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Due to climate change and human activity, wildfires are expected to become more frequent and extreme worldwide, causing economic and ecological disasters. The deployment of preventive measures and operational forecasts can be aided by stochastic modeling that helps to understand and quantify the mechanisms governing the occurrence intensity. We here develop a point process framework for wildfire ignition points observed in the French Mediterranean basin since 1995, and we fit a spatio-temporal log-Gaussian Cox process with monthly temporal resolution in a Bayesian framework using the integrated nested Laplace approximation (INLA). Human activity is the main direct cause of wildfires and is indirectly measured through a number of appropriately defined proxies related to land-use covariates (urbanization, road network) in our approach, and we further integrate covariates of climatic and environmental conditions to explain wildfire occurrences. We include spatial random effects with Matérn covariance and temporal autoregression at yearly resolution. Two major methodological challenges are tackled: first, handling and unifying multi-scale structures in data is achieved through computer-intensive preprocessing steps with GIS software and kriging techniques; second, INLA-based estimation with high-dimensional response vectors and latent models is facilitated through intra-year subsampling, taking into account the occurrence structure of wildfires.