Automatic identification of landslide disasters from remote sensing images based on deep learning models is an effective method, but most of the existing models use multi-temporal images or geological data to improve the accuracy, which suffers from problems of high model complexity and data acquisition difficulty. We proposed a Mv2_SA_DeepLabv3+ model, which is a lightweight deep learning network for detecting landslide disaster occurrence areas from remote sensing images captured in a single temporal image. Mv2_SA_DeepLabv3+ model is based on the Deeplabv3+ network structure. Firstly, it used the lightweight MobileNetV2 network as the backbone network for feature extraction to reduce the complexity of the model. Secondly, it improves the Atrous Spatial Pyramid Pooling (ASPP) module to obtain more features to improve the model accuracy. Finally, the loss function is optimized by combining CrossEntropy Loss and Dice Loss to solve the problem of positive and negative category imbalance of the samples. Furthermore, two publicly available landslide disaster remote sensing image datasets were utilized to validate the proposed model. The experiment results indicated that the Mv2_SA_DeepLabv3+ model shows a good performance, with the Kappa coefficient and the F1 score of 0.83 and 86.84%, which outperforms the original UNet (improved of 0.11 and 8.34%). These results demonstrate the model can obviously overcome the interference of irrelevant information, so as to accurately recognize the landslide area. Besides, the model has a low computational complexity, which facilitates the rapid deployment of the model and provides data support for further disaster emergency rescue.