GeoDeep Masterclass: Object Detection & Semantic Segmentation in Satellite Imagery

Unlocking Geospatial Insights with AI-Powered Analysis GeoDeep Interface Example Technical Specifications & Environment Setup Hardware Recommendations Processor: AMD Ryzen 9 9950X (16-core/32-thread) Memory: 96GB DDR5 @4800MT/s Storage: Crucial T700 4TB NVMe (12.4GB/s read) OS: Ubuntu 24 LTS via WSL2 on Windows 11 Pro Essential Software Stack # Python Environment sudo add-apt-repository ppa:deadsnakes/ppa sudo apt update sudo apt install jq python3-pip python3.12-venv # GeoDeep Installation python3 -m venv ~/.geodeep source ~/.geodeep/bin/activate python3 -m pip install geodeep # Spatial Database Setup wget https://github.com/duckdb/duckdb/releases/download/v1.1.3/duckdb_cli-linux-amd64.zip unzip -j duckdb_cli-linux-amd64.zip chmod +x duckdb Visualization Tools QGIS 3.42 with Tile+ Plugin DuckDB Spatial Extensions INSTALL h3 FROM community; LOAD spatial; Pre-Trained Model Performance Analysis Vehicle Detection (YOLOv7) geodeep visual.tif cars –output cars.geojson Results: 304 vehicles detected with confidence distribution: Confidence Range Detections 30-39% 86 40-49% 97 ≥80% 12 Vehicle Detection Heatmap Building Segmentation (UNet …