Amine-Fathallah0/LacunaAmine

Solar panel and boiler counting model for the Lacuna Solar Survey Challenge on Zindi. Built using InceptionV3, hybrid Albumentations + TorchVision augmentations, and trained with MAE loss. Final predictions made on satellite and drone imagery from Madagascar. # ☀️ Lacuna Solar Survey Challenge - Solar Panel & Boiler Detection ## 🏆 Project Overview This repository contains our solution for the Lacuna Solar Survey Challenge hosted on Zindi. The goal was to build a robust machine learning model capable of accurately detecting and counting **solar panels** and **solar boilers** from **satellite and drone imagery** of Madagascar. A scalable detection system like this can help: - Governments - NGOs - Energy providers ...to better plan, monitor, and optimize renewable energy deployment across Africa. --- ## 📜 Competition Description > Access to reliable and sustainable energy is a critical issue in Madagascar and across Africa, where a significant portion of the population lacks electricity or relies on costly, environmentally harmful energy sources. Solar energy presents a viable alternative, but mapping the distribution of solar panels and boilers is challenging over vast and remote areas. Participants were challenged to: - Detect and **count** the number of **solar panels** and **solar boilers** per image. - Build scalable, efficient, and accurate machine learning solutions. 🏆 Winning solutions were announced at the **Global AI Summit for Africa**, held in **Kigali, Rwanda** in **April 2025**. --- ## 📂 Project Structure ├── GODS.ipynb # Main training and inference notebook ├── README.md # Project overview (this file) ## 🛠️ Approach ### 📦 Data Preprocessing - Combined `title` and `content` columns into a unified `text` field. - Filled missing values with empty strings. - Mapped metadata fields (`placement`, `img_origin`) and created full image paths. - Applied **Stratified K-Fold** (7 folds) cross-validation based on combined panel and boiler counts. ### 🎨 Data Augmentation - **Albumentations** transformations: - Resizing, flips, brightness/contrast adjustment, elastic/grid distortions, normalization. - **Custom Torch transforms**: - Random sharpness and random blur added for better generalization. - **Combined …