Loïc Landrieu

FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these datasets pose major pro...

📢 FLAIR-HUB dataset
A new large-scale, multimodal dataset for land cover and crop type mapping
🤗 Dataset: huggingface.co/datasets/IGN...
📄 Preprint: arxiv.org/abs/2506.07080
🤗 Pretrained models: huggingface.co/collections/...
💻 Code: github.com/IGNF/FLAIR-HUB
🌐 Project : arxiv.org/abs/2506.07080

#CVPR2025 Sat June 14 (PM)
🌍 Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation
@nicolasdufour.bsky.social @vickykalogeiton.bsky.social @davidpicard.bsky.social @loicland.bsky.social
📄 pdf: arxiv.org/abs/2412.06781
🌐 webpage: nicolas-dufour.github.io/plonk.html

AnySat has been accepted as a ✨ highlight at #CVPR2025! See you in Nashville 🎉

We’ll also be presenting this work at:
📍 @egu.eu on 02/04 in Vienna
📍 @esa.int / NASA Workshop on Foundation Models on 05/04 in Rome

We're hiring! IMAGINE @ École des Ponts (Paris area) is opening a 4-year "CV for X" researcher position:
– competitive salary
– no teaching load
– starting pkg ≈ 2 PhDs
– goal: impactful core AI + X (climate, biodiversity, robotics...)
Apply by May 31: imagine-lab.enpc.fr/wp-content/u...

🚨 Workshop alert!

🌍💿 TerraBytes: Towards global datasets and models for Earth Observation
Co-located with ICML 2025 (Vancouver, Canada)

🔗 terrabytes-workshop.github.io

📝 We accept short 4-pages and full 8-pages paper on remote sensing, Earth Observation and machine learning.

📆 Deadline: May 16th

Fantastic idea from @davidpicard.bsky.social. All are welcome, even if you don't have a CVPR paper!

🚨 Future open positions in AI at my home institution* targeted towards young researchers. These are tenure track positions with a good starting package, aimed at guiding you towards an ERC StG within 4 years.

Feel free to contact me in DM.

*ENPC, part of Institut Polytechnique de Paris

Our CVPR Workshop EarthVision, bridging computer vision and Earth observation, was accepted at #CVPR2025 for its 8th edition! Submit your papers by March 3 and stay tuned for more info : grss-ieee.org/events/earth...

Check out our new work with @gastruc.bsky.social and @nicaogr.bsky.social and Clément Mallet! The one-stop shop for multimodal Earth Observation 🤩

Very fine-grained geolocation may be better viewed a retrieval problem. We want to learn generalizable geographic features from images. In osv5m.github.io we added a 1km buffer between train and test to penalize overfitting. The train and test cars are also different to discourage "meta" learning.

Amazing work again by @nicolasdufour.bsky.social, check out the demo with any picture and see if our model can guess its location!
🔗 huggingface.co/spaces/nicol...

Catch us at #NeurIPS2024 Dataset and Benchmarks! 🎉
🖼️ Poster: 5302 | 12/12 | 11 AM
📜 Paper: openreview.net/pdf?id=QpF3D...
🌐 Web & Data: archaeoscape.ai/data/2024
🤝 Joint Work: ENPC + French School of Asian Studies (#EFEO)

Archaeoscape has 2× the area and 3× the labels of comparable archaeological datasets, and is fully open-access! We performed an extensive benchmark of modern CV models and showed that segmenting archaeological traces is a surprisingly tough challenge—even for foundation models.

Khmer vestiges go far beyond monumental stone temples. Wooden and earthen structures faded centuries ago but left subtle geometric elevation patterns in the landscape. Can you spot these hidden structures on the LiDAR elevation maps? 🔍

Airborne #LiDAR has revolutionized the study of ancient rainforest civilizations by seeing through dense canopies. Yet archaeologists still annotate their data manually. Introducing Archaeoscape at #NeurIPS2024 —the first deep learning-scale, open-access archaeological dataset🧵👇

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition!

We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423