Land Use and Land Cover Classification Model
Developed a deep learning model for classifying Multi-Spectral Surface Reflectance Data from the LandSAT satellite for the purpose of developing Land Cover Maps for Fido State, Nigeria.
Iwinosa O is a Full Stack Engineer with several years of experience in developing scalable, high-performance applications across web and mobile platforms. Proficient in React Native, Next.js, and TypeScript, Iwinosa builds dynamic mobile and web applications. He has a strong backend foundation with Express.js, FastAPI, and Django, and is skilled in database management using PostgreSQL. His expertise includes CI/CD pipelines with Docker and GitHub Actions, and styling with Tailwind CSS. Iwinosa is also adept at implementing security features like role-based access control and integrating smart contracts using Solidity. At RushAM, Iwinosa developed a React Native mobile app with role-based access control and location services. For Tradify, he built a secure backend using FastAPI, enhancing transaction flows and API documentation. He also created a microservice for GDeliver that integrated with Google Maps to manage delivery pricing in real-time. Iwinosa holds a Bachelor of Engineering in Computer Engineering from the University of Benin. He is well-suited for roles that require developing robust web and mobile applications with a focus on performance and security.
Developed a deep learning model for classifying Multi-Spectral Surface Reflectance Data from the LandSAT satellite for the purpose of developing Land Cover Maps for Fido State, Nigeria.
Built a microservice that communicated with Chowdeck and Google Maps to provide real-time delivery pricing information and delivery management functionality for a Shopity store
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Full Stack Engineer
0-2 years experience
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