Artificial intelligence for country-scale land cover description.

The National Institute of Geographic and Forest Information (IGN) has developed Artificial Intelligence (AI) models that describe land cover at the pixel level from IGN aerial images. This is part of the production process for the Large Scale and Land Use Reference (OCS GE). This contribution is threefold:

Methodology: the training strategy and the use of these models will be reviewed by focusing on i) the selection of the task performed by the models, ii) the approach for choosing and producing learning samples and iii) the training strategy to generalize to the scale of Metropolitan France. The evaluation of the models using various metrics will also be discussed. Visuals will be provided to illustrate the quality of the results. Furthermore, we will explain how AI products are incorporated into the production of the OCS GE.

Continuous improvement: the models are continuously improved, particularly through the implementation of FLAIR (French Land cover from Aerospace ImageRy) challenges towards the scientific community. The challenges FLAIR#1 and FLAIR#2 dealt with model generalization and domain adaptation as well as data fusion, i.e., how to develop an AI model that can process very high spatial resolution images (e.g., IGN aerial acquisitions) and satellite image time series (Sentinel-2 images) as input. We will both review the challenges implementation and the obtained results, leveraging convolutional and attention-based models, ensembling methods and pseudo-labelling. As the AI model for land cover goes far beyond the context of OCS GE production, additional experiments outside of the challenges will be discussed, allowing the development of additional AI models to process other modalities (very high spatial resolution satellite images, historical images, etc.).

Open access: all source code and data, including AI land cover predictions maps, are openly distributed. These resources are distributed via the challenges and as products (CoSIA: Land Cover by Artificial Intelligence) by a dedicated platform, which is of interest for AI users and non-specialists including users from the geoscience and remote sensing community.