Multimodal Vision Research Laboratory

MVRL

Research Area: Biodiversity and Conservation

We develop novel algorithms for monitoring environmental changes and biodiversity at a global scale. Our work focuses on using computer vision and remote sensing techniques to track changes in ecosystems, monitor species populations, and support conservation efforts through automated analysis of imagery from satellites, webcams, and other imaging sources. Recent research includes unified embedding spaces for ecological applications (TaxaBind), language-driven hierarchical species distribution modeling (LD-SDM), global and local entailment learning for natural world imagery (RCME), and probabilistic masked multimodal embedding models for ecology (ProM3E). We also work on cross-view contrastive masked autoencoders for bird species classification and mapping (BirdSat), and improved canopy vertical structural diversity mapping across varied topographies using deep learning techniques.

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Publications

  1. Sastry S, Khanal S, Dhakal A, Lin J, Cher D, Jarosz P, Jacobs N. 2025. ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology .
    paper bibtex Remote Sensing GeoAI Ecology
  2. Thumbnail for LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
    Sastry S, Xing X, Dhakal A, Khanal S, Ahmad A, Jacobs N. 2025. LD-SDM: Language-Driven Hierarchical Species Distribution Modeling. In: Computer Vision for Ecology (IEEE International Conference on Computer Vision (ICCV) Workshops).
    paper bibtex Remote Sensing Ecology GeoAI Vision-Language
  3. Thumbnail for Global and Local Entailment Learning for Natural World Imagery
    Sastry S, Dhakal A, Xing E, Khanal S, Jacobs N. 2025. Global and Local Entailment Learning for Natural World Imagery. In: IEEE International Conference on Computer Vision (ICCV).
    code website paper bibtex Remote Sensing Ecology GeoAI
  4. Thumbnail for TaxaBind: A Unified Embedding Space for Ecological Applications
    Sastry S, Khanal S, Dhakal A, Ahmad A, Jacobs N. 2025. TaxaBind: A Unified Embedding Space for Ecological Applications. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    code website paper bibtex Remote Sensing Ecology GeoAI
  5. Sastry S, Xing X, Dhakal A, Khanal S, Ahmad A, Jacobs N. 2024. LD-SDM: Language-Driven Hierarchical Species Distribution Modeling. In: American Geophysical Union (AGU) Fall Meeting Abstracts.
    bibtex Remote Sensing Ecology GeoAI
  6. Ahmad A, Dhakal A, Sastry S, Khanal S, Xing E, Jacobs N. 2024. Improved Canopy Vertical Structural Diversity Mapping Across Varied Topographies Using Deep Learning Techniques. In: American Geophysical Union (AGU) Fall Meeting Abstracts.
    bibtex Remote Sensing Ecology GeoAI
  7. Thumbnail for BirdSat: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
    Sastry S, Khanal S, Dhakal A, Huang D, Jacobs N. 2024. BirdSat: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    code paper bibtex Remote Sensing Ecology
  8. Thumbnail for Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest
    Maretto RV, Fonseca LMG, Jacobs NB, Körting TS, Bendini HN, Parente LL. 2020. Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest. IEEE Geoscience and Remote Sensing Letters 18:771–775. DOI: 10.1109/LGRS.2020.2986407.
    paper doi bibtex Remote Sensing Ecology GeoAI
  9. Thumbnail for Deep Learning for Conifer/Deciduous Classification of Airborne LiDAR 3D Point Clouds Representing Individual Trees
    Hamraz H, Jacobs NB, Contreras MA, Clark CH. 2019. Deep Learning for Conifer/Deciduous Classification of Airborne LiDAR 3D Point Clouds Representing Individual Trees. ISPRS Journal of Photogrammetry and Remote Sensing 158:219–230. DOI: 10.1016/j.isprsjprs.2019.10.011.
    paper doi bibtex Remote Sensing Ecology GeoAI