Getnet Demil
Jenberia

1. 01

   Multi-Modal Satellite Data Fusion

   Developing CrossFuse, a hybrid CNN-Transformer architecture that fuses Sentinel-1 SAR and Sentinel-2 optical imagery to produce high-resolution snow cover and water equivalent estimates for runoff prediction.

   • Sentinel-1 SAR
   • Sentinel-2
   • CNN-Transformer
   • Snow Hydrology
2. 02

   Deep Learning for Snow Segmentation

   Designing mDeepLabV3+ with dilated convolutions and a ResNet backbone to reliably separate snow from clouds in Sentinel-2 multispectral imagery, enabling year-round snow monitoring for water resource management.

   • DeepLabV3+
   • ResNet
   • Semantic Segmentation
   • Cloud Detection
3. 03

   AI for Climate & Emergency Response

   Leveraging NLP and social media analytics for real-time local climate impact monitoring, and building AI-enhanced early warning systems that improve emergency communication across Nordic countries.

   • NLP
   • Social Media Analytics
   • Early Warning Systems
   • Climate AI

Research Interests

• From Earth Observation to Earth Intelligence

  Advancing the shift from raw satellite data toward decision-ready insight: applying AI, multimodal fusion, and geospatial foundation models to turn Earth observation streams into predictive, actionable intelligence for environmental monitoring, early warning, and resource management.

  • Earth Intelligence
  • Predictive Modeling
  • Digital Twin
  • Decision Support

• Geospatial AI & Earth Observation

  Designing scalable AI pipelines for large-area Earth observation, from pixel-level classification to geophysical parameter retrieval across diverse landscapes and sensor modalities.

  Specialising in snow cover mapping, fractional snow estimation, and hydrological parameter retrieval using multi-sensor satellite archives at continental scale.

  • SAR Fusion
  • Segmentation
  • GeoAI
  • Sentinel

• Multi-Sensor & Multimodal Fusion

  Cross-modal learning architectures that jointly process SAR, optical, multispectral, thermal, LiDAR, DEM, and climate reanalysis data, unlocking richer scene understanding than any single source.

• Computer Vision for Remote Sensing

  Semantic segmentation, object detection, and scene understanding tailored to satellite and aerial imagery, addressing scale, illumination, and spectral complexity.

• Foundation Models for EO

  Fine-tuning and adapting large geospatial foundation models (SAM, DINOv2, SatMAE, Prithvi) to downstream EO tasks: change detection, land cover, and parameter retrieval.

• Environmental & Climate Monitoring

  Multi-temporal satellite analysis for hydrology, flood mapping, land use change, vegetation dynamics, and long-term cryosphere and ecosystem monitoring.