Multidisciplinary Institute in Artificial intelligence

 
Sophie Giffard-Roisin
Researcher, IRD, ISTerre, Grenoble
sophie.giffard@univ-grenoble-alpes.fr

Jocelyn Chanussot
Research Director, INRIA, Grenoble
Jocelyn.chanussot@inria.fr

DESCRIPTION

Satellite based remote sensing, using a variety of sensing modalities (optical, radar, hyperspectral, lidar) offers a unique source of information to monitor the environment, with fine spatial resolution, wide coverage and frequent revisit. This enables addressing the challenge of natural hazard monitoring an forecasting, which has a significant societal impact. To fully harness the potential of remote sensing data, advanced algorithms in machine learning, deep learning, or more broadly artificial intelligence, must be developed. Gathering an interdisciplinary team of experts, from data science, environmental and Earth sciences, as well as social sciences, this chair will focus on three important topics: forest monitoring, Earth deformation estimation and volcanic inverse modeling. From a methodological point of view, research will be conducted on the analysis of multimodal and time series deep learning, deep learning inverse problems and foundation models.

ACTIVITIES

The chair is organized in 3 work-packages with a total of 6 main tasks. 4 PhD students will be hired as well as some post-doctoral and interns fellow.

WP1: Remote Sensing, AI and forestry

• Tree mortality in mountain forests in the face of increasing fire risk
• Socio-spatial implementation of the Morvan Christmas trees

WP2: Multimodality for Earth deformation estimation

• Multi-modal estimation of earth displacement fields: application to
• earthquakes.
• Monitoring of critical risk zones near infrastructures

WP3: Volcanic inverse modeling

• Fast estimation of volcanic model parameters
• Rapid classification of volcanic mechanism

CHAIR EVENTS

SELECTED LIST OF PUBLICATIONS

• [1] Dutrieux, R., Ose, K., de Boissieu, F., Féret, J.-B., 2024. fordead: a python package for vegetation anomalies detection from SENTINEL-2 images. https://doi.org/10.5281/zenodo.12802456
• [2] Tolan, J., Yang, H.I., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J. and Moutakanni, T., 2024. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment, 300, p.113888
• .[3] Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Du, Q., & Zhang, B. (2020). More diverse means better: Multimodal deep learning meets remote-sensing imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4340-4354.
• [4] Guo, X., et al. "Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
• [5] Lacoste, A., et al. "Geo-bench: Toward foundation models for earth monitoring." Advances in Neural Information Processing Systems 36 (2024).
• [6] Ilg, E., et al. "Flownet 2.0: Evolution of optical flow estimation with deep networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
• [7] Teed Z., and Jia Deng. "Raft: Recurrent all-pairs field transforms for optical flow." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. Springer International Publishing, 2020.
• [8] Albino, F., Amelung, F., & Gregg, P. (2018). The role of pore fluid pressure on the failure of magma reservoirs: insights from Indonesian and Aleutian arc volcanoes. Journal of Geophysical Research: Solid Earth, 123(2), 1328-1349.
• [9] Dumont, Q., Cayol, V., & Froger, J. L. (2024). Is stress modeling able to forecast intrusions and slip events at Piton de la Fournaise volcano. Earth and Planetary Science Letters, 626, 118494.
• [10] Anantrasirichai, N., Biggs, J., Albino, F., & Bull, D. (2019a). A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sensing of Environment, 230, 111179.
• [11]Anantrasirichai, N., Biggs, J., Albino, F., & Bull, D. (2019b). The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series. Geophysical Research Letters, 46(21), 11850-11858.
• [12] Biggs, J., Anantrasirichai, N., Albino, F., Lazecky, M., & Maghsoudi, Y. (2022). Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery. Bulletin of Volcanology, 84(12), 100.
• [13] Gaddes, M. E., Hooper, A., & Bagnardi, M. (2019). Using machine learning to automatically detect volcanic unrest in a time series of interferograms. Journal of Geophysical Research: Solid Earth, 124(11), 12304-12322.
• [14] Gaddes, M., Hooper, A., & Albino, F. (2024). Simultaneous classification and location of volcanic deformation in SAR interferograms using a convolutional neural network. Earth and Space Science, 11(6), e2024EA003679.
• [15] Lopez-Uroz L., Yan Y., Benoit A., Albino F., Bouygues P., Giffard-Roisin S., Pinel V., Exploring Deep Learning for Volcanic Source Inversion, IEEE Transactions on Geosciences & Remote Sensing, vol.62, doi: 10.1109/TGRS.2024.3494253
• [16] Lahssini K., Teste F., Dayal K. R., Durrieu S., Ienco D. and Monnet J.-M., "Combining LiDAR Metrics and Sentinel-2 Imagery to Estimate Basal Area and Wood Volume in Complex Forest Environment via Neural Networks," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 4337-4348, 2022, doi: 10.1109/JSTARS.2022.3175609
• [17] Dayal K. R., Durrieu S., Lahssini K., Ienco D. and Monnet J.-M., "Enhancing Forest Attribute Prediction by Considering Terrain and Scan Angles From Lidar Point Clouds: A Neural Network Approach," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3531-3544, 2023, doi:10.1109/JSTARS.2023.3263595
• [18] Montagnon, T., Hollingsworth, J., Pathier, E., Marchandon, M., Dalla Mura, M., Giffard-Roisin, S., & Dalla, Mura. (2024). GeoFlowNet: Fast and Accurate Sub-pixel Displacement Estimation From Optical Satellite Images based on Deep Learning.
• [19] Okada, Y. (1985). Surface deformation due to shear and tensile faults in a half-space. Bulletin of the seismological society of America, 75(4), 1135-1154.
• [20] - Cayol V., Carry T., Michon L, Chaput M., Famin V., Bodart O., Froger J.L., Romagnoli C. (2014), Sheared sheet intrusions as mechanism for lateral flank displacement on basaltic volcanoes: Applications to Réunion Island volcanoes, Journal of Geophysical Research, 119, 7607-7635.
• [21] Dumont, Q., Cayol, V., Froger, J. L., & Peltier, A. (2022). 22 years of satellite imagery reveal a major destabilization structure at Piton de la Fournaise. Nature communications, 13(1), 2649.