In recent years, the auto-extraction of topographic features has gathered significant attention due to its potential to revolutionize geospatial data processing. Nevertheless, traditional techniques struggle to handle the variability of topographic data, which leads to suboptimal performance in real-world applications. Hence, this paper proposes a novel deep learning-based framework, “HGFeNet” that involves Convolutional Neural Networks (CNNs) and hypergraphs for efficient and accurate extraction of topographic features from complex terrain data. CNNs are employed to learn spatial hierarchies and extract relevant features from raw topographic maps, while hypergraph structures are introduced to model and capture high-order relationships between features, enhancing the representation of intricate topographic patterns. The integration of CNNs with hypergraphs enables the system to address challenges such as noise reduction and the preservation of topological context in large-scale datasets. Experiments using the Massachusetts Roads dataset show that the proposed approach performs significantly in terms of accuracy and computational efficiency than the existing models. The results highlight the effectiveness of the model as a robust solution for advanced terrain modeling and spatial data processing.