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Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression within remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks (Dataset and code available here: dgominski.github.io/drift/). To match real-world applicative interests, we compare methods through a restrictive setup where no prior on the target domain is available during training, and models are adapted with limited information during testing. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression as a strong baseline for transduction in low-data regimes. Our comparison highlights the comparative advantages of inductive and transductive methods in cross-domain regression.

S. Li and D. Gominski—Equal contribution.

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Acknowledgements

PC acknowledges support from the European Space Agency Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/4000123662) and RECCAP2 project 1190 (ESA ESRIN/4000123002/ 18/I-NB) and the ANR BMBF French German AI4FOREST (ANR-22-FAI1-0002).

MB and SL were supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY) and a DFF Sapere Aude grant (no. 9064-00049B).

DG ackowledge support by the European Union’s Eurostars programme through the C-Trees project, grant number E114613.

XT acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY).

Author information

Authors and Affiliations

  1. University of Copenhagen, Copenhagen, Denmark

    Sizhuo Li, Dimitri Gominski, Martin Brandt & Xiaoye Tong

  2. LSCE, Paris, France

    Sizhuo Li & Philippe Ciais

Authors
  1. Sizhuo Li
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  2. Dimitri Gominski
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  3. Martin Brandt
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  4. Xiaoye Tong
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  5. Philippe Ciais
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Corresponding author

Correspondence to Dimitri Gominski .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Li, S., Gominski, D., Brandt, M., Tong, X., Ciais, P. (2024). Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15135. Springer, Cham. https://doi.org/10.1007/978-3-031-72980-5_6

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