Projects in brief



Project Name: AeroVision
Project Type: B2B, web application and drone solution (DRONE/APP)
Brief Description: An aerial monitoring information system for the digitalization of the Far Eastern Federal District (FEFD). The system is designed for processing and analyzing aerial photographs, allowing the detection and counting of salmon and sea lions, as well as monitoring natural resources and predicting emergencies.

Key Project Features

Goals and Objectives:
  • Formulating flight missions
  • Coordinating flights and group movements
  • Creating reports on detected objects
  • Processing thousands of photos per flight
  • Monitoring natural and man-made hazards
Key Results (KPI):
  • Increase in animal count accuracy by 35%
  • Reduction in data processing time by 50%
  • Improvement in natural resource monitoring efficiency by 40%
  • Reduction in monitoring and reporting costs by 30%

My Role and Team

My Role: Senior Product Manager
Main Responsibilities:
  • Developing long-term product strategy and unique selling propositions
  • Implementing and testing algorithms for aerial photo analysis
  • Conducting user research and gathering feedback
  • Developing partnership strategies with government bodies and research institutions
  • Improving the user interface and functionality of the platform
Team Composition:
  • 1 PM
  • 3 Developers
  • 2 Analysts
  • 1 Tester

Timeline and Tools

Timeline: 2020 – 2024
Project Management Tools:
  • Using Agile and Scrum methodologies
  • Jira for task tracking
  • Confluence for documentation
  • Slack for communication
  • Figma for creating CJM and analyzing customer interactions

Achievements and Results

Successful Moments:
  • Successful integration of the system with government structures
  • Positive feedback from VNIRO and other users
  • Reduction in report creation time by 45%
  • Attraction of 15 million rubles in funding

Issues and Solutions

Problems in Aerial Photo Analysis

Problem: High accuracy requirements for analyzing and processing large volumes of data.
  • Developing and implementing algorithms for processing and analyzing aerial photographs with high accuracy.
  • Conducting numerous tests and improvements to enhance algorithm accuracy and efficiency.
  • Using machine learning methods for automatic classification and counting of objects in images.
Problem: Limited computing power for processing large volumes of data.
  • Implementing cloud computing for processing and storing large volumes of data.
  • Optimizing algorithms to improve performance and reduce processing time.
  • Using distributed computing for parallel data processing.

Development Problems

Problem: Integrating multiple functions into a single platform with an intuitive interface.
  • Conducting detailed user research and creating CJM in Figma to analyze customer interactions.
  • Working closely with designers to develop a user-friendly and understandable interface.
  • Continuously collecting user feedback and iteratively improving the interface and functionality based on this information.
Problem: Supporting stable platform operation on various devices and browsers.
  • Using modern web development technologies to ensure cross-browser compatibility.
  • Thorough testing on various devices and browsers to identify and fix bugs.
  • Implementing automated testing for quick problem detection and reducing code errors.

Communication Issues with Government Bodies and Research Institutions

Problem: Strict reporting rules and standards.
  • Organizing demonstrations and presentations showing the system's compliance with requirements and standards.
  • Conducting pilot projects to obtain real feedback and recommendations.
  • Constant interaction with government bodies and research institutions, discussing their needs and integrating their suggestions into the product.
Problem: Challenges in establishing and maintaining long-term partnerships.
  • Developing a partnership strategy that includes favorable cooperation conditions and bonus programs for government bodies and research institutions.
  • Regular meetings and seminars to share experiences and discuss opportunities for platform improvement.
  • Continuous support and training for government and research institution staff on using the platform.