Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

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Talent Scout

J

Jowaria Khan

University of Michigan, Ann Arbor

A

Anindya Sarkar

Washington University in St. Louis

Y

Yevgeniy Vorobeychik

Washington University in St. Louis

E

Elizabeth Bondi-Kelly

University of Michigan, Ann Arbor

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References (59)

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Prototype-Based Continual Learning with Label-Free Replay Buffer and Cluster Preservation Loss
2025Agil Aghasanli, Yi Li et al.
[2]
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
2025Luise Ge, Michael Lanier et al.
[3]
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
2025Michael Muehlebach, Zhiyu He et al.
[4]
Active Geospatial Search for Efficient Tenant Eviction Outreach
2024Anindya Sarkar, Alex DiChristofano et al.
[5]
Artificial Intelligence and Sustainable Development Goals: Systematic Literature Review of the Construction Industry
2024Massimo Regona, Tan Yigitcanlar et al.
[6]
Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion
2024Su Cao, Xuesheng Zhao et al.
[7]
Computer Vision Applications for Urban Planning: A Systematic Review of Opportunities and Constraints
2023Raveena Marasinghe, Tan Yigitcanlar et al.
[8]
Foundation Models for Generalist Geospatial Artificial Intelligence
2023Johannes Jakubik, Sujit Roy et al.
[9]
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
2023Anindya Sarkar, Nathan Jacobs et al.
[10]
Estimating daily air temperature and pollution in Catalonia: a comprehensive spatiotemporal modelling of multiple exposures.
2023C. Milà, J. Ballester et al.
[11]
Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model
2023Y. Liao, Zhaoli Wang et al.
[12]
Development of an AI advisor for conceptual land use planning
2023Chulwoong Park, Wonjun No et al.
[13]
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
2023Gengchen Mai, Weiming Huang et al.
[14]
A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment
2023M. S. Tehrany, M. Batur et al.
[15]
Segment Anything
2023A. Kirillov, Eric Mintun et al.
[16]
Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances
2023Hassan Gharoun, Fereshteh Momenifar et al.
[17]
Algorithmic Urban Planning for Smart and Sustainable Development: Systematic Review of the Literature
2023Tim Heinrich Son, Zack Weedon et al.
[18]
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2023Davide Cacciarelli, M. Kulahci
[19]
RingMo-Sense: Remote Sensing Foundation Model for Spatiotemporal Prediction via Spatiotemporal Evolution Disentangling
2023Fanglong Yao, Wanxuan Lu et al.
[20]
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling
2023Xian Sun, Peijin Wang et al.

Showing 20 of 59 references

Founder's Pitch

"Revolutionizing geospatial discovery with a low-cost, concept-guided online meta-learning framework for efficient target detection."

Geospatial AIScore: 6View PDF ↗

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0-10 scale

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5

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4/4 signals

10

Series A Potential

4/4 signals

10

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Why It Matters

In many critical fields like environmental monitoring and disaster response, data is costly and limited, making it crucial to have a system that can efficiently target and uncover high-value data points without exhaustive manual effort or extensive labeling.

Product Angle

This technology can be productized into a cloud-based geospatial analytics platform that assists governmental and private sector organizations in effectively monitoring environments through intelligent data sampling and discovery.

Disruption

This approach could replace many traditional and costly geospatial data collection methods that rely heavily on extensive human resources or static analysis models.

Product Opportunity

The market for environmental monitoring under constraints like limited budgets and sparse data is large due to the increasing need for sustainable practices across industries. Potential customers include environmental agencies, urban planners, and disaster response teams.

Use Case Idea

A commercial application could be a SaaS tool for environmental agencies for automated detection of pollution hotspots using minimal datasets.

Science

The paper proposes a framework that uses a blend of active learning, online meta-learning, and relevance-aware strategies to optimize sample selection under budget constraints. It incorporates conditional variational auto-encoders to capture latent concepts, such as land cover, to enhance prediction accuracy in dynamic, real-world geospatial scenarios.

Method & Eval

The methodology was validated using a real-world dataset related to PFAS contamination, highlighting its effectiveness in sparse data settings and its ability to outperform traditional methods by being more adaptive and resource-efficient.

Caveats

The system may face challenges in scenarios where the available domain-specific concept data is highly noisy or inaccurate, potentially leading to less effective predictions.

Author Intelligence

Jowaria Khan

LEAD
University of Michigan, Ann Arbor
jowaria@umich.edu

Anindya Sarkar

Washington University in St. Louis

Yevgeniy Vorobeychik

Washington University in St. Louis

Elizabeth Bondi-Kelly

University of Michigan, Ann Arbor

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