WebODM-Enabled Agricultural Mapping Initiative for Visayas State University

Visayas State University (VSU), through its Department of Geodetic Engineering, supports agricultural research, education, and extension initiatives across the Philippines. The university manages vegetable farms, demonstration sites, and research plots that contribute to its Food Innovation Technology Center and broader agricultural programs.

Through these activities, VSU generates critical data to support crop management, climate resilience research, and evidence-based guidance for farmers, researchers, and partner agencies.

Training on UAS and cloud computing for resilient communities at Visayas State University.

VSU relied on manual surveying and fragmented data collection methods to monitor agricultural assets and to train their students. While these approaches supported ongoing research activities, they created several operational and technical limitations:

• Delays in producing accurate, up-to-date maps

• Difficulty tracking changes in land use and crop conditions over time

• Fragmented datasets across teams, projects, and research activities

• Limited ability to generate high-resolution imagery and 3D models at scale

• Challenges supporting precision agriculture, research validation, and farmer-facing recommendations

  
  

These limitations reduced the efficiency of research programs and made it more difficult for the university to provide timely, data-driven insights to farmers, academic teams, and partner organizations.

The objective of our engagement was to implement a scalable, cost-effective, and open-source geospatial processing workflow to support agricultural monitoring and research.

The solution was designed to:

• Enable rapid processing of drone imagery into orthomosaics and 3D models

• Provide centralized access to high-resolution geospatial datasets

• Improve tracking of farm conditions and land-use changes over time

• Support precision agriculture, research validation, and extension programs

• Reduce reliance on manual surveying and disconnected data workflows

• Create a foundation for future geospatial research and innovation

Help.NGO working with the state university deployed a cloud-based geospatial processing pipeline using Amazon EC2 for compute and Amazon S3 for storage. This approach enabled VSU to process drone imagery more efficiently while centralizing access to raw imagery, processed outputs, and research datasets.

By combining scalable cloud infrastructure with open-source geospatial tools, the recommended solution improved data management, reduced operational complexity, and strengthened VSU’s ability to deliver timely, evidence-based insights.

Several potential approaches were evaluated:

• Continued reliance on manual surveying, which was slow, resource-intensive, and difficult to scale

• Fully on-premise processing systems, which introduced maintenance burdens and limited compute flexibility

• Proprietary GIS platforms, which created higher costs and reduced long-term adaptability

The selected AWS-based solution using Amazon EC2 and Amazon S3 was optimal because it:

• Provided scalable, cost-effective compute capacity for imagery processing workloads

• Supported open-source tools aligned with academic and research use cases

• Reduced infrastructure maintenance and operational overhead

• Enabled centralized, durable storage of geospatial datasets

• Preserved flexibility for future integration with GIS platforms and research workflows

Help.NGO designed a cloud-based geospatial processing workflow using AWS to support efficient drone data ingestion, processing, storage, and access.

AWS Services

Amazon EC2

• Hosts geospatial processing tools, such as WebODM or similar open-source platforms

• Provides scalable compute capacity for image processing, orthomosaic generation, and 3D model creation

• Allows processing resources to be scaled based on project size and research needs

Amazon S3

• Provides centralized storage for raw drone imagery, processed outputs, and research datasets

• Enables durable, organized, and shared access to geospatial data across research teams

• Supports long-term data retention and future analysis

Cloud-native WebODM pipeline on AWS enabling secure ingestion, processing, and delivery of drone imagery.

The solution significantly improved VSU’s agricultural research and operational capabilities. By moving from manual and fragmented workflows to a centralized cloud-based model, VSU was able to:

• Reduce the time required to produce high-resolution maps and 3D models

• Improve monitoring of agricultural areas and research plots

• Enable more consistent tracking of crop conditions and land-use changes

• Improve data accessibility and collaboration across teams

• Strengthen support for precision agriculture and applied research programs

• Enhance the university’s ability to provide evidence-based recommendations to farmers and partners

The implementation also provided VSU with a scalable foundation for expanding geospatial research, supporting climate-resilient agriculture, and advancing agricultural innovation across the region.

The project supported VSU's to transition from manual, fragmented data collection methods to a centralized and scalable geospatial processing workflow. Existing processes relied heavily on manual surveying and disconnected datasets, making it difficult to maintain consistency, monitor changes over time, and efficiently generate high-resolution outputs.

The solution also needed to align with academic and budget constraints. This required infrastructure that was cost-effective, flexible, and compatible with open-source tools, while avoiding unnecessary complexity for university teams.

These challenges created a clear opportunity to modernize VSU’s geospatial capabilities. By leveraging AWS for scalable processing and centralized storage, Help.NGO helped streamline research workflows and improve access to high-quality geospatial data. The use of open-source tools integrated with AWS services allowed VSU to maintain flexibility while strengthening its ability to support precision agriculture, research validation, and extension programs.

As a result, VSU is better positioned to expand its geospatial research capacity and deliver more timely, actionable insights to farmers, researchers, and partner agencies.

Additionally, the solution required secure handling of research and agricultural datasets, with an emphasis on controlled access, data integrity, reliable storage, and responsible data sharing across multiple users and academic stakeholders.

Help.NGO implemented controls designed to protect data, improve governance, and support reliable research workflows, including:

• Centralized data storage using Amazon S3 to support controlled and durable access to datasets

• Controlled access to processing environments on Amazon EC2, restricting administrative access to authorized personnel

• Encryption of data at rest and in transit to protect sensitive research data

• Structured data management workflows to improve consistency, governance, and traceability

• Secure handling of datasets across multiple users to preserve integrity and support collaborative research