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Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

Nowadays, with the increasing number of data sources, especially in environmental domain, earth observation programs face major challenges for environmental data exploitation, mainly due to data sources heterogeneity of different types such as access techniques, used protocols, languages, data formats, etc. Although typical solutions abstract from this heterogeneity with a layer of data services, the development of such systems remains tedious in this context. In this paper, we propose an approach based on Model-Driven Engineering (MDE) combined with semantic annotations, to automate data service development on top of data sources. Our work contributes to the development of integrated service-based architectures driven by automatic service generation, data integration from existing environmental systems and automatic service annotations. Our solution, applied to the detection of natural disasters, provides 1) appropriate modelling of data sources and services to apply model-to-text (M2T) transformations, 2) automatic generation of Representational State Transfer (REST) data service code template, 3) automatic generation of semantically annotated Hypermedia-based descriptors of these services. We have implemented and evaluated our solution with a set of real data sources provided by the Sahara and Sahel Observatory (OSS), OpenWeatherMap and CHIRPS.