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EmoGRACE framework

EmoGRACE: Advancing Emotion Analysis for Social Media Data

In the discipline of Natural Language Processing (NLP), sentiment analysis has evolved from basic polarity classification (positive vs. negative) of entire texts or sentences to more nuanced aspect-based sentiment analysis (ABSA), which identifies sentiments related to specific terms or phrases within a text.
FireSim simulations of Palisades wilfire (TEMA Project. Tecnosylva. Wildfire simulation research. 2025-04-01. Own elaboration).

Technosylva: The Spanish Brain Fighting Wildfires in California

In the fight against infernos, technology has emerged as a powerful tool and at the forefront of this innovation is Technosylva. Over the last decade, Technosylva has played a crucial role in California’s wildfire prevention and response efforts, developing cutting-edge technology that helps firefighters and emergency responders make informed decisions and act swiftly.
Flood Sentinel-3B scene from Jan. 9th 2025 (18:24), showing the smoke plumes produced by the Palisades Fire (left) and Eaton Fire (right; source: DLR)

Multi-sensor Near-Realtime Burnt Area Monitoring of the devastating Palisades and Eaton Fires

Starting on 7 January 2025, several severe wildfires raged in the Greater Los Angeles area. The department for Georisks and Civil Security at the German Aerospace Center (DLR) prepared damage assessment maps through automated analyses of satellite data. Burnt area perimeters could have been derived in near-real time after the acquisition from the Copernicus Data Space Ecosystem.
Drone ©Bavarian Red Cross HQ

Drones in Search and Rescue Operations

In the last decade, however, the development of drones has accelerated significantly. The aircraft have become smaller and more affordable. Control systems have become simpler and smarter, and the range of applications has become increasingly diverse. Starting from research purposes, drones have found wide usage in all Public Protection and Disaster Relief (PPDR) organisations.
"Smoke" – © Kaibab National Forest, 2020, CC BY 2.0

Reactive Model Correction: Mitigating Spurious Correlations in Natural Disaster Management

Artificial Intelligence (AI) has become a critical tool in natural disaster management, enabling real-time predictions, risk assessments, and decision-making. However, the effectiveness of AI models can be undermined by spurious correlations—where irrelevant features in the training data influence predictions. These shortcuts, often due to imperfections in training datasets, pose serious risks in high-stakes scenarios.