GeoNode AI addresses the challenge of improving public sector network scalability and efficiency in rural and remote areas. By leveraging KMeans clustering, it proposes optimal telecom node locations based on population coordinates and elevation data. The platform integrates real-time Sentinel-2 satellite imagery from the Copernicus API to provide terrain context, enhancing planning accuracy. Its interactive web interface, built with Flask and Folium, features a map with draggable nodes, a live coverage calculator, and export options (CSV, GeoJSON). Designed for accessibility, it includes a user-friendly sidebar for settings and statistics, making it a valuable tool for planners and engineers. Licensed under MIT, GeoNode AI is extensible, with potential for custom data integration and advanced terrain analysis.

Here is a **long description** for your project idea: **Solar Panel Monitoring System with Drone Simulation, CNN-Based Defect Detection, and YOLOv9 Dataset Integration via Web Interface**. --- ## 🌞 Long Description: Solar Panel Monitoring with AI and Drones This project presents a simulated **intelligent solar panel monitoring system** that integrates **drone-based inspection**, **deep learning-based defect detection**, and a **web-based visualization interface**. The goal is to demonstrate how modern AI techniques can automate and enhance the monitoring of solar panel infrastructure to ensure optimal performance and early fault detection. --- ### 🚁 1. Drone Simulation and Image Capture The system includes a simulated **DroneAPI** which emulates: - **Takeoff and landing operations** - **Autonomous navigation** to predefined solar panel locations - **Capture of RGB images** representing visual data - **Capture of thermal images** mimicking heat distribution across panels This simulation framework can easily be adapted to real drone APIs such as DJI or Parrot for real-world deployment. --- ### 🧠 2. Defect Detection using Deep Learning (CNN) To analyze the condition of the solar panels: - A **Convolutional Neural Network (CNN)** is used to classify RGB images into **three categories**: - `Normal` - `Crack` - `Dirt` The CNN is designed to be lightweight and fast, ideal for real-time edge deployment or drone-based processing. Thermal images are further analyzed for **hot spots**, which may indicate malfunctioning cells or overheating, using threshold-based anomaly detection. --- ### 📊 3. Automated Report Generation After inspecting all panels: - The system generates a visual report using `matplotlib` which includes: - A histogram showing the distribution of detected defects - A bar chart indicating the number of hot spots per panel - The report and all captured images are saved in the `static/` folder and can be accessed from the web interfac