Machine Learning for Earth Observation

Enhancing humanitarian response and development planning by automating the detection and classification of Earth features using AI-driven models, to improve geospatial data accuracy and efficiency across diverse regions. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

The ultimate objective of this project is to revolutionize the process of geospatial data acquisition and analysis through the development and deployment of advanced AI-driven models for detecting and classifying Earth features such as buildings, rooftops, and solar panels. This initiative is designed to address the challenges of inefficiency, limited scalability, and the underutilization of current geospatial analysis methods that hamper effective humanitarian response, climate action, and development planning. The project will unfold over several key phases, each meticulously planned to ensure the successful development and deployment of this transformative technology:

• Data Collection and Initial Model Development:
  • Identifying Sources and Data Collection: Initiate by identifying the most suitable sources of aerial imagery, collecting data from both open source platforms and other providers willing to support non-commercial use. Focus will be on areas that align with the defined project scope, such as regions with dense building concentrations.
  • Data Preprocessing and Feature Definition: Conduct data preprocessing activities including annotation, normalization, and exploratory analysis. Define key features of interest such as buildings, solar panels, and rooftop materials and establish baseline workflows for model training.
• Model Development and Initial Testing:
  • Baseline Model Implementation: Implement baseline AI models for feature detection and classification, such as building detection and solar panel mapping. Begin testing these models on selected sample datasets to generate initial performance metrics, including Intersection over Union (IoU) and accuracy.
  • Validation and Post-Processing: Compare model outputs to benchmark models to validate their effectiveness. Construct the necessary post-processing pipelines to refine the outputs for practical application.
• Model Refinement and Validation:
  • Refine Models: Based on initial results and feedback, refine the models through hyperparameter tuning and further finetuning. Adjust and optimize the models to improve their performance and applicability across varied geographies and data sets.
  • Robustness Validation: Validate the refined models across different regions and datasets to ensure their robustness and reliability in diverse environmental conditions.
• Reporting and Strategic Planning: 
  • Interim Report Compilation: Compile an interim report that summarizes the findings, challenges encountered, and initial performance metrics. The report will also identify promising approaches for scalability and highlight gaps that require further exploration.
• Consolidation and Finalization: 
  • Finalize Models and Documentation: Select the best-performing models and finalize the training, inference, and post-processing pipelines to ensure their reproducibility and usability. Prepare comprehensive documentation of the methodologies and deliverables.
  • Final Report Preparation: Prepare the final project report detailing key insights, methodologies, and recommendations for the next steps. This report will serve as a blueprint for scaling the solution and applying it to broader applications.

This project aims to deliver an innovative AI-driven solution that significantly improves the accuracy, efficiency, and scalability of geospatial data analysis. By leveraging cutting-edge AI technologies, this initiative is expected to transform how geospatial data is used in planning and response activities, thereby enhancing the effectiveness of interventions in humanitarian, climate, and developmental contexts. This strategic approach promises substantial benefits in terms of reducing labor costs, speeding up data processing, and providing stakeholders with the tools they need for informed decision-making, ultimately contributing to more effective planning and intervention strategies globally.

The goals

The primary objective of this project is to streamline the process of geospatial data analysis through the development of AI-based tools. These tools are designed to automate the detection and classification of Earth features from satellite imagery, enhancing the accuracy and efficiency of data utilized for humanitarian efforts, climate action, and urban planning. By leveraging advanced machine learning technologies, this project aims to overcome the limitations of current geospatial data analysis methods, promoting better decision-making in critical areas.

Project Goals:

1. Data Preprocessing and Feature Definition:

• The initial phase will focus on preparing the data for analysis, including annotation, normalization, and exploratory analysis.
• Features of interest, such as buildings, solar panels, and rooftop materials, will be clearly defined to establish baseline workflows for subsequent modeling efforts.

2. Implementation and Initial Testing of Baseline Models:

• Baseline models for feature detection and classification will be implemented to identify and map targeted features accurately.
• Initial testing on sample datasets will be conducted to generate performance metrics (e.g., Intersection over Union, accuracy), with a comparison to benchmark models for validation.

3. Model Refinement and Validation:

• Based on testing results and feedback, models will undergo refinement processes including hyperparameter tuning and fine-tuning.
• Validation efforts will be expanded across different regions and datasets to ensure robustness and reliability of the models.

4. Interim Reporting and Identification of Scalable Approaches:

• An interim report will be compiled to summarize findings, outline challenges faced, and provide recommendations for further exploration.
• The report will also identify the most promising approaches for scalability and highlight areas requiring additional focus.

5. Consolidation and Final Reporting:

• The final phase will involve selecting the best-performing models and finalizing the training and inference pipelines to ensure reproducibility and usability.
• A comprehensive final report will be prepared, documenting key insights, detailed methodologies, and deliverables, along with recommendations for future steps.

By accomplishing these goals, this project seeks to deliver advanced, reliable AI tools that significantly enhance the quality and accessibility of geospatial data analysis. This initiative not only promotes the development of sophisticated AI capabilities but also supports a broad spectrum of applications that can benefit from enhanced data precision, from urban development to environmental conservation.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will build AI solutions to make a real-world impact and go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works