Segment Anything Model for Road Network Graph Extraction
arxiv(2024)
摘要
We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for
extracting large-scale, vectorized road network graphs from satellite imagery.
To predict graph geometry, we formulate it as a dense semantic segmentation
task, leveraging the inherent strengths of SAM. The image encoder of SAM is
fine-tuned to produce probability masks for roads and intersections, from which
the graph vertices are extracted via simple non-maximum suppression. To predict
graph topology, we designed a lightweight transformer-based graph neural
network, which leverages the SAM image embeddings to estimate the edge
existence probabilities between vertices. Our approach directly predicts the
graph vertices and edges for large regions without expensive and complex
post-processing heuristics, and is capable of building complete road network
graphs spanning multiple square kilometers in a matter of seconds. With its
simple, straightforward, and minimalist design, SAM-Road achieves comparable
accuracy with the state-of-the-art method RNGDet++, while being 40 times faster
on the City-scale dataset. We thus demonstrate the power of a foundational
vision model when applied to a graph learning task. The code is available at
https://github.com/htcr/sam_road.
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