Recurrent Neural Networks (RNNs) are currently state of art tools for processing and classifying data sequences. This work aims to exploit these capabilities in Long-Short Term Memory (LSTM) networks which are a powerful variant of RNNs for encoding the birds' trajectory data into state vectors. These vectors should encapsulate the contextual information about the immediate trajectory coordinates. Therefore, they can generate new trajectory points based on their state and the latest output. However, probabilistic behavior of birds, effects of environment and noisy nature of measurements pose challenges for training and testing of the LSTM network models. This study solely focuses on the effects of spatial context and their significance in subsequent outputs to achieve compact representation of the traversed trajectory. Therefore, trajectory coordinates of birds were used as input to LSTM networks to learn spatial path features encoded in hidden vectors of the network. In the end, t-SNE method is used to visualize the state vectors in lower dimensional space embeddings and It was observed that these vectors contained contextual information about the traversed path.