Loïc Landrieu
loicland.bsky.social
Loïc Landrieu
@loicland.bsky.social

Senior researcher at IMAGINE (ENPC, LIGM).

Machine learning & computer vision for 3D + geospatial + historical data.

loiclandrieu.com

Engineering 30%
Environmental science 27%

Reposted by Loïc Landrieu

📢 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
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...
arxiv.org

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
#CVPR2025 Sat June 14 (PM) ✨ Highlight
🛰️ AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
@gastruc.bsky.social @nicaogr.bsky.social @loicland.bsky.social
📄 pdf: arxiv.org/abs/2412.14123
🌐 webpage: gastruc.github.io/anysat

Reposted by Loïc Landrieu

#CVPR2025 Sat June 14 (PM) ✨ Highlight
🛰️ AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
@gastruc.bsky.social @nicaogr.bsky.social @loicland.bsky.social
📄 pdf: arxiv.org/abs/2412.14123
🌐 webpage: gastruc.github.io/anysat

Reposted by Loïc Landrieu

#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
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...

Reposted by Loïc Landrieu

🚨 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!
🔥🔥🔥 CV Folks, I have some news! We're organizing a 1-day meeting in center Paris on June 6th before CVPR called CVPR@Paris (similar as NeurIPS@Paris) 🥐🍾🥖🍷

Registration is open (it's free) with priority given to authors of accepted papers: cvprinparis.github.io/CVPR2025InPa...

Big 🧵👇 with details!
🔥🔥🔥 CV Folks, I have some news! We're organizing a 1-day meeting in center Paris on June 6th before CVPR called CVPR@Paris (similar as NeurIPS@Paris) 🥐🍾🥖🍷

Registration is open (it's free) with priority given to authors of accepted papers: cvprinparis.github.io/CVPR2025InPa...

Big 🧵👇 with details!

Reposted by Loïc Landrieu

🚨 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 🤩
🤔 What if embedding multimodal EO data was as easy as using a ResNet on images?
Introducing AnySat: one model for any resolution (0.2m–250m), scale (0.3–2600 hectares), and modalities (choose from 11 sensors & time series)!
Try it with just a few lines of code:

Reposted by Loïc Landrieu

🤔 What if embedding multimodal EO data was as easy as using a ResNet on images?
Introducing AnySat: one model for any resolution (0.2m–250m), scale (0.3–2600 hectares), and modalities (choose from 11 sensors & time series)!
Try it with just a few lines of code:

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.
OSV-5M
osv5m.github.io

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...
🌍 Guessing where an image was taken is a hard, and often ambiguous problem. Introducing diffusion-based geolocation—we predict global locations by refining random guesses into trajectories across the Earth's surface!

🗺️ Paper, code, and demo: nicolas-dufour.github.io/plonk
🌍 Guessing where an image was taken is a hard, and often ambiguous problem. Introducing diffusion-based geolocation—we predict global locations by refining random guesses into trajectories across the Earth's surface!

🗺️ Paper, code, and demo: nicolas-dufour.github.io/plonk

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🧵👇

Reposted by Loïc Landrieu

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