Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.
Implementation of the paper "ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data" in TensorFlow.
This software is a modification of Visualizer from Spacenet competition for road extraction. I made it work for DeepGlobe dataset also.
TensorFlow implementation of D-LinkNet for road extraction.
Deep Learning based Road Segmentation using Satellite Imagery
End-to-end pipeline that extracts roads from occluded satellite imagery and stress-tests urban road networks: occlusion-robust segmentation → Union-Find/MST topological healing → betweenness-centrality criticality + node-ablation Resilience Index → interactive Streamlit dashboard. (ISRO BAH 2026 · PS-4)
Deep learning framework for post-disaster road accessibility assessment using SegFormer-B2, graph analysis, and OpenStreetMap (OSM).
Semantic segmentation (UNet + EfficientNet) that extracts roads from satellite imagery anywhere in the world — FastAPI + PyTorch backend, React/Leaflet map UI, GeoJSON export. Deployable as a single Docker image.
AI-powered Road Network Resilience Analysis using Satellite Imagery, Deep Learning, and Graph Analytics.
AI-powered road resilience system that detects and reconstructs occluded roads from satellite imagery using deep learning, enabling accurate mapping for disaster response, infrastructure planning, and smart mobility.
AI-powered system for automatic road extraction from satellite imagery and alert generation for newly detected roads.
AI-powered road resilience system that detects and reconstructs occluded roads from satellite imagery using deep learning, enabling accurate mapping for disaster response, infrastructure planning, and smart mobility.
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