Mamba for Landslide Detection: A Lightweight Model for Mapping Landslides With Very High-Resolution Images

Heavy rainfall and earthquake in mountain areas usually trigger numerous landslides. Fast and accurate mapping of landslides is crucial for risk management and emergency rescue. Deep learning-based landslide detection methods can automate identification, but convolutional neural network (CNN) models focus primarily on local features, often missing crucial global context in landslide images. Conversely, Transformer-based models excel at capturing global features but are hindered by high computational complexity. As a result, existing detection models struggle to strike an effective balance between accuracy and efficiency. To address this issue, this article presents a lightweight landslide detection method based on the newly proposed Mamba network. Specifically, a landslide detection model named SegMamba2D with an encoder–decoder structure is proposed. In the encoder, the Mamba network is used to extract multiscale features. A state-space model (SSM) is employed to reduce computational complexity while maintaining accuracy. In the decoder, a multilayer perceptron is used to build a lightweight decoder, ensuring that the model’s overall complexity remains low. The experimental results on both public and new datasets demonstrate that SegMamba2D achieves a superior landslide detection accuracy, with an approximately 2% improvement in F1 score across various scenarios over conventional models, while significantly reducing computational costs. Additionally, SegMamba2D demonstrates robust generalization performance across diverse research areas. These advancements highlight the model’s potential to enhance accuracy in creating landslide inventories and expedite emergency response times during landslide disasters.