Saad Farah

Developed and deployed a Real Estate Marketing Management Application, streamlining property listings, user authentication, contact management, and analytics for real estate agents and property owners. - Frontend: Built with Next.js, utilizing Chakra UI/Tailwind CSS, React Context API, and Mapbox for interactive property maps. - Backend: Developed with Spring Boot, implementing JWT authentication, PostgreSQL database, and Redis caching for performance. - Deployment: Dockerized the application and managed the codebase on GitHub, ensuring version control and seamless collaboration. - Features: User management, property listing, search and filtering, lead tracking, analytics, and Stripe/PayPal payment integration.

This project, titled "Advanced Deep Learning-Driven Urban Imaging: From Satellite Upscaling to 3D Visualization," focuses on optimizing photovoltaic (PV) installations in urban areas using a combination of deep learning, remote sensing, and GIS technologies. It involves four key components: enhancing Sentinel-2 satellite imagery with a super-resolution GAN model (GS-SRGAN), applying advanced models like Mask R-CNN, MaskFormer, and Mask2Former for precise rooftop segmentation, estimating building heights using the SENDEMHEIGHT model, and visualizing results with the UrbanVue3D tool, which aids in efficient PV placement and urban planning.

Recognizing an Exeptional Contribution in AI & Software Development

Developing an application for managing investment projects in the Laayoune Boujdour Sakia el Hamra region and visualizing them on a map using the Django web framework in Python

Development of an application for managing students, teachers, and staff, as well as IT asset management and network device configuration within the center

Development of a module on the Odoo ERP for managing the IT inventory within the municipality of Laayoune

Development of an application for managing complaints within the municipality of Laayoune