel framework for zero-shot intermodal data retrieval of RS data. Thereupon, we design an encoder–decoder structure that ensures enhanced overlapping among the two data domains utilizing cross-triplet and cross-projection loss functions. Furthermore, we propose a sketch-based representation of the RS database Earth on Canvas with diverse classes. We perform a thorough benchmarking of this data set and demonstrate that the proposed framework outperforms state-of-the-art methods for zero-shot sketch-based retrieval framework for RS data.