Advanced Model Optimization and Deployment for Geospatial AI Applications

This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at https://omdena.com/projects. 

The problem

The rapid expansion of geospatial AI has unlocked new possibilities in humanitarian response, particularly in areas like disaster damage assessment, refugee camp planning, and urban slum mapping. However, despite the promise of models like YOLO and SAM, current approaches often fall short when applied to real-world, high-stakes environments. Challenges include inconsistent integration of models, lack of standard evaluation metrics, and limited testing across diverse contexts. These technical gaps hinder the deployment of reliable solutions, especially for organizations operating in dynamic and resource-constrained settings.

Impact of the Problem:

• Inconsistent Results: Without standardized evaluation methods and optimized architectures, model performance can vary significantly between geographies and use cases, limiting scalability and trust.
• Inefficient Humanitarian Planning: Inaccurate or incomplete building segmentation hampers critical operations such as emergency response and infrastructure planning in vulnerable regions.
• Limited Adoption of AI Tools: Complex or unreliable AI systems are less likely to be adopted by NGOs and governments, stalling progress in tech-enabled humanitarian work.
• Resource Wastage: Conventional lithium extraction processes consume vast amounts of water and energy, and in many cases require caustic chemical treatments for separation – leading to sustainability concerns and regulatory scrutiny.

This project seeks to address these limitations by developing a robust, modular geospatial AI pipeline that integrates state-of-the-art detection models. By refining model performance, standardizing evaluation metrics, and applying the pipeline to relevant humanitarian scenarios, the initiative aims to create tools that are both technically sound and grounded in real-world needs.

The project goals

The primary goal of this project is to develop a modular and optimized geospatial AI pipeline that integrates YOLO and SAM models for building segmentation and related humanitarian detection tasks. This initiative will focus on enhancing model integration, evaluating performance tradeoffs, and standardizing metrics to ensure accuracy and consistency across global use cases. The project will unfold over the following planned phases:

• Core Model Evaluation and Optimization: In the initial phase, the team will develop and document a modular script that integrates YOLO and SAM models. Using annotated datasets and existing open geospatial resources, the project will evaluate multiple model configurations.
• Model Comparison and Benchmarking: This phase will focus on identifying the most effective model configurations by comparing their performance across different data types and conditions.
• Use Case Application and Testing: The final phase will test the best-performing models in real-world humanitarian scenarios, such as disaster damage assessment, refugee camp planning, and urban slum detection. This will validate the pipeline’s applicability in diverse contexts and help refine the models based on practical outcomes.

Through this approach, the project aims to deliver a high-performing, explainable, and adaptable geospatial detection system that supports NGOs, governments, and humanitarian agencies in critical planning and emergency response efforts.

**More details will be shared with the designated team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works