> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # Remote Sensing > AI-driven sidewalk detection pipeline for statewide infrastructure analysis. ## Overview This section documents our **statewide, AI-driven sidewalk detection pipeline** that transforms high-resolution aerial imagery into accurate, vectorized sidewalk networks. The system spans the full machine learning lifecycle, from dataset curation and model training, to scalable inference and statewide deployment. Project overview and system architecture GIS data formats and the open lakehouse stack ## Models Our primary semantic segmentation model Alternative Detectron2 model architecture Segment Anything Model exploration ## Training Distributed training with Ray and ClearML Data cleaning, augmentation, and streaming Training results and model performance ## Inference Ray + Triton distributed inference system Production deployment and performance analysis ## Key Achievements * **9,202 tiles** processed in a single statewide run * **4.8 million chips** with \~319 billion pixels analyzed * **\~17 hours** for complete state coverage on single GPU * **99.66% success rate** with robust fault handling * **96% pixel accuracy** and **0.77 mean IoU** on validation ## Technology Stack * **Model:** DeepLabV3+ with ResNet-103 backbone * **Training:** Detectron2 with Hugging Face streaming * **Inference:** Ray actors + NVIDIA Triton Inference Server * **Output:** GeoJSON centerlines for transportation planning *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/products/applications/remote-sensing/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).