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20182022
most citedSat2Graph: Road Graph Extraction through Graph-Tensor Encoding

7 citations · 14 across the 5 of their papers we have counts for

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cs.CV20215 cited

Updating Street Maps using Changes Detected in Satellite Imagery

Favyen Bastani, Songtao He, Satvat Jagwani +5

Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS traject…

cs.CV20207 cited

Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding

Songtao He, Favyen Bastani, Satvat Jagwani +6

Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which pr…

cs.CV20192 cited

RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

Songtao He, Favyen Bastani, Satvat Jagwani +7

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of ro…

cs.CV2019

Inferring and Improving Street Maps with Data-Driven Automation

Favyen Bastani, Songtao He, Satvat Jagwani +7

Street maps are a crucial data source that help to inform a wide range of decisions, from navigating a city to disaster relief and urban planning. However, in many parts of the wor…

cs.CV2019

Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors

Mohammad Mahdi Derakhshani, Saeed Masoudnia, Amir Hossein Shaker +4

We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefull…

cs.CV2019

Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs

Saeed Masoudnia, Omid Mersa, Babak N. Araabi +3

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in presence of skilled forgeries that are not available during training. This study aims…