Satellite image deep learning The FLAIR land cover mapping challenge
In this episode, I caught up with Nicolas Gonthier to learn about the FLAIR land cover mapping challenge.
In this challenge, 20cm resolution aerial imagery was used to create high-quality annotations. This data was paired with a time series of medium-resolution Sentinel 2 images to create a rich, multidimensional dataset. Participants in the challenge were able to surpass the baseline solution by 10 points in the target metric, representing a significant step forward in land cover classification capabilities.
The dataset is now being expanded to cover a larger area and incorporate additional imaging modalities, which have been shown to improve performance on this task. Nicolas also provided important context about the objectives of the organisation running this challenge, such as the need to balance model performance with processing costs.
* 🖥️ FLAIR website
* 🖥️ Page on the objectives of FLAIR
* 📖 The NeuRIPS paper about FLAIR
* 🖥️ IGN datahub
* 📺 Video of this conversation on YouTube
Bio: Nicolas Gonthier is a R&D project manager in the innovation team at IGN the French National Institute of Geographical and Forest Information. He received a MSc. in data science from ISAE Supaero in 2017 and a Ph.D. degree in computer vision from Université Paris Saclay - Télécom Paris in 2021. His work focus on deep learning for earth observation (land cover segmentation, change detection, etc) and computer vision for geospatial data. He participate to different research and innovation projects.
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