Topo4Vec automates detection of topological errors in geospatial vector data via error simulation and spatial representation learning, reporting peak accuracies of 0.99 for overlapping polygons and 0.60 for street network errors across three cities.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Reinforcement learning paired with a geometry-aware Polygons Transformer achieves area utilization competitive with the Sparrow heuristic solver for 2D irregular nesting.
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
citing papers explorer
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Geometry-Aware Reinforcement Learning for 2D Irregular Nesting
Reinforcement learning paired with a geometry-aware Polygons Transformer achieves area utilization competitive with the Sparrow heuristic solver for 2D irregular nesting.
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Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.