Large-Scale Landslides Detection from Satellite Images with Incomplete Labels
This addresses landslide detection for disaster management in mountainous areas, but appears incremental as it evaluates existing methods without new breakthroughs.
The paper tackled landslide detection from satellite images using deep neural networks to aid disaster response, demonstrating potential for meaningful social impact in disasters and rescue.
Earthquakes and tropical cyclones cause the suffering of millions of people around the world every year. The resulting landslides exacerbate the effects of these disasters. Landslide detection is, therefore, a critical task for the protection of human life and livelihood in mountainous areas. To tackle this problem, we propose a combination of satellite technology and Deep Neural Networks (DNNs). We evaluate the performance of multiple DNN-based methods for landslide detection on actual satellite images of landslide damage. Our analysis demonstrates the potential for a meaningful social impact in terms of disasters and rescue.