Multimodal Dataset for Wildfire Risk Prediction in Cyprus

Wildfires detection is a major issue for authorities. There are various causes of fire events with the most common being human influence. A fire risk prediction model through the analysis of geo-environmental and climate data is important for early warning and fire management. In this work, a dataset from multiple modalities, including road density, travelers, forest-agriculture interface, burned areas from historical fire events, metrological data, land cover, vegetation indices from data cube, is generated. Artificial intelligence and machine learning models can use this multimodal dataset to improve forest fire management.