A frequency-domain and context-guided deep learning transformer architecture for real-time landslide detection

A Hybrid Deep Learning Approach for Early Detection of Wildfires Using Transformer Models and Feature Fusion Techniques

Natural disasters, particularly wildfires, pose a serious threat to ecosystems, human lives, and infrastructure, making rapid and accurate fire detection essential for effective disaster management. Despite recent advances in deep learning-based wildfire detection, conventional CNN-based approaches are inherently limit...

A Landslide Detection Network Integrating Mamba and Deformable Sliding Window Attention

Landslide detection is essential for geological disaster mitigation, yet existing deep learning methods still struggle with the high cost of global feature modeling, limited receptive fields in window-based attention, and insufficient fusion of local and global information. To address these challenges, we propose MTTNe...

LMAF-Net: Lightweight Multi-Attention Fusion Landslide Identification Network

Landslides, as sudden, destructive geological events, threaten human lives, property, and infrastructure. Remote sensing and UAV imagery provide essential data for landslide identification, yet landslide targets often appear as sparse small objects, and existing lightweight models still lack sufficient accuracy under s...

EfficientNet-B4 and Temporal Transformer Framework for Real-Time Wildlife Activity Recognition and Automated Alert Generation

InSAR-Guard: Lightweight AI Framework for Landslide Hazard Detection

Landslides are a type of natural disaster that causes massive loss of life and property. Continuous observation of ground deformation is necessary for early warning and mitigation of risks. Interferometric Synthetic Aperture Radar (InSAR) technology facilitates large-scale, all-weather observation of ground deformation...

A Lightweight Forest Fire Detection Model with Multi-Granularity Vision-Language Enhancement

In recent years, Unmanned Aerial Vehicle (UAV)-based object detection technology has demonstrated immense potential for forest fire monitoring in complex environments. However, constrained by the drastic multi-scale variations in fire targets, severe background interference, and the limited computational resources of e...